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Mark Podolskij

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Kim Christensen & Mark Podolskij, 2011. "Asymptotic theory of range-based multipower variation," CREATES Research Papers 2011-47, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Liu, Qiang & Liu, Yiqi & Liu, Zhi & Wang, Li, 2018. "Estimation of spot volatility with superposed noisy data," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 62-79.
    2. Ma, Feng & Liu, Jing & Huang, Dengshi & Chen, Wang, 2017. "Forecasting the oil futures price volatility: A new approach," Economic Modelling, Elsevier, vol. 64(C), pages 560-566.
    3. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    4. Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, vol. 30(C), pages 34-50.
    5. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    6. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers 2014-27, Department of Economics and Business Economics, Aarhus University.
    7. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    8. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    9. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    10. Duembgen, Moritz & Podolskij, Mark, 2015. "High-frequency asymptotics for path-dependent functionals of Itô semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1195-1217.
    11. Beatriz Vaz de Melo Mendes & Victor Bello Accioly, 2017. "Improving (E)GARCH forecasts with robust realized range measures: Evidence from international markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 631-658, October.
    12. Zhi Liu, 2017. "Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations," Finance and Stochastics, Springer, vol. 21(2), pages 427-469, April.
    13. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.

  2. Kim Christensen & Roel Oomen & Mark Podolskij, 2011. "Fact or friction: Jumps at ultra high frequency," CREATES Research Papers 2011-19, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Gilder, Dudley & Shackleton, Mark B. & Taylor, Stephen J., 2014. "Cojumps in stock prices: Empirical evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 443-459.
    2. Mohamed Arouri & Oussama M’saddek & Duc Khuong Nguyen & Kuntara Pukthuanthong, 2019. "Cojumps and asset allocation in international equity markets," Post-Print hal-02078138, HAL.
    3. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    4. Almut Veraart, 2011. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(3), pages 253-291, September.
    5. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2017. "Decoupling the short- and long-term behavior of stochastic volatility," CREATES Research Papers 2017-26, Department of Economics and Business Economics, Aarhus University.
    6. Li, Yingying & Zhang, Zhiyuan & Li, Yichu, 2018. "A unified approach to volatility estimation in the presence of both rounding and random market microstructure noise," Journal of Econometrics, Elsevier, vol. 203(2), pages 187-222.
    7. Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
    8. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    9. Olivier Scaillet & Adrien Treccani & Christopher Trevisan, 2017. "High-Frequency Jump Analysis of the Bitcoin Market," Papers 1704.08175, arXiv.org, revised Jun 2017.
    10. Giorgio Mirone, 2017. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    11. Giorgio Mirone, 2018. "Cross-sectional noise reduction and more efficient estimation of Integrated Variance," CREATES Research Papers 2018-18, Department of Economics and Business Economics, Aarhus University.
    12. Eduardo Rossi & Paolo Santucci de Magistris, 2018. "Indirect inference with time series observed with error," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 874-897, September.
    13. F. Lilla, 2017. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed," Working Papers wp1099, Dipartimento Scienze Economiche, Universita' di Bologna.
    14. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2016. "Decoupling the short- and long-term behavior of stochastic volatility," Papers 1610.00332, arXiv.org, revised Jul 2017.
    15. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," CFS Working Paper Series 477, Center for Financial Studies (CFS).
    16. Jiang, George J. & Zhu, Kevin X., 2017. "Information Shocks and Short-Term Market Underreaction," Journal of Financial Economics, Elsevier, vol. 124(1), pages 43-64.
    17. Audrino, Francesco & Hu, Yujia, 2011. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Economics Working Paper Series 1138, University of St. Gallen, School of Economics and Political Science.
    18. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    19. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    20. Sabet, Amir H. & Heaney, Richard, 2016. "An event study analysis of oil and gas firm acreage and reserve acquisitions," Energy Economics, Elsevier, vol. 57(C), pages 215-227.
    21. Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2014. "Multi-jumps," MPRA Paper 58175, University Library of Munich, Germany.
    22. Martin Tegnér & Rolf Poulsen, 2018. "Volatility Is Log-Normal—But Not for the Reason You Think," Risks, MDPI, Open Access Journal, vol. 6(2), pages 1-16, April.
    23. Bibinger, Markus & Neely, Christopher J. & Winkelmann, Lars, 2017. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Working Papers 2017-12, Federal Reserve Bank of St. Louis.
    24. Zhihong Jian & Zhican Zhu & Jie Zhou & Shuai Wu, 2018. "The Magnet Effect of Circuit Breakers: A role of price jumps and market liquidity," Departmental Working Papers 2018-01, The University of Winnipeg, Department of Economics.
    25. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
    26. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    27. Dungey, Mardi & Henry, Olan T & Hvodzdyk, Lyudmyla, 2013. "The impact of jumps and thin trading on realized hedge ratios," Working Papers 2013-02, University of Tasmania, Tasmanian School of Business and Economics, revised 28 Mar 2013.
    28. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    29. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
    30. Piccotti, Louis R., 2018. "Jumps, cojumps, and efficiency in the spot foreign exchange market," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 49-67.
    31. Becker, Janis & Leschinski, Christian, 2018. "The Bias of Realized Volatility," Hannover Economic Papers (HEP) dp-642, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    32. F. Lilla, 2016. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models," Working Papers wp1084, Dipartimento Scienze Economiche, Universita' di Bologna.
    33. Huang, Alex YiHou, 2016. "Impacts of implied volatility on stock price realized jumps," Economic Systems, Elsevier, vol. 40(4), pages 622-630.
    34. Mingmian Cheng & Norman R. Swanson, 2019. "Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence," Econometrics, MDPI, Open Access Journal, vol. 7(1), pages 1-32, March.
    35. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
    36. Rasmus Tangsgaard Varneskov, 2011. "Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise," CREATES Research Papers 2011-31, Department of Economics and Business Economics, Aarhus University.
    37. Antoine Jacquier & Mikko S. Pakkanen & Henry Stone, 2017. "Pathwise large deviations for the Rough Bergomi model," Papers 1706.05291, arXiv.org, revised Dec 2018.
    38. González-Urteaga, Ana & Muga, Luis & Santamaria, Rafael, 2015. "Momentum and default risk. Some results using the jump component," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 185-193.
    39. Christian Bayer & Chiheb Ben Hammouda & Raul Tempone, 2018. "Hierarchical adaptive sparse grids for option pricing under the rough Bergomi model," Papers 1812.08533, arXiv.org.
    40. Roland Füss & Ferdinand Mager & Michael Stein & Lu Zhao, 2018. "Financial crises, price discovery, and information transmission: a high-frequency perspective," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 333-365, November.
    41. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    42. Oomen, Roel, 2017. "Execution in an aggregator," LSE Research Online Documents on Economics 67454, London School of Economics and Political Science, LSE Library.
    43. Juho Kanniainen & Ye Yue, 2019. "The Arrival of News and Return Jumps in Stock Markets: A Nonparametric Approach," Papers 1901.02691, arXiv.org.
    44. Markus Bibinger & Markus Reiss & Nikolaus Hautsch & Peter Malec, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," SFB 649 Discussion Papers SFB649DP2014-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    45. Jondeau, Eric & Lahaye, Jérôme & Rockinger, Michael, 2015. "Estimating the price impact of trades in a high-frequency microstructure model with jumps," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 205-224.

  3. Mark Podolskij & Mathieu Rosenbaum, 2011. "Testing the local volatility assumption: a statistical approach," CREATES Research Papers 2011-04, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Jean Jacod & Mark Podolskij, 2012. "A Test for the Rank of the Volatility Process: The Random Perturbation Approach," Global COE Hi-Stat Discussion Paper Series gd12-268, Institute of Economic Research, Hitotsubashi University.
    2. Jean Jacod & Mark Podolskij, 2012. "A test for the rank of the volatility process: the random perturbation approach," CREATES Research Papers 2012-57, Department of Economics and Business Economics, Aarhus University.

  4. Kim Christensen & Mark Podolskij & Mathias Vetter, 2011. "On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes," CREATES Research Papers 2011-53, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Yoann Potiron & Per Mykland, 2015. "Estimation of integrated quadratic covariation with endogenous sampling times," Papers 1507.01033, arXiv.org, revised Nov 2016.
    2. Yuta Koike, 2013. "Limit Theorems for the Pre-averaged Hayashi-Yoshida Estimator with Random Sampling," Global COE Hi-Stat Discussion Paper Series gd12-276, Institute of Economic Research, Hitotsubashi University.
    3. Koike, Yuta, 2014. "Limit theorems for the pre-averaged Hayashi–Yoshida estimator with random sampling," Stochastic Processes and their Applications, Elsevier, vol. 124(8), pages 2699-2753.
    4. Altmeyer, Randolf & Bibinger, Markus, 2015. "Functional stable limit theorems for quasi-efficient spectral covolatility estimators," Stochastic Processes and their Applications, Elsevier, vol. 125(12), pages 4556-4600.
    5. Markus Bibinger & Lars Winkelmann, 2013. "Econometrics of co-jumps in high-frequency data with noise," SFB 649 Discussion Papers SFB649DP2013-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Markus Bibinger & Per A. Mykland, 2016. "Inference for Multi-dimensional High-frequency Data with an Application to Conditional Independence Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1078-1102, December.
    7. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    8. Ulrich Hounyo & Sílvia Gonçalves & Nour Meddahi, 2016. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CIRANO Working Papers 2016s-25, CIRANO.
    9. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    10. Yoann Potiron & Per Mykland, 2016. "Local Parametric Estimation in High Frequency Data," Papers 1603.05700, arXiv.org, revised Aug 2018.
    11. Habib Hasnaoui, 2014. "Alternative Beta Risk Estimators in Emerging Markets: The Case of Tunisia," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 2(2), pages 96-105.
    12. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," CFS Working Paper Series 477, Center for Financial Studies (CFS).
    13. Veredas, David & Vander Elst, Harry, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Mykland, Per A. & Zhang, Lan & Chen, Dachuan, 2019. "The algebra of two scales estimation, and the S-TSRV: High frequency estimation that is robust to sampling times," Journal of Econometrics, Elsevier, vol. 208(1), pages 101-119.
    15. Naoto Kunitomo & Hiroumi Misaki & Seisho Sato, 2015. "The SIML Estimation of Integrated Covariance and Hedging Coefficient under Round-off Errors, Micro-market Price Adjustments and Random Sampling," CIRJE F-Series CIRJE-F-965, CIRJE, Faculty of Economics, University of Tokyo.
    16. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    17. Ogihara, Teppei & Yoshida, Nakahiro, 2014. "Quasi-likelihood analysis for nonsynchronously observed diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 124(9), pages 2954-3008.
    18. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    19. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    20. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2013. "Estimating the Quadratic Covariation Matrix from Noisy Observations: Local Method of Moments and Efficiency," SFB 649 Discussion Papers SFB649DP2013-017, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    21. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    22. Naoto Kunitomo & Hiroumi Misaki & Seisho Sato, 2015. "The SIML Estimation of Integrated Covariance and Hedging Coefficient Under Round-off Errors, Micro-market Price Adjustments and Random Sampling," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(3), pages 333-368, September.
    23. Jacod, Jean & Li, Yingying & Zheng, Xinghua, 2019. "Estimating the integrated volatility with tick observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 80-100.
    24. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    25. Markus Bibinger & Per A. Mykland, 2013. "Inference for Multi-Dimensional High-Frequency Data: Equivalence of Methods, Central Limit Theorems, and an Application to Conditional Independence Testing," SFB 649 Discussion Papers SFB649DP2013-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    26. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    27. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
    28. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    29. Randolf Altmeyer & Markus Bibinger, 2014. "Functional stable limit theorems for efficient spectral covolatility estimators," SFB 649 Discussion Papers SFB649DP2014-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    30. Alberto Ohashi & Alexandre B Simas, 2015. "Principal Components Analysis for Semimartingales and Stochastic PDE," Papers 1503.05909, arXiv.org, revised Mar 2016.

  5. Nikolaus Hautsch & Mark Podolskij, 2010. "Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," CREATES Research Papers 2010-29, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Nicole Wiebach & Lutz Hildebrandt, 2010. "Context Effects as Customer Reaction on Delisting of Brands," SFB 649 Discussion Papers SFB649DP2010-056, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Dungey, Mardi & Erdemlioglu, Deniz & Matei, Marius & Yang, Xiye, 2018. "Testing for mutually exciting jumps and financial flights in high frequency data," Journal of Econometrics, Elsevier, vol. 202(1), pages 18-44.
    3. Christian Basteck & Tijmen R. Daniëls, 2010. "Every Symmetric 3 x 3 Global Game of Strategic Complementarities Is Noise Independent," SFB 649 Discussion Papers SFB649DP2010-061, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    5. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    6. Agnieszka Janek & Tino Kluge & Rafal Weron & Uwe Wystup, 2010. "FX Smile in the Heston Model," Papers 1010.1617, arXiv.org.
    7. Ulrich Hounyo & Sílvia Gonçalves & Nour Meddahi, 2016. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CIRANO Working Papers 2016s-25, CIRANO.
    8. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    9. Borak, Szymon & Misiorek, Adam & Weron, Rafal, 2010. "Models for Heavy-tailed Asset Returns," MPRA Paper 25494, University Library of Munich, Germany.
    10. Enno Mammen & Christoph Rothe & Melanie Schienle, 2010. "Nonparametric Regression with Nonparametrically Generated Covariates," SFB 649 Discussion Papers SFB649DP2010-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    12. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    13. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    14. Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2014. "Multi-jumps," MPRA Paper 58175, University Library of Munich, Germany.
    15. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    16. Jacod, Jean & Li, Yingying & Zheng, Xinghua, 2019. "Estimating the integrated volatility with tick observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 80-100.
    17. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    18. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    19. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2010. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," SFB 649 Discussion Papers SFB649DP2010-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    20. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    21. Franziska Schulze, 2010. "Spatial Dependencies in German Matching Functions," SFB 649 Discussion Papers SFB649DP2010-054, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    22. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    23. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    24. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    25. Ralf Sabiwalsky, 2010. "Executive Compensation Regulation and the Dynamics of the Pay-Performance Sensitivity," SFB 649 Discussion Papers SFB649DP2010-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    26. Mathias Pohl & Alexander Ristig & Walter Schachermayer & Ludovic Tangpi, 2018. "Theoretical and empirical analysis of trading activity," Papers 1803.04892, arXiv.org, revised Oct 2018.
    27. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    28. Markus Bibinger & Markus Reiss & Nikolaus Hautsch & Peter Malec, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," SFB 649 Discussion Papers SFB649DP2014-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    29. Vladimir Panov, 2010. "Estimation of the signal subspace without estimation of the inverse covariance matrix," SFB 649 Discussion Papers SFB649DP2010-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    30. Maria Grith & Volker Krätschmer, 2010. "Parametric estimation of risk neutral density functions," SFB 649 Discussion Papers SFB649DP2010-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  6. Ivan Nourdin & Giovanni Peccati & Mark Podolskij, 2010. "Quantitative Breuer-Major Theorems," CREATES Research Papers 2010-22, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Pham, Viet-Hung, 2013. "On the rate of convergence for central limit theorems of sojourn times of Gaussian fields," Stochastic Processes and their Applications, Elsevier, vol. 123(6), pages 2158-2174.
    2. Marie Kratz & Sreekar Vadlamani, 2016. "CLT for Lipschitz-Killing curvatures of excursion sets of Gaussian random fields," Working Papers hal-01373091, HAL.
    3. Ivan Nourdin & David Nualart & Rola Zintout, 2016. "Multivariate central limit theorems for averages of fractional Volterra processes and applications to parameter estimation," Statistical Inference for Stochastic Processes, Springer, vol. 19(2), pages 219-234, July.
    4. Bardet, Jean-Marc & Surgailis, Donatas, 2013. "Moment bounds and central limit theorems for Gaussian subordinated arrays," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 457-473.
    5. Nicolaescu, Liviu I., 2017. "A CLT concerning critical points of random functions on a Euclidean space," Stochastic Processes and their Applications, Elsevier, vol. 127(10), pages 3412-3446.

  7. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Limit theorems for functionals of higher order differences of Brownian semi-stationary processes," CREATES Research Papers 2009-60, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mark Podolskij & Katrin Wasmuth, 2012. "Goodness-of-fit testing for fractional diffusions," CREATES Research Papers 2012-12, Department of Economics and Business Economics, Aarhus University.

  8. Mark Podolskij & Mathias Vetter, 2009. "Understanding limit theorems for semimartingales: a short survey," CREATES Research Papers 2009-47, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mark Podolskij & Christian Schmidt & Mathias Vetter, 2015. "On U- and V-statistics for discontinuous Itô semimartingale," CREATES Research Papers 2015-52, Department of Economics and Business Economics, Aarhus University.
    2. Markus Bibinger & Mathias Vetter, 2015. "Estimating the quadratic covariation of an asynchronously observed semimartingale with jumps," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 707-743, August.
    3. Mark Podolskij & Katrin Wasmuth, 2013. "Goodness-of-fit testing for fractional diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 16(2), pages 147-159, July.
    4. Altmeyer, Randolf & Bibinger, Markus, 2015. "Functional stable limit theorems for quasi-efficient spectral covolatility estimators," Stochastic Processes and their Applications, Elsevier, vol. 125(12), pages 4556-4600.
    5. Rasmus Tangsgaard Varneskov, 2011. "Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices," CREATES Research Papers 2011-35, Department of Economics and Business Economics, Aarhus University.
    6. Andreas Basse-O'Connor & Mark Podolskij, 2015. "On critical cases in limit theory for stationary increments Lévy driven moving averages," CREATES Research Papers 2015-57, Department of Economics and Business Economics, Aarhus University.
    7. Kolokolov, Aleksey & Livieri, Giulia & Pirino, Davide, 2018. "Statistical inferences for price staleness," SAFE Working Paper Series 236, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    8. Zu, Yang & Peter Boswijk, H., 2014. "Estimating spot volatility with high-frequency financial data," Journal of Econometrics, Elsevier, vol. 181(2), pages 117-135.
    9. Claudio Heinrich & Mark Podolskij, 2014. "On spectral distribution of high dimensional covariation matrices," CREATES Research Papers 2014-54, Department of Economics and Business Economics, Aarhus University.
    10. Neil Shephard & Kevin Sheppard, 2012. "Efficient and feasible inference for the components of financial variation using blocked multipower variation," Economics Series Working Papers 593, University of Oxford, Department of Economics.
    11. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    12. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    13. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    14. Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010. "Bootstrapping realized multivariate volatility measures," MPRA Paper 40123, University Library of Munich, Germany.
    15. Markus Bibinger & Mathias Vetter, 2013. "Estimating the quadratic covariation of an asynchronously observed semimartingale with jumps," SFB 649 Discussion Papers SFB649DP2013-029, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Imma Valentina Curato, 2012. "Asymptotics for the Fourier estimators of the volatility of volatility and the leverage," Working Papers - Mathematical Economics 2012-11, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    17. Andreas Basse-O'Connor & Raphaël Lachièze-Rey & Mark Podolskij, 2015. "Limit theorems for stationary increments Lévy driven moving averages," CREATES Research Papers 2015-56, Department of Economics and Business Economics, Aarhus University.
    18. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    19. José Manuel Corcuera & Emil Hedevang & Mikko S. Pakkanen & Mark Podolskij, 2012. "Asymptotic theory for Brownian semi-stationary processes with application to turbulence," CREATES Research Papers 2012-52, Department of Economics and Business Economics, Aarhus University.
    20. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    21. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
    22. Randolf Altmeyer & Markus Bibinger, 2014. "Functional stable limit theorems for efficient spectral covolatility estimators," SFB 649 Discussion Papers SFB649DP2014-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    23. Corcuera, José Manuel & Hedevang, Emil & Pakkanen, Mikko S. & Podolskij, Mark, 2013. "Asymptotic theory for Brownian semi-stationary processes with application to turbulence," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2552-2574.

  9. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Multipower Variation for Brownian Semistationary Processes," CREATES Research Papers 2009-21, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ivan Nourdin & Giovanni Peccati & Mark Podolskij, 2010. "Quantitative Breuer-Major Theorems," CREATES Research Papers 2010-22, Department of Economics and Business Economics, Aarhus University.
    2. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Limit theorems for functionals of higher order differences of Brownian semi-stationary processes," CREATES Research Papers 2009-60, Department of Economics and Business Economics, Aarhus University.
    3. Mark Podolskij & Katrin Wasmuth, 2012. "Goodness-of-fit testing for fractional diffusions," CREATES Research Papers 2012-12, Department of Economics and Business Economics, Aarhus University.
    4. José Manuel Corcuera & Emil Hedevang & Mikko S. Pakkanen & Mark Podolskij, 2012. "Asymptotic theory for Brownian semi-stationary processes with application to turbulence," CREATES Research Papers 2012-52, Department of Economics and Business Economics, Aarhus University.

  10. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kunitomo, Naoto & Sato, Seisho, 2013. "Separating Information Maximum Likelihood estimation of the integrated volatility and covariance with micro-market noise," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 282-309.
    2. Yoann Potiron & Per Mykland, 2015. "Estimation of integrated quadratic covariation with endogenous sampling times," Papers 1507.01033, arXiv.org, revised Nov 2016.
    3. Yuta Koike, 2013. "Limit Theorems for the Pre-averaged Hayashi-Yoshida Estimator with Random Sampling," Global COE Hi-Stat Discussion Paper Series gd12-276, Institute of Economic Research, Hitotsubashi University.
    4. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    5. Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Series Working Papers 604, University of Oxford, Department of Economics.
    6. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    7. Koike, Yuta, 2014. "Limit theorems for the pre-averaged Hayashi–Yoshida estimator with random sampling," Stochastic Processes and their Applications, Elsevier, vol. 124(8), pages 2699-2753.
    8. Jianqing Fan & Yingying Li & Ke Yu, 2012. "Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 412-428, March.
    9. Rasmus Tangsgaard Varneskov, 2011. "Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices," CREATES Research Papers 2011-35, Department of Economics and Business Economics, Aarhus University.
    10. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    11. Peter R. Hansen & Asger Lunde & Valeri Voev, 2010. "Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility," CREATES Research Papers 2010-74, Department of Economics and Business Economics, Aarhus University.
    12. Li, Yingying & Zhang, Zhiyuan & Li, Yichu, 2018. "A unified approach to volatility estimation in the presence of both rounding and random market microstructure noise," Journal of Econometrics, Elsevier, vol. 203(2), pages 187-222.
    13. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    14. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Tinbergen Institute Discussion Papers 14-037/III, Tinbergen Institute.
    15. Markus Bibinger & Per A. Mykland, 2016. "Inference for Multi-dimensional High-frequency Data with an Application to Conditional Independence Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1078-1102, December.
    16. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    17. Markus Bibinger & Markus Reiß, 2011. "Spectral estimation of covolatility from noisy observations using local weights," SFB 649 Discussion Papers SFB649DP2011-086, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    18. Ulrich Hounyo & Sílvia Gonçalves & Nour Meddahi, 2016. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CIRANO Working Papers 2016s-25, CIRANO.
    19. Xinghua Zheng & Yingying Li, 2010. "On the estimation of integrated covariance matrices of high dimensional diffusion processes," Papers 1005.1862, arXiv.org, revised Mar 2012.
    20. Yoann Potiron & Per Mykland, 2016. "Local Parametric Estimation in High Frequency Data," Papers 1603.05700, arXiv.org, revised Aug 2018.
    21. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    22. Liu, Cheng & Tang, Cheng Yong, 2014. "A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data," Journal of Econometrics, Elsevier, vol. 180(2), pages 217-232.
    23. Liu, Qiang & Liu, Yiqi & Liu, Zhi, 2018. "Estimating spot volatility in the presence of infinite variation jumps," Stochastic Processes and their Applications, Elsevier, vol. 128(6), pages 1958-1987.
    24. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    25. Veredas, David & Vander Elst, Harry, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    26. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    27. Raddant, Matthias & Kenett, Dror, 2016. "Interconnectedness in the global financial market," Annual Conference 2016 (Augsburg): Demographic Change 145560, Verein für Socialpolitik / German Economic Association.
    28. Shen, Keren & Yao, Jianfeng & Li, Wai Keung, 2019. "On a spiked model for large volatility matrix estimation from noisy high-frequency data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 207-221.
    29. Yacine Aït-Sahalia & Dacheng Xiu, 2015. "Principal Component Analysis of High Frequency Data," NBER Working Papers 21584, National Bureau of Economic Research, Inc.
    30. Todorov, Viktor & Bollerslev, Tim, 2010. "Jumps and betas: A new framework for disentangling and estimating systematic risks," Journal of Econometrics, Elsevier, vol. 157(2), pages 220-235, August.
    31. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    32. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    33. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2013. "Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," CREATES Research Papers 2013-07, Department of Economics and Business Economics, Aarhus University.
    34. Park, Sujin & Hong, Seok Young & Linton, Oliver, 2016. "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error," Journal of Econometrics, Elsevier, vol. 191(2), pages 325-347.
    35. P Gorgi & P R Hansen & P Janus & S J Koopman, 2019. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 17(1), pages 1-32.
    36. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    37. Markus Bibinger & Markus Reiß, 2014. "Spectral Estimation of Covolatility from Noisy Observations Using Local Weights," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 23-50, March.
    38. Melvin, Michael & Prins, John & Shand, Duncan, 2013. "Forecasting Exchange Rates: an Investor Perspective," Handbook of Economic Forecasting, Elsevier.
    39. Ogihara, Teppei & Yoshida, Nakahiro, 2014. "Quasi-likelihood analysis for nonsynchronously observed diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 124(9), pages 2954-3008.
    40. Liu, Zhi & Kong, Xin-Bing & Jing, Bing-Yi, 2018. "Estimating the integrated volatility using high-frequency data with zero durations," Journal of Econometrics, Elsevier, vol. 204(1), pages 18-32.
    41. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    42. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    43. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    44. Kim, Donggyu & Kong, Xin-Bing & Li, Cui-Xia & Wang, Yazhen, 2018. "Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 203(1), pages 69-79.
    45. Zhi Liu, 2016. "Estimating integrated co-volatility with partially miss-ordered high frequency data," Statistical Inference for Stochastic Processes, Springer, vol. 19(2), pages 175-197, July.
    46. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    47. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    48. Jianqing Fan & Alex Furger & Dacheng Xiu, 2016. "Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 489-503, October.
    49. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    50. Markus Bibinger & Per A. Mykland, 2013. "Inference for Multi-Dimensional High-Frequency Data: Equivalence of Methods, Central Limit Theorems, and an Application to Conditional Independence Testing," SFB 649 Discussion Papers SFB649DP2013-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    51. Lam, Clifford & Feng, Phoenix, 2018. "A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data," Journal of Econometrics, Elsevier, vol. 206(1), pages 226-257.
    52. Rasmus Tangsgaard Varneskov, 2011. "Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise," CREATES Research Papers 2011-31, Department of Economics and Business Economics, Aarhus University.
    53. Sujin Park & Oliver Linton, 2012. "Estimating the Quadratic Covariation Matrix for an Asynchronously Observed Continuous Time Signal Masked by Additive Noise," FMG Discussion Papers dp703, Financial Markets Group.
    54. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    55. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    56. Zhi Liu, 2017. "Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations," Finance and Stochastics, Springer, vol. 21(2), pages 427-469, April.
    57. Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.
    58. Xin-Bing Kong, 2017. "On the number of common factors with high-frequency data," Biometrika, Biometrika Trust, vol. 104(2), pages 397-410.
    59. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    60. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
    61. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
    62. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    63. Philip L. H. Yu & W. K. Li & F. C. Ng, 2017. "The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 513-527, October.
    64. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    65. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
    66. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(2), pages 383-417.

  11. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Degiannakis, Stavros & Floros, Christos, 2014. "Intra-Day Realized Volatility for European and USA Stock Indices," MPRA Paper 64940, University Library of Munich, Germany, revised Jan 2015.
    2. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2013. "On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 59-84.
    3. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    4. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    6. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    7. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    8. Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
    9. Mancini, Cecilia, 2013. "Measuring the relevance of the microstructure noise in financial data," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2728-2751.
    10. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    11. Ulrich Hounyo & Sílvia Gonçalves & Nour Meddahi, 2016. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CIRANO Working Papers 2016s-25, CIRANO.
    12. Giorgio Mirone, 2017. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    13. Álvaro Cartea & Dimitrios Karyampas, 2009. "The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets," Birkbeck Working Papers in Economics and Finance 0914, Birkbeck, Department of Economics, Mathematics & Statistics.
    14. Cecilia Mancini, 2012. "Measuring the relevance of the microstructure noise in financial data," Working Papers - Mathematical Economics 2012-09, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    15. Veredas, David & Vander Elst, Harry, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    17. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    18. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    19. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Documents de travail du Centre d'Economie de la Sorbonne 17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    20. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
    21. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    22. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    23. Christophe Boucher & Gilles de Truchis & Elena Dumitrescu & Sessi Tokpavi, 2017. "Testing for Extreme Volatility Transmission with Realized Volatility Measures," EconomiX Working Papers 2017-20, University of Paris Nanterre, EconomiX.
    24. Neil Shephard & Kevin Sheppard, 2012. "Efficient and feasible inference for the components of financial variation using blocked multipower variation," Economics Series Working Papers 593, University of Oxford, Department of Economics.
    25. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    26. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    27. Robert Azencott & Peng Ren & Ilya Timofeyev, 2017. "Realized volatility and parametric estimation of Heston SDEs," Papers 1706.04566, arXiv.org.
    28. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    29. Li, Gang & Zhang, Chu, 2016. "On the relationship between conditional jump intensity and diffusive volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 196-213.
    30. Frowin Schulz & Karl Mosler, 2011. "The effect of infrequent trading on detecting price jumps," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 27-58, March.
    31. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    32. Hillebrand, Eric & Schnabl, Gunther & Ulu, Yasemin, 2009. "Japanese foreign exchange intervention and the yen-to-dollar exchange rate: A simultaneous equations approach using realized volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 490-505, July.
    33. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    34. Konstantinos Gkillas & Rangan Gupta & Chi Keung Marco Lau & Tahir Suleman, 2018. "Jumps Beyond the Realms of Cricket: India’s Performance in One Day Internationals and Stock Market Movements," Working Papers 201871, University of Pretoria, Department of Economics.
    35. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.
    36. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    37. Shin Kanaya & Taisuke Otsu, 2011. "Large Deviations of Realized Volatility," Cowles Foundation Discussion Papers 1798, Cowles Foundation for Research in Economics, Yale University.
    38. Yingjie Dong & Yiu-Kuen Tse, 2017. "Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility," Econometrics, MDPI, Open Access Journal, vol. 5(4), pages 1-19, November.
    39. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.

  12. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," OFRC Working Papers Series 2008fe25, Oxford Financial Research Centre.

    Cited by:

    1. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    2. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    3. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.

  13. Mark Podolskij & Daniel Ziggel, 2008. "New tests for jumps: a threshold-based approach," CREATES Research Papers 2008-34, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Marina Theodosiou & Filip Zikes, 2011. "A Comprehensive Comparison of Alternative Tests for Jumps in Asset Prices," Working Papers 2011-2, Central Bank of Cyprus.
    2. Marina Theodosiou, 2010. "Calendar Time Sampling of High Frequency Financial Asset Price and the Verdict on Jumps," Working Papers 2010-7, Central Bank of Cyprus.
    3. Torben B. Rasmussen, 2009. "Jump Testing and the Speed of Market Adjustment," CREATES Research Papers 2009-08, Department of Economics and Business Economics, Aarhus University.

  14. Mark Podolskij & Mathias Vetter, 2008. "Bipower-type estimation in a noisy diffusion setting," CREATES Research Papers 2008-25, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2013. "On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 59-84.
    2. Koike, Yuta, 2014. "Limit theorems for the pre-averaged Hayashi–Yoshida estimator with random sampling," Stochastic Processes and their Applications, Elsevier, vol. 124(8), pages 2699-2753.
    3. M. Podolskij & D. Ziggel, 2010. "New tests for jumps in semimartingale models," Statistical Inference for Stochastic Processes, Springer, vol. 13(1), pages 15-41, April.
    4. Cecilia Mancini & Vanessa Mattiussi & Roberto Reno', 2012. "Spot Volatility Estimation Using Delta Sequences," Working Papers - Mathematical Economics 2012-10, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    5. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    6. Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
    7. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    8. Nikolaus Hautsch & Mark Podolskij, 2010. "Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," CREATES Research Papers 2010-29, Department of Economics and Business Economics, Aarhus University.
    9. Mykland, Per A. & Zhang, Lan & Chen, Dachuan, 2019. "The algebra of two scales estimation, and the S-TSRV: High frequency estimation that is robust to sampling times," Journal of Econometrics, Elsevier, vol. 208(1), pages 101-119.
    10. Bing-Yi Jing & Zhi Liu & Xin-Bing Kong, 2014. "On the Estimation of Integrated Volatility With Jumps and Microstructure Noise," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 457-467, July.
    11. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    12. Zhihong Jian & Zhican Zhu & Jie Zhou & Shuai Wu, 2018. "The Magnet Effect of Circuit Breakers: A role of price jumps and market liquidity," Departmental Working Papers 2018-01, The University of Winnipeg, Department of Economics.
    13. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
    14. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    15. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    16. Ogawa, Shigeyoshi & Ngo, Hoang-Long, 2010. "Real-time estimation scheme for the spot cross volatility of jump diffusion processes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(9), pages 1962-1976.
    17. Ngo Hoang-Long & Ogawa Shigeyoshi, 2009. "A central limit theorem for the functional estimation of the spot volatility," Monte Carlo Methods and Applications, De Gruyter, vol. 15(4), pages 353-380, January.
    18. Corradi, Valentina & Silvapulle, Mervyn J. & Swanson, Norman R., 2018. "Testing for jumps and jump intensity path dependence," Journal of Econometrics, Elsevier, vol. 204(2), pages 248-267.
    19. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
    20. Michael Creel & Dennis Kristensen, 2014. "ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models," CREATES Research Papers 2014-30, Department of Economics and Business Economics, Aarhus University.
    21. Zhi Liu, 2017. "Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations," Finance and Stochastics, Springer, vol. 21(2), pages 427-469, April.
    22. Shimizu, Yasutaka, 2009. "Functional estimation for Lvy measures of semimartingales with Poissonian jumps," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1073-1092, July.
    23. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    24. Jean Jacod & Mark Podolskij & Mathias Vetter, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-61, Department of Economics and Business Economics, Aarhus University.

  15. Mark Podolskij & Daniel Ziggel, 2008. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2008-22, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Chen, Qiang & Zheng, Xu & Pan, Zhiyuan, 2015. "Asymptotically distribution-free tests for the volatility function of a diffusion," Journal of Econometrics, Elsevier, vol. 184(1), pages 124-144.
    2. Papanicolaou, Alex & Giesecke, Kay, 2016. "Variation-based tests for volatility misspecification," Journal of Econometrics, Elsevier, vol. 191(1), pages 217-230.
    3. Adam D. Bull, 2015. "Semimartingale detection and goodness-of-fit tests," Papers 1506.00088, arXiv.org, revised Jun 2016.

  16. Mark Podolskij & Mathias Vetter, 2007. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," CREATES Research Papers 2007-27, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    3. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    4. Podolskij, Mark & Vetter, Mathias, 2008. "Bipower-type estimation in a noisy diffusion setting," Technical Reports 2008,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Almut Veraart, 2011. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(3), pages 253-291, September.
    6. Shuichi Nagata, 2012. "Consistent Estimation of Integrated Volatility Using Intraday Absolute Returns for SV Jump Diffusion Processes," Economics Bulletin, AccessEcon, vol. 32(1), pages 306-314.
    7. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
    8. Maria Elvira Mancino & Simona Sanfelici, 2011. "Estimation of Quarticity with High Frequency Data," Working Papers - Mathematical Economics 2011-06, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, revised Jan 2012.
    9. Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print hal-00741630, HAL.
    10. Nikolaus Hautsch & Mark Podolskij, 2010. "Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," CREATES Research Papers 2010-29, Department of Economics and Business Economics, Aarhus University.
    11. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    12. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    13. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
    14. Jean Jacod & Yingying Li & Per A. Mykland & Mark Podolskij & Mathias Vetter, 2007. "Microstructure Noise in the Continuous Case: The Pre-Averaging Approach - JLMPV-9," CREATES Research Papers 2007-43, Department of Economics and Business Economics, Aarhus University.
    15. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2007. "Microstructure noise in the continuous case: the pre-averaging approach," Technical Reports 2007,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    16. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2011. "Modeling microstructure noise with mutually exciting point processes," Papers 1101.3422, arXiv.org.
    17. Markus Reiss, 2010. "Asymptotic equivalence and sufficiency for volatility estimation under microstructure noise," Papers 1001.3006, arXiv.org.
    18. Silja Kinnebrock & Mark Podolskij, 2007. "A Note on the Central Limit Theorem for Bipower Variation of General Functions," OFRC Working Papers Series 2007fe03, Oxford Financial Research Centre.
    19. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    20. Todorov, Viktor, 2009. "Estimation of continuous-time stochastic volatility models with jumps using high-frequency data," Journal of Econometrics, Elsevier, vol. 148(2), pages 131-148, February.
    21. Ole E. Barndorff-Nielsen & Almut E. D. Veraart, 2009. "Stochastic volatility of volatility in continuous time," CREATES Research Papers 2009-25, Department of Economics and Business Economics, Aarhus University.
    22. Jean Jacod & Mark Podolskij & Mathias Vetter, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-61, Department of Economics and Business Economics, Aarhus University.

  17. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2007. "Microstructure noise in the continuous case: the pre-averaging approach," Technical Reports 2007,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Chen, Richard Y. & Mykland, Per A., 2017. "Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data," Journal of Econometrics, Elsevier, vol. 200(1), pages 79-103.
    2. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2013. "On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 59-84.
    3. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    4. Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Series Working Papers 604, University of Oxford, Department of Economics.
    5. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    6. Koike, Yuta, 2014. "Limit theorems for the pre-averaged Hayashi–Yoshida estimator with random sampling," Stochastic Processes and their Applications, Elsevier, vol. 124(8), pages 2699-2753.
    7. M. Podolskij & D. Ziggel, 2010. "New tests for jumps in semimartingale models," Statistical Inference for Stochastic Processes, Springer, vol. 13(1), pages 15-41, April.
    8. Podolskij, Mark & Vetter, Mathias, 2008. "Bipower-type estimation in a noisy diffusion setting," Technical Reports 2008,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. Altmeyer, Randolf & Bibinger, Markus, 2015. "Functional stable limit theorems for quasi-efficient spectral covolatility estimators," Stochastic Processes and their Applications, Elsevier, vol. 125(12), pages 4556-4600.
    10. Andersen, Torben G. & Bondarenko, Oleg & Todorov, Viktor & Tauchen, George, 2015. "The fine structure of equity-index option dynamics," Journal of Econometrics, Elsevier, vol. 187(2), pages 532-546.
    11. Wang, Fangfang, 2014. "Optimal design of Fourier estimator in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 708-722.
    12. Cecilia Mancini & Vanessa Mattiussi & Roberto Reno', 2012. "Spot Volatility Estimation Using Delta Sequences," Working Papers - Mathematical Economics 2012-10, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    13. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
    14. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk — realised semivariance," CREATES Research Papers 2008-42, Department of Economics and Business Economics, Aarhus University.
    15. Maria Elvira Mancino & Simona Sanfelici, 2011. "Estimation of Quarticity with High Frequency Data," Working Papers - Mathematical Economics 2011-06, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, revised Jan 2012.
    16. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
    17. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2008. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," BIS Working Papers 249, Bank for International Settlements.
    18. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    19. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    20. Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print hal-00741630, HAL.
    21. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    22. Fangfang Wang, 2016. "An Unbiased Measure of Integrated Volatility in the Frequency Domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 147-164, March.
    23. Wang, Kent & Liu, Junwei & Liu, Zhi, 2013. "Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1777-1786.
    24. Markus Bibinger & Per A. Mykland, 2016. "Inference for Multi-dimensional High-frequency Data with an Application to Conditional Independence Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1078-1102, December.
    25. Mancini, Cecilia, 2013. "Measuring the relevance of the microstructure noise in financial data," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2728-2751.
    26. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    27. Chen, Bin & Song, Zhaogang, 2013. "Testing whether the underlying continuous-time process follows a diffusion: An infinitesimal operator-based approach," Journal of Econometrics, Elsevier, vol. 173(1), pages 83-107.
    28. Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Series Working Papers 438, University of Oxford, Department of Economics.
    29. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," CREATES Research Papers 2011-37, Department of Economics and Business Economics, Aarhus University.
    30. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    31. Nikolaus Hautsch & Mark Podolskij, 2010. "Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," CREATES Research Papers 2010-29, Department of Economics and Business Economics, Aarhus University.
    32. Habib Hasnaoui, 2014. "Alternative Beta Risk Estimators in Emerging Markets: The Case of Tunisia," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 2(2), pages 96-105.
    33. Liu, Cheng & Tang, Cheng Yong, 2014. "A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data," Journal of Econometrics, Elsevier, vol. 180(2), pages 217-232.
    34. Ubukata, Masato & Watanabe, Toshiaki, 2015. "Evaluating the performance of futures hedging using multivariate realized volatility," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 148-171.
    35. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    36. Wang, Chengyang & Nishiyama, Yoshihiko, 2015. "Volatility forecast of stock indices by model averaging using high-frequency data," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 324-337.
    37. Veredas, David & Vander Elst, Harry, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    38. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    39. Xin-Bing Kong, 2013. "A direct approach to risk approximation for vast portfolios under gross-exposure constraint using high-frequency data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 647-669, November.
    40. Shen, Keren & Yao, Jianfeng & Li, Wai Keung, 2019. "On a spiked model for large volatility matrix estimation from noisy high-frequency data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 207-221.
    41. Novotný, Jan & Petrov, Dmitri & Urga, Giovanni, 2015. "Trading price jump clusters in foreign exchange markets," Journal of Financial Markets, Elsevier, vol. 24(C), pages 66-92.
    42. Mykland, Per A. & Zhang, Lan & Chen, Dachuan, 2019. "The algebra of two scales estimation, and the S-TSRV: High frequency estimation that is robust to sampling times," Journal of Econometrics, Elsevier, vol. 208(1), pages 101-119.
    43. Lee, Suzanne S. & Mykland, Per A., 2012. "Jumps in equilibrium prices and market microstructure noise," Journal of Econometrics, Elsevier, vol. 168(2), pages 396-406.
    44. Clinet, Simon & Potiron, Yoann, 2018. "Efficient asymptotic variance reduction when estimating volatility in high frequency data," Journal of Econometrics, Elsevier, vol. 206(1), pages 103-142.
    45. Li, Shaoyu & Zheng, Tingguo, 2017. "Modeling spot rate using a realized stochastic volatility model with level effect and dynamic drift☆," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 200-221.
    46. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    47. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    48. Zu, Yang & Peter Boswijk, H., 2014. "Estimating spot volatility with high-frequency financial data," Journal of Econometrics, Elsevier, vol. 181(2), pages 117-135.
    49. Yacine Aït-Sahalia & Jianqing Fan & Roger J. A. Laeven & Christina Dan Wang & Xiye Yang, 2017. "Estimation of the Continuous and Discontinuous Leverage Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1744-1758, October.
    50. Park, Sujin & Hong, Seok Young & Linton, Oliver, 2016. "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error," Journal of Econometrics, Elsevier, vol. 191(2), pages 325-347.
    51. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    52. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
    53. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    54. Markus Bibinger & Markus Reiß, 2014. "Spectral Estimation of Covolatility from Noisy Observations Using Local Weights," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 23-50, March.
    55. Schmisser Emeline, 2011. "Non-parametric drift estimation for diffusions from noisy data," Statistics & Risk Modeling, De Gruyter, vol. 28(2), pages 119-150, May.
    56. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    57. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    58. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    59. Vitali Alexeev & Mardi Dungey & Wenying Yao, 2016. "Continuous and Jump Betas: Implications for Portfolio Diversification," Econometrics, MDPI, Open Access Journal, vol. 4(2), pages 1-15, June.
    60. Liu, Zhi & Kong, Xin-Bing & Jing, Bing-Yi, 2018. "Estimating the integrated volatility using high-frequency data with zero durations," Journal of Econometrics, Elsevier, vol. 204(1), pages 18-32.
    61. Neil Shephard & Kevin Sheppard, 2012. "Efficient and feasible inference for the components of financial variation using blocked multipower variation," Economics Series Working Papers 593, University of Oxford, Department of Economics.
    62. Shota Gugushvili & Frank van der Meulen & Moritz Schauer & Peter Spreij, 2018. "Nonparametric Bayesian volatility learning under microstructure noise," Papers 1805.05606, arXiv.org.
    63. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    64. Kim, Donggyu & Wang, Yazhen, 2016. "Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data," Journal of Econometrics, Elsevier, vol. 194(2), pages 220-230.
    65. Jacod, Jean & Li, Yingying & Zheng, Xinghua, 2019. "Estimating the integrated volatility with tick observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 80-100.
    66. Kim, Donggyu & Kong, Xin-Bing & Li, Cui-Xia & Wang, Yazhen, 2018. "Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 203(1), pages 69-79.
    67. Zhi Liu, 2016. "Estimating integrated co-volatility with partially miss-ordered high frequency data," Statistical Inference for Stochastic Processes, Springer, vol. 19(2), pages 175-197, July.
    68. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    69. Anne Brix & Asger Lunde, 2015. "Prediction-based estimating functions for stochastic volatility models with noisy data: comparison with a GMM alternative," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 433-465, October.
    70. Clément, Emmanuelle & Gloter, Arnaud, 2011. "Limit theorems in the Fourier transform method for the estimation of multivariate volatility," Stochastic Processes and their Applications, Elsevier, vol. 121(5), pages 1097-1124, May.
    71. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    72. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    73. Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2005. "Edgeworth Expansions for Realized Volatility and Related Estimators," NBER Technical Working Papers 0319, National Bureau of Economic Research, Inc.
    74. Markus Reiss, 2010. "Asymptotic equivalence and sufficiency for volatility estimation under microstructure noise," Papers 1001.3006, arXiv.org.
    75. Bandi, F.M. & Renò, R., 2016. "Price and volatility co-jumps," Journal of Financial Economics, Elsevier, vol. 119(1), pages 107-146.
    76. Figueroa-López, José E. & Nisen, Jeffrey, 2013. "Optimally thresholded realized power variations for Lévy jump diffusion models," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2648-2677.
    77. Aït-Sahalia, Yacine & Fan, Jianqing & Li, Yingying, 2013. "The leverage effect puzzle: Disentangling sources of bias at high frequency," Journal of Financial Economics, Elsevier, vol. 109(1), pages 224-249.
    78. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    79. Bonart, Julius & Lillo, Fabrizio, 2018. "A continuous and efficient fundamental price on the discrete order book grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 698-713.
    80. N.H. Bingham & John M. Fry & Rüdiger Kiesel, 2010. "Multivariate elliptic processes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(s1), pages 352-366.
    81. Jacod, Jean & Mykland, Per A., 2015. "Microstructure noise in the continuous case: Approximate efficiency of the adaptive pre-averaging method," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2910-2936.
    82. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    83. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
    84. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
    85. Benjamin Favetto, 2016. "Estimating functions for noisy observations of ergodic diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 19(1), pages 1-28, April.
    86. Donggyu Kim, 2016. "Statistical Inference for Unified Garch–Itô Models with High-Frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 513-532, July.
    87. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    88. Aït-Sahalia, Yacine & Jacod, Jean & Li, Jia, 2012. "Testing for jumps in noisy high frequency data," Journal of Econometrics, Elsevier, vol. 168(2), pages 207-222.
    89. Wooyong Lee & Priscilla E. Greenwood & Nancy Heckman & Wolfgang Wefelmeyer, 2017. "Pre-averaged kernel estimators for the drift function of a diffusion process in the presence of microstructure noise," Statistical Inference for Stochastic Processes, Springer, vol. 20(2), pages 237-252, July.
    90. Vetter, Mathias, 2014. "Inference on the Lévy measure in case of noisy observations," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 125-133.
    91. Reiß, Markus & Todorov, Viktor & Tauchen, George, 2015. "Nonparametric test for a constant beta between Itô semi-martingales based on high-frequency data," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2955-2988.
    92. Zhi Liu, 2017. "Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations," Finance and Stochastics, Springer, vol. 21(2), pages 427-469, April.
    93. Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.
    94. Rosenbaum, Mathieu & Tankov, Peter, 2011. "Asymptotic results for time-changed Lévy processes sampled at hitting times," Stochastic Processes and their Applications, Elsevier, vol. 121(7), pages 1607-1632, July.
    95. Xin-Bing Kong, 2017. "On the number of common factors with high-frequency data," Biometrika, Biometrika Trust, vol. 104(2), pages 397-410.
    96. Todorov, Viktor, 2009. "Estimation of continuous-time stochastic volatility models with jumps using high-frequency data," Journal of Econometrics, Elsevier, vol. 148(2), pages 131-148, February.
    97. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    98. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    99. Flavia Barsotti & Simona Sanfelici, 2016. "Market Microstructure Effects on Firm Default Risk Evaluation," Econometrics, MDPI, Open Access Journal, vol. 4(3), pages 1-31, July.
    100. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
    101. Kim, Seonjin & Zhao, Zhibiao, 2014. "Specification test for Markov models with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 118-133.
    102. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
    103. Yiqi Liu & Qiang Liu & Zhi Liu & Deng Ding, 2017. "Determining the integrated volatility via limit order books with multiple records," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1697-1714, November.
    104. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    105. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    106. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
    107. Li, Yingying & Xie, Shangyu & Zheng, Xinghua, 2016. "Efficient estimation of integrated volatility incorporating trading information," Journal of Econometrics, Elsevier, vol. 195(1), pages 33-50.
    108. Adam D. Bull, 2015. "Semimartingale detection and goodness-of-fit tests," Papers 1506.00088, arXiv.org, revised Jun 2016.
    109. Torben B. Rasmussen, 2009. "Jump Testing and the Speed of Market Adjustment," CREATES Research Papers 2009-08, Department of Economics and Business Economics, Aarhus University.
    110. Jean Jacod & Mark Podolskij & Mathias Vetter, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-61, Department of Economics and Business Economics, Aarhus University.
    111. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.

  18. Silja Kinnebrock & Mark Podolskij, 2007. "A Note on the Central Limit Theorem for Bipower Variation of General Functions," OFRC Working Papers Series 2007fe03, Oxford Financial Research Centre.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk — realised semivariance," CREATES Research Papers 2008-42, Department of Economics and Business Economics, Aarhus University.
    3. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Limit theorems for functionals of higher order differences of Brownian semi-stationary processes," CREATES Research Papers 2009-60, Department of Economics and Business Economics, Aarhus University.
    4. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    5. Mark Podolskij & Mathias Vetter, 2009. "Understanding limit theorems for semimartingales: a short survey," CREATES Research Papers 2009-47, Department of Economics and Business Economics, Aarhus University.
    6. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij & Jeannette H.C. Woerner, 2008. "Bipower variation for Gaussian processes with stationary increments," CREATES Research Papers 2008-21, Department of Economics and Business Economics, Aarhus University.
    7. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    8. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Multipower Variation for Brownian Semistationary Processes," CREATES Research Papers 2009-21, Department of Economics and Business Economics, Aarhus University.
    9. Ngo Hoang-Long & Ogawa Shigeyoshi, 2009. "A central limit theorem for the functional estimation of the spot volatility," Monte Carlo Methods and Applications, De Gruyter, vol. 15(4), pages 353-380, January.
    10. Mark Podolskij & Mathieu Rosenbaum, 2012. "Testing the local volatility assumption: a statistical approach," Annals of Finance, Springer, vol. 8(1), pages 31-48, February.
    11. Fukasawa, Masaaki & Rosenbaum, Mathieu, 2012. "Central limit theorems for realized volatility under hitting times of an irregular grid," Stochastic Processes and their Applications, Elsevier, vol. 122(12), pages 3901-3920.
    12. Duembgen, Moritz & Podolskij, Mark, 2015. "High-frequency asymptotics for path-dependent functionals of Itô semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1195-1217.
    13. Ole E. Barndorff-Nielsen & Almut E. D. Veraart, 2009. "Stochastic volatility of volatility in continuous time," CREATES Research Papers 2009-25, Department of Economics and Business Economics, Aarhus University.

  19. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2007. "Power variation for Gaussian processes with stationary increments," CREATES Research Papers 2007-42, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ivan Nourdin & Giovanni Peccati & Mark Podolskij, 2010. "Quantitative Breuer-Major Theorems," CREATES Research Papers 2010-22, Department of Economics and Business Economics, Aarhus University.
    2. José León & Carenne Ludeña, 2015. "Difference based estimators and infill statistics," Statistical Inference for Stochastic Processes, Springer, vol. 18(1), pages 1-31, April.
    3. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Limit theorems for functionals of higher order differences of Brownian semi-stationary processes," CREATES Research Papers 2009-60, Department of Economics and Business Economics, Aarhus University.
    4. Gärtner, Kerstin & Podolskij, Mark, 2015. "On non-standard limits of Brownian semi-stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 653-677.
    5. Andreas Basse-O'Connor & Mark Podolskij, 2015. "On critical cases in limit theory for stationary increments Lévy driven moving averages," CREATES Research Papers 2015-57, Department of Economics and Business Economics, Aarhus University.
    6. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij & Jeannette H.C. Woerner, 2008. "Bipower variation for Gaussian processes with stationary increments," CREATES Research Papers 2008-21, Department of Economics and Business Economics, Aarhus University.
    7. Mark Podolskij & Katrin Wasmuth, 2012. "Goodness-of-fit testing for fractional diffusions," CREATES Research Papers 2012-12, Department of Economics and Business Economics, Aarhus University.
    8. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Multipower Variation for Brownian Semistationary Processes," CREATES Research Papers 2009-21, Department of Economics and Business Economics, Aarhus University.
    9. Liu, Guangying & Zhang, Xinsheng, 2011. "Power variation of fractional integral processes with jumps," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 962-972, August.
    10. José Manuel Corcuera & Gergely Farkas, 2010. "Power variation for Itô integrals with respect to "&agr;"-stable processes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(s1), pages 276-289.
    11. Andreas Basse-O'Connor & Raphaël Lachièze-Rey & Mark Podolskij, 2015. "Limit theorems for stationary increments Lévy driven moving averages," CREATES Research Papers 2015-56, Department of Economics and Business Economics, Aarhus University.
    12. Pakkanen, Mikko S., 2014. "Limit theorems for power variations of ambit fields driven by white noise," Stochastic Processes and their Applications, Elsevier, vol. 124(5), pages 1942-1973.
    13. Corcuera, José Manuel & Hedevang, Emil & Pakkanen, Mikko S. & Podolskij, Mark, 2013. "Asymptotic theory for Brownian semi-stationary processes with application to turbulence," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2552-2574.
    14. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2014. "Discretization of Lévy semistationary processes with application to estimation," CREATES Research Papers 2014-21, Department of Economics and Business Economics, Aarhus University.

  20. Jean Jacod & Yingying Li & Per A. Mykland & Mark Podolskij & Mathias Vetter, 2007. "Microstructure Noise in the Continuous Case: The Pre-Averaging Approach - JLMPV-9," CREATES Research Papers 2007-43, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    3. Jianqing Fan & Yingying Li & Ke Yu, 2012. "Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 412-428, March.
    4. Podolskij, Mark & Vetter, Mathias, 2008. "Bipower-type estimation in a noisy diffusion setting," Technical Reports 2008,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk — realised semivariance," CREATES Research Papers 2008-42, Department of Economics and Business Economics, Aarhus University.
    6. Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Series Working Papers 438, University of Oxford, Department of Economics.
    7. Xinghua Zheng & Yingying Li, 2010. "On the estimation of integrated covariance matrices of high dimensional diffusion processes," Papers 1005.1862, arXiv.org, revised Mar 2012.
    8. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
    9. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2011. "Modeling microstructure noise with mutually exciting point processes," Papers 1101.3422, arXiv.org.
    10. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    11. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    12. Olaf Posch, 2009. "Explaining Output Volatility: The Case of Taxation," CESifo Working Paper Series 2751, CESifo Group Munich.
    13. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    14. Todorov, Viktor, 2009. "Estimation of continuous-time stochastic volatility models with jumps using high-frequency data," Journal of Econometrics, Elsevier, vol. 148(2), pages 131-148, February.
    15. Torben B. Rasmussen, 2009. "Jump Testing and the Speed of Market Adjustment," CREATES Research Papers 2009-08, Department of Economics and Business Economics, Aarhus University.
    16. Jean Jacod & Mark Podolskij & Mathias Vetter, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-61, Department of Economics and Business Economics, Aarhus University.

  21. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2006. "Bias-Correcting the Realized Range-Based Variance in the Presence of Market Microstructure Noise," Technical Reports 2006,52, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Liu, Qiang & Liu, Yiqi & Liu, Zhi & Wang, Li, 2018. "Estimation of spot volatility with superposed noisy data," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 62-79.
    2. Massimiliano Caporin & Gabriel G. Velo, 2011. "Modeling and forecasting realized range volatility," "Marco Fanno" Working Papers 0128, Dipartimento di Scienze Economiche "Marco Fanno".
    3. Fangfang Wang, 2016. "An Unbiased Measure of Integrated Volatility in the Frequency Domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 147-164, March.
    4. Filip Zikes, 2017. "Measuring Transaction Costs in the Absence of Timestamps," Finance and Economics Discussion Series 2017-045, Board of Governors of the Federal Reserve System (US).
    5. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    6. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
    7. Wu, Chih-Chiang & Chiu, Junmao, 2017. "Economic evaluation of asymmetric and price range information in gold and general financial markets," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 53-68.
    8. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers 2014-27, Department of Economics and Business Economics, Aarhus University.
    9. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 535-551.
    10. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    11. Neda Todorova, 2012. "Volatility estimators based on daily price ranges versus the realized range," Applied Financial Economics, Taylor & Francis Journals, vol. 22(3), pages 215-229, February.
    12. Caporin, Massimiliano & Velo, Gabriel G., 2015. "Realized range volatility forecasting: Dynamic features and predictive variables," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 98-112.
    13. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 9(3), pages 1-25, July.
    14. Beatriz Vaz de Melo Mendes & Victor Bello Accioly, 2017. "Improving (E)GARCH forecasts with robust realized range measures: Evidence from international markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 631-658, October.
    15. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    16. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.

  22. Christensen, Kim & Podolskij, Mark, 2006. "Range-Based Estimation of Quadratic Variation," Technical Reports 2006,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Liu, Qiang & Liu, Yiqi & Liu, Zhi & Wang, Li, 2018. "Estimation of spot volatility with superposed noisy data," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 62-79.
    4. Ma, Feng & Liu, Jing & Huang, Dengshi & Chen, Wang, 2017. "Forecasting the oil futures price volatility: A new approach," Economic Modelling, Elsevier, vol. 64(C), pages 560-566.
    5. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    6. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    7. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    8. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    9. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    10. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Neil Shephard & Kevin Sheppard, 2012. "Efficient and feasible inference for the components of financial variation using blocked multipower variation," Economics Series Working Papers 593, University of Oxford, Department of Economics.
    12. Tseng-Chan Tseng & Hung-Cheng Lai & Cha-Fei Lin, 2012. "The impact of overnight returns on realized volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 22(5), pages 357-364, March.
    13. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(1), pages 29-56, Winter.
    14. Mark Podolskij & Daniel Ziggel, 2007. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2007-26, Department of Economics and Business Economics, Aarhus University.
    15. Silja Kinnebrock & Mark Podolskij, 2007. "A Note on the Central Limit Theorem for Bipower Variation of General Functions," OFRC Working Papers Series 2007fe03, Oxford Financial Research Centre.
    16. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    17. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    18. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    19. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.

  23. Dette, Holger & Podolskij, Mark, 2005. "Testing the parametric form of the volatility in continuous time diffusion models: an empirical process approach," Technical Reports 2005,50, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Dette, Holger & Podolskij, Mark, 2008. "Testing the parametric form of the volatility in continuous time diffusion models--a stochastic process approach," Journal of Econometrics, Elsevier, vol. 143(1), pages 56-73, March.
    2. Mark Podolskij & Daniel Ziggel, 2007. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2007-26, Department of Economics and Business Economics, Aarhus University.
    3. Mark Podolskij & Mathieu Rosenbaum, 2012. "Testing the local volatility assumption: a statistical approach," Annals of Finance, Springer, vol. 8(1), pages 31-48, February.

  24. Ole Barndorff-Nielsen & Svend Erik Graversen & Jean Jacod & Mark Podolskij & Neil Shephard, 2004. "A Central Limit Theorem for Realised Power and Bipower Variations of Continuous Semimartingales," Economics Papers 2004-W29, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    3. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Ole Barndorff-Nielsen & Neil Shephard, 2004. "Multipower Variation and Stochastic Volatility," Economics Papers 2004-W30, Economics Group, Nuffield College, University of Oxford.
    5. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    6. Taamouti, Abderrahim & García, René & Dufour, Jean-Marie, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Podolskij, Mark & Vetter, Mathias, 2008. "Bipower-type estimation in a noisy diffusion setting," Technical Reports 2008,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CARF F-Series CARF-F-145, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Christensen, Kim & Podolskij, Mark, 2006. "Range-Based Estimation of Quadratic Variation," Technical Reports 2006,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    11. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk — realised semivariance," CREATES Research Papers 2008-42, Department of Economics and Business Economics, Aarhus University.
    12. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    13. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    14. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Limit theorems for functionals of higher order differences of Brownian semi-stationary processes," CREATES Research Papers 2009-60, Department of Economics and Business Economics, Aarhus University.
    15. Almut Veraart, 2008. "Inference for the jump part of quadratic variation of Itô semimartingales," CREATES Research Papers 2008-17, Department of Economics and Business Economics, Aarhus University.
    16. Nikolaus Hautsch & Mark Podolskij, 2010. "Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," CREATES Research Papers 2010-29, Department of Economics and Business Economics, Aarhus University.
    17. Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2011. "A Functional Filtering and Neighborhood Truncation Approach to Integrated Quarticity Estimation," NBER Working Papers 17152, National Bureau of Economic Research, Inc.
    18. Mark Podolskij & Mathias Vetter, 2009. "Understanding limit theorems for semimartingales: a short survey," CREATES Research Papers 2009-47, Department of Economics and Business Economics, Aarhus University.
    19. Diep Duong & Norman R. Swanson, 2011. "Empirical Evidence on Jumps and Large Fluctuations in Individual Stocks," Departmental Working Papers 201116, Rutgers University, Department of Economics.
    20. Carla Ysusi, 2006. "Detecting Jumps in High-Frequency Financial Series Using Multipower Variation," Working Papers 2006-10, Banco de México.
    21. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    22. Cecilia Mancini & Fabio Gobbi, 2010. "Identifying the Brownian Covariation from the Co-Jumps Given Discrete Observations," Working Papers - Mathematical Economics 2010-05, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    23. Mark Podolskij & Daniel Ziggel, 2008. "New tests for jumps: a threshold-based approach," CREATES Research Papers 2008-34, Department of Economics and Business Economics, Aarhus University.
    24. Todorov, Viktor & Bollerslev, Tim, 2010. "Jumps and betas: A new framework for disentangling and estimating systematic risks," Journal of Econometrics, Elsevier, vol. 157(2), pages 220-235, August.
    25. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    26. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij & Jeannette H.C. Woerner, 2008. "Bipower variation for Gaussian processes with stationary increments," CREATES Research Papers 2008-21, Department of Economics and Business Economics, Aarhus University.
    27. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    28. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    29. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
    30. Holger Dette & Mark Podolskij & Mathias Vetter, 2006. "Estimation of Integrated Volatility in Continuous-Time Financial Models with Applications to Goodness-of-Fit Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 259-278.
    31. Valentina Corradi & Norman Swanson & Walter Distaso, 2006. "Predictive Density Estimators for Daily Volatility Based on the Use of Realized Measures," Departmental Working Papers 200620, Rutgers University, Department of Economics.
    32. Dette, Holger & Podolskij, Mark, 2005. "Testing the parametric form of the volatility in continuous time diffusion models: an empirical process approach," Technical Reports 2005,50, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    33. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    34. Alexander Alvarez & Fabien Panloup & Monique Pontier & Nicolas Savy, 2012. "Estimation of the instantaneous volatility," Statistical Inference for Stochastic Processes, Springer, vol. 15(1), pages 27-59, April.
    35. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    36. Mark Podolskij & Daniel Ziggel, 2007. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2007-26, Department of Economics and Business Economics, Aarhus University.
    37. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Multipower Variation for Brownian Semistationary Processes," CREATES Research Papers 2009-21, Department of Economics and Business Economics, Aarhus University.
    38. Mark Podolskij & Mathieu Rosenbaum, 2012. "Testing the local volatility assumption: a statistical approach," Annals of Finance, Springer, vol. 8(1), pages 31-48, February.
    39. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    40. Bekiros, Stelios & Jlassi, Mouna & Naoui, Kamel & Uddin, Gazi Salah, 2017. "The asymmetric relationship between returns and implied volatility: Evidence from global stock markets," Journal of Financial Stability, Elsevier, vol. 30(C), pages 156-174.
    41. Rasmus Tangsgaard Varneskov, 2011. "Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise," CREATES Research Papers 2011-31, Department of Economics and Business Economics, Aarhus University.
    42. Shin Kanaya & Taisuke Otsu, 2011. "Large Deviations of Realized Volatility," Cowles Foundation Discussion Papers 1798, Cowles Foundation for Research in Economics, Yale University.
    43. Ole E. Barndorff-Nielsen & Almut E. D. Veraart, 2009. "Stochastic volatility of volatility in continuous time," CREATES Research Papers 2009-25, Department of Economics and Business Economics, Aarhus University.
    44. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    45. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    46. Carla Ysusi, 2007. "Multipower Variation Under Market Microstructure Effects," Working Papers 2007-13, Banco de México.
    47. Fabio Gobbi & Cecilia Mancini, 2006. "Identifying the covariation between the diffusion parts and the co-jumps given discrete observations," Papers math/0610621, arXiv.org, revised Jul 2008.
    48. Diep Duong & Norman R. Swanson, 2011. "Volatility in Discrete and Continuous Time Models: A Survey with New Evidence on Large and Small Jumps," Departmental Working Papers 201117, Rutgers University, Department of Economics.
    49. Carla Ysusi, 2006. "Estimating Integrated Volatility Using Absolute High-Frequency Returns," Working Papers 2006-13, Banco de México.
    50. Jean Jacod & Mark Podolskij & Mathias Vetter, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-61, Department of Economics and Business Economics, Aarhus University.

  25. Vetter, Mathias & Podolskij, Mark & Dette, Holger, 2004. "Estimation of integrated volatility in continuous time financial models with applications to goodness-of-fit testing," Technical Reports 2004,32, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2011. "Choice is Suffering: A Focused Information Criterion for Model Selection Activation Program for Disadvantaged Youths," Ruhr Economic Papers 250, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Almut Veraart, 2011. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(3), pages 253-291, September.
    3. Mark Podolskij & Katrin Wasmuth, 2013. "Goodness-of-fit testing for fractional diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 16(2), pages 147-159, July.
    4. Behl, Peter & Dette, Holger & Frondel, Manuel & Vance, Colin, 2018. "A focused information criterion for quantile regression: Evidence for the rebound effect," Ruhr Economic Papers 766, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Mark Podolskij & Mathias Vetter, 2009. "Understanding limit theorems for semimartingales: a short survey," CREATES Research Papers 2009-47, Department of Economics and Business Economics, Aarhus University.
    6. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2012. "Choice is suffering: A Focused Information Criterion for model selection," Economic Modelling, Elsevier, vol. 29(3), pages 817-822.
    7. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    8. Mark Podolskij & Katrin Wasmuth, 2012. "Goodness-of-fit testing for fractional diffusions," CREATES Research Papers 2012-12, Department of Economics and Business Economics, Aarhus University.
    9. Mark Podolskij & Daniel Ziggel, 2007. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2007-26, Department of Economics and Business Economics, Aarhus University.
    10. Masuda, Hiroki, 2013. "Asymptotics for functionals of self-normalized residuals of discretely observed stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2752-2778.
    11. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2013. "Energy substitution: When model selection depends on the focus," Energy Economics, Elsevier, vol. 39(C), pages 233-238.
    12. Mark Podolskij & Mathieu Rosenbaum, 2012. "Testing the local volatility assumption: a statistical approach," Annals of Finance, Springer, vol. 8(1), pages 31-48, February.
    13. Silja Kinnebrock & Mark Podolskij, 2007. "A Note on the Central Limit Theorem for Bipower Variation of General Functions," OFRC Working Papers Series 2007fe03, Oxford Financial Research Centre.
    14. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2011. "Being Focused: When the Purpose of Inference Matters for Model Selection," Ruhr Economic Papers 264, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. Lin, Liang-Ching & Lee, Sangyeol & Guo, Meihui, 2013. "Goodness-of-fit test for stochastic volatility models," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 473-498.
    16. Adam D. Bull, 2015. "Semimartingale detection and goodness-of-fit tests," Papers 1506.00088, arXiv.org, revised Jun 2016.

Articles

  1. Mark Podolskij & Mathieu Rosenbaum, 2012. "Testing the local volatility assumption: a statistical approach," Annals of Finance, Springer, vol. 8(1), pages 31-48, February.
    See citations under working paper version above.
  2. Nourdin, Ivan & Peccati, Giovanni & Podolskij, Mark, 2011. "Quantitative Breuer-Major theorems," Stochastic Processes and their Applications, Elsevier, vol. 121(4), pages 793-812, April.
    See citations under working paper version above.
  3. Mark Podolskij & Mathias Vetter, 2010. "Understanding limit theorems for semimartingales: a short survey," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(s1), pages 329-351.
    See citations under working paper version above.
  4. M. Podolskij & D. Ziggel, 2010. "New tests for jumps in semimartingale models," Statistical Inference for Stochastic Processes, Springer, vol. 13(1), pages 15-41, April.

    Cited by:

    1. Gilder, Dudley & Shackleton, Mark B. & Taylor, Stephen J., 2014. "Cojumps in stock prices: Empirical evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 443-459.
    2. Mohamed Arouri & Oussama M’saddek & Duc Khuong Nguyen & Kuntara Pukthuanthong, 2019. "Cojumps and asset allocation in international equity markets," Post-Print hal-02078138, HAL.
    3. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    4. Nolte, Ingmar & Xu, Qi, 2015. "The economic value of volatility timing with realized jumps," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 45-59.
    5. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    6. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    7. Masuda, Hiroki, 2013. "Asymptotics for functionals of self-normalized residuals of discretely observed stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2752-2778.
    8. Gnabo, Jean-Yves & Hvozdyk, Lyudmyla & Lahaye, Jérôme, 2014. "System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 147-174.

  5. Christensen, Kim & Oomen, Roel & Podolskij, Mark, 2010. "Realised quantile-based estimation of the integrated variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 74-98, November.
    See citations under working paper version above.
  6. Christensen, Kim & Kinnebrock, Silja & Podolskij, Mark, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Journal of Econometrics, Elsevier, vol. 159(1), pages 116-133, November.
    See citations under working paper version above.
  7. Barndorff-Nielsen, Ole E. & Corcuera, José Manuel & Podolskij, Mark, 2009. "Power variation for Gaussian processes with stationary increments," Stochastic Processes and their Applications, Elsevier, vol. 119(6), pages 1845-1865, June.
    See citations under working paper version above.
  8. Podolskij, Mark & Vetter, Mathias, 2009. "Bipower-type estimation in a noisy diffusion setting," Stochastic Processes and their Applications, Elsevier, vol. 119(9), pages 2803-2831, September.
    See citations under working paper version above.
  9. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    See citations under working paper version above.
  10. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2009. "Microstructure noise in the continuous case: The pre-averaging approach," Stochastic Processes and their Applications, Elsevier, vol. 119(7), pages 2249-2276, July.
    See citations under working paper version above.
  11. Kinnebrock, Silja & Podolskij, Mark, 2008. "A note on the central limit theorem for bipower variation of general functions," Stochastic Processes and their Applications, Elsevier, vol. 118(6), pages 1056-1070, June.
    See citations under working paper version above.
  12. Dette, Holger & Podolskij, Mark, 2008. "Testing the parametric form of the volatility in continuous time diffusion models--a stochastic process approach," Journal of Econometrics, Elsevier, vol. 143(1), pages 56-73, March.

    Cited by:

    1. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2011. "Choice is Suffering: A Focused Information Criterion for Model Selection Activation Program for Disadvantaged Youths," Ruhr Economic Papers 250, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Chen, Qiang & Zheng, Xu & Pan, Zhiyuan, 2015. "Asymptotically distribution-free tests for the volatility function of a diffusion," Journal of Econometrics, Elsevier, vol. 184(1), pages 124-144.
    3. Behl, Peter & Dette, Holger & Frondel, Manuel & Vance, Colin, 2018. "A focused information criterion for quantile regression: Evidence for the rebound effect," Ruhr Economic Papers 766, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Tianshun Yan & Changlin Mei, 2017. "A test for a parametric form of the volatility in second-order diffusion models," Computational Statistics, Springer, vol. 32(4), pages 1583-1596, December.
    5. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    6. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2012. "Choice is suffering: A Focused Information Criterion for model selection," Economic Modelling, Elsevier, vol. 29(3), pages 817-822.
    7. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    8. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    9. Mark Podolskij & Daniel Ziggel, 2007. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2007-26, Department of Economics and Business Economics, Aarhus University.
    10. Papanicolaou, Alex & Giesecke, Kay, 2016. "Variation-based tests for volatility misspecification," Journal of Econometrics, Elsevier, vol. 191(1), pages 217-230.
    11. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2013. "Energy substitution: When model selection depends on the focus," Energy Economics, Elsevier, vol. 39(C), pages 233-238.
    12. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2011. "Being Focused: When the Purpose of Inference Matters for Model Selection," Ruhr Economic Papers 264, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Adam D. Bull, 2015. "Semimartingale detection and goodness-of-fit tests," Papers 1506.00088, arXiv.org, revised Jun 2016.

  13. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.

    Cited by:

    1. Degiannakis, Stavros & Floros, Christos, 2014. "Intra-Day Realized Volatility for European and USA Stock Indices," MPRA Paper 64940, University Library of Munich, Germany, revised Jan 2015.
    2. Li, Hongquan & Hong, Yongmiao, 2011. "Financial volatility forecasting with range-based autoregressive volatility model," Finance Research Letters, Elsevier, vol. 8(2), pages 69-76, June.
    3. Jui-Cheng Hung & Tien-Wei Lou & Yi-Hsien Wang & Jun-De Lee, 2013. "Evaluating and improving GARCH-based volatility forecasts with range-based estimators," Applied Economics, Taylor & Francis Journals, vol. 45(28), pages 4041-4049, October.
    4. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Liu, Qiang & Liu, Yiqi & Liu, Zhi & Wang, Li, 2018. "Estimation of spot volatility with superposed noisy data," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 62-79.
    6. Massimiliano Caporin & Gabriel G. Velo, 2011. "Modeling and forecasting realized range volatility," "Marco Fanno" Working Papers 0128, Dipartimento di Scienze Economiche "Marco Fanno".
    7. Christensen, Kim & Podolskij, Mark, 2006. "Range-Based Estimation of Quadratic Variation," Technical Reports 2006,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Oleg Sokolinskiy & Dick van Dijk, 2011. "Forecasting Volatility with Copula-Based Time Series Models," Tinbergen Institute Discussion Papers 11-125/4, Tinbergen Institute.
    9. Fangfang Wang, 2016. "An Unbiased Measure of Integrated Volatility in the Frequency Domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 147-164, March.
    10. Peter Reinhard Hansen & Zhuo Huang, 2012. "Exponential GARCH Modeling with Realized Measures of Volatility," CREATES Research Papers 2012-44, Department of Economics and Business Economics, Aarhus University.
    11. Ma, Feng & Liu, Jing & Huang, Dengshi & Chen, Wang, 2017. "Forecasting the oil futures price volatility: A new approach," Economic Modelling, Elsevier, vol. 64(C), pages 560-566.
    12. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    13. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 15(3), pages 94-138.
    14. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    15. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    16. Massimiliano Caporin & Angelo Ranaldo & Paolo Santucci de Magistris, 2011. "On the Predictability of Stock Prices: A Case for High and Low Prices," "Marco Fanno" Working Papers 0136, Dipartimento di Scienze Economiche "Marco Fanno".
    17. Filip Zikes, 2017. "Measuring Transaction Costs in the Absence of Timestamps," Finance and Economics Discussion Series 2017-045, Board of Governors of the Federal Reserve System (US).
    18. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
    19. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    20. Degiannakis, Stavros, 2017. "The one-trading-day-ahead forecast errors of intra-day realized volatility," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1298-1314.
    21. Lin, Xiaoqiang & Fei, Fangyu & Wang, Yudong, 2011. "Analysis of the efficiency of the Shanghai stock market: A volatility perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3486-3495.
    22. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
    23. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
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    See citations under working paper version above.
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