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Valeri Voev

Personal Details

First Name:Valeri
Middle Name:
Last Name:Voev
Suffix:
RePEc Short-ID:pvo59
http://www.econ.au.dk/research/research-centres/creates/people/research-fellows/valeri-voev/
Terminal Degree:2008 Fachbereich Wirtschaftswissenschaften; Universität Konstanz (from RePEc Genealogy)

Affiliation

(in no particular order)

Center for Research in Econometric Analysis of Time Series (CREATES)
Institut for Økonomi (Department of Economics and Business)
Aarhus Universitet (University of Aarhus)

Aarhus, Denmark
http://www.creates.au.dk/



Building 1322, DK-8000 Aarhus C
RePEc:edi:creaudk (more details at EDIRC)

Institut for Økonomi (Department of Economics and Business)
Aarhus Universitet (University of Aarhus)

Aarhus, Denmark
http://www.au.dk/om/organisation/institutter/departmentofeconomicsandbusiness/




RePEc:edi:ifoaudk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. 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.
  2. 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.
  3. Roxana Halbleib & Valerie Voev, 2010. "Forecasting Multivariate Volatility Using the VARFIMA Model on Realized Covariance Cholesky Factors," Working Papers ECARES ECARES 2010-041, ULB -- Universite Libre de Bruxelles.
  4. Rasmus Tangsgaard Varneskov & Valeri Voev, 2010. "The Role of Realized Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts," CREATES Research Papers 2010-45, Department of Economics and Business Economics, Aarhus University.
  5. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
  6. Ingmar Nolte & Valeri Voev, 2009. "Least Squares Inference on Integrated Volatility and the Relationship between Efficient Prices and Noise," CREATES Research Papers 2009-16, Department of Economics and Business Economics, Aarhus University.
  7. Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, Department of Economics and Business Economics, Aarhus University.
  8. Roxana Chiriac & Valeri Voev, 2008. "Modelling and Forecasting Multivariate Realized Volatility," CREATES Research Papers 2008-39, Department of Economics and Business Economics, Aarhus University.

Articles

  1. Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2014. "Realized Beta Garch: A Multivariate Garch Model With Realized Measures Of Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 774-799, August.
  2. 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.
  3. Ingmar Nolte & Valeri Voev, 2011. "Least Squares Inference on Integrated Volatility and the Relationship Between Efficient Prices and Noise," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 94-108, April.
  4. Halbleib Roxana & Voev Valeri, 2011. "Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 134-152, February.
  5. Valeri Voev, 2011. "Trading Dynamics in the Foreign Exchange Market: A Latent Factor Panel Intensity Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(4), pages 685-716.
  6. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
  7. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 68-104.

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. 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.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    2. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España;Working Papers Homepage.
    3. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    4. Matteo Luciani & David Veredas, "undated". "A simple model for vast panels of volatilities," ULB Institutional Repository 2013/136239, ULB -- Universite Libre de Bruxelles.
    5. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. 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.
    7. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    8. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2011. "The Merit of High-Frequency Data in Portfolio Allocation," SFB 649 Discussion Papers SFB649DP2011-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Harry-Paul Vander Elst & David Veredas, 2014. "Disentangled Jump-Robust Realized Covariances and Correlations with Non-Synchronous Prices," Working Papers ECARES ECARES 2014-35, ULB -- Universite Libre de Bruxelles.

  2. 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.

    Cited by:

    1. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    2. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. 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.
    5. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
    6. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    7. 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.
    8. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
    9. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    10. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2011. "The Merit of High-Frequency Data in Portfolio Allocation," SFB 649 Discussion Papers SFB649DP2011-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Peter Christoffersen & Mathieu Fournier & Kris Jacobs, 2013. "The Factor Structure in Equity Options," CREATES Research Papers 2013-47, Department of Economics and Business Economics, Aarhus University.
    12. Asger Lunde & Kasper V. Olesen, 2014. "Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange," CREATES Research Papers 2013-19, Department of Economics and Business Economics, Aarhus University.

  3. Roxana Halbleib & Valerie Voev, 2010. "Forecasting Multivariate Volatility Using the VARFIMA Model on Realized Covariance Cholesky Factors," Working Papers ECARES ECARES 2010-041, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    2. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.

  4. Rasmus Tangsgaard Varneskov & Valeri Voev, 2010. "The Role of Realized Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts," CREATES Research Papers 2010-45, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. 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.
    3. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    5. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    6. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    7. 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.
    8. Dimitrios P. Louzis, 2015. "The economic value of flexible dynamic correlation models," Economics Bulletin, AccessEcon, vol. 35(1), pages 774-782.

  5. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    2. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 433-478.
    3. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," CORE Discussion Papers 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    5. Dimitrios P. Louzis, 2015. "The economic value of flexible dynamic correlation models," Economics Bulletin, AccessEcon, vol. 35(1), pages 774-782.
    6. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. 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.

  6. Ingmar Nolte & Valeri Voev, 2009. "Least Squares Inference on Integrated Volatility and the Relationship between Efficient Prices and Noise," CREATES Research Papers 2009-16, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. 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.
    3. Selma Chaker, 2013. "Volatility and Liquidity Costs," Staff Working Papers 13-29, Bank of Canada.
    4. 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.

  7. Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, 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. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    3. 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.
    4. 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.
    5. 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.

  8. Roxana Chiriac & Valeri Voev, 2008. "Modelling and Forecasting Multivariate Realized Volatility," CREATES Research Papers 2008-39, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    2. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    3. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    4. Asai, M. & Chang, C-L. & McAleer, M.J., 2016. "Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers," Econometric Institute Research Papers EI2016-41, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 433-478.
    6. Visar Malaj & Arben Malaj, 2015. "Long Memory Volatility Models in R: Application to a Regional Blue Chips Index," EJIS European Journal of Interdisciplinary Studies Articles, European Center for Science Education and Research, vol. 2, May-Augus.
    7. Oh, Dong Hwan & Patton, Andrew J., 2015. "High-Dimensional Copula-Based Distributions with Mixed Frequency Data," Finance and Economics Discussion Series 2015-50, Board of Governors of the Federal Reserve System (U.S.).
    8. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "A Simple Test on Structural Change in Long-Memory Time Series," Hannover Economic Papers (HEP) dp-592, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    9. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," CORE Discussion Papers 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model," Working Papers on Finance 1211, University of St. Gallen, School of Finance.
    11. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 15(2), pages 247-285.
    12. Pop, Raluca Elena, 2012. "Herd behavior towards the market index: evidence from Romanian stock exchange," MPRA Paper 51595, University Library of Munich, Germany.
    13. Robinson Kruse & Christian Leschinski & Michael Will, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," CREATES Research Papers 2016-17, Department of Economics and Business Economics, Aarhus University.
    14. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," CORE Discussion Papers 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Manabu Asai & Michael McAleer, 2017. "The impact of jumps and leverage in forecasting covolatility," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 638-650, October.
    16. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    17. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    18. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    19. Kleppe, Tore Selland & Liesenfeld, Roman, 2011. "Efficient high-dimensional importance sampling in mixture frameworks," Economics Working Papers 2011-11, Christian-Albrechts-University of Kiel, Department of Economics.
    20. 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.
    21. Xu, Jiawen & Perron, Pierre, 2014. "Forecasting return volatility: Level shifts with varying jump probability and mean reversion," International Journal of Forecasting, Elsevier, vol. 30(3), pages 449-463.
    22. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    23. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Working Papers in Economics 14/10, University of Canterbury, Department of Economics and Finance.
    24. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    25. Nick Taylor, 2017. "Timing Strategy Performance in the Crude Oil Futures Market," Bristol Accounting and Finance Discussion Papers 17/7, School of Economics, Finance, and Management, University of Bristol, UK.
    26. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    27. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series 1409, University of St. Gallen, School of Economics and Political Science.
    28. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
    29. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    30. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    31. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, Open Access Journal, vol. 5(2), pages 1-31, June.
    32. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    33. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    34. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    35. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    36. Gribisch, Bastian, 2013. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79823, Verein für Socialpolitik / German Economic Association.
    37. Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2011. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-812, CIRJE, Faculty of Economics, University of Tokyo.
    38. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    39. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010-07, Christian-Albrechts-University of Kiel, Department of Economics.
    40. Wang, Hao & Yue, Mengqi & Zhao, Hua, 2015. "Cojumps in China's spot and stock index futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 35(PB), pages 541-557.
    41. 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.
    42. 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.
    43. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    44. Dimitrios P. Louzis, 2015. "The economic value of flexible dynamic correlation models," Economics Bulletin, AccessEcon, vol. 35(1), pages 774-782.
    45. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    46. Philip Bertram & Robinson Kruse & Philipp Sibbertsen, 2013. "Fractional integration versus level shifts: the case of realized asset correlations," Statistical Papers, Springer, vol. 54(4), pages 977-991, November.
    47. 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.
    48. 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.
    49. Andre Lucas & Anne Opschoor, 2016. "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns," Tinbergen Institute Discussion Papers 16-069/IV, Tinbergen Institute, revised 07 Jul 2017.
    50. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
    51. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," CORE Discussion Papers 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    52. BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," CORE Discussion Papers 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    53. Markopoulou, Chrysi E. & Skintzi, Vasiliki D. & Refenes, Apostolos-Paul N., 2016. "Realized hedge ratio: Predictability and hedging performance," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 121-133.
    54. Dimpfl, Thomas & Jank, Stephan, 2011. "Can internet search queries help to predict stock market volatility?," CFR Working Papers 11-15, University of Cologne, Centre for Financial Research (CFR).
    55. Jin, Xin & Maheu, John M & Yang, Qiao, 2017. "Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices," MPRA Paper 81920, University Library of Munich, Germany.
    56. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series 80, National Centre for Econometric Research.
    57. 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.
    58. Parrini, Alessandro, 2012. "Indirect estimation of GARCH models with alpha-stable innovations," MPRA Paper 38544, University Library of Munich, Germany.
    59. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.
    60. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.

Articles

  1. Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2014. "Realized Beta Garch: A Multivariate Garch Model With Realized Measures Of Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 774-799, August.

    Cited by:

    1. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 15(2), pages 247-285.
    2. Sofiane Aboura & Julien Chevallier, 2014. "Cross-Market Spillovers with ‘Volatility Surprise’," EconomiX Working Papers 2014-46, University of Paris Nanterre, EconomiX.
    3. 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.
    4. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    5. Matteo Bonato and Luca Taschini, 2016. "Comovement and the Financialization of Commodities," Working Papers 587, Economic Research Southern Africa.
    6. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series 1409, University of St. Gallen, School of Economics and Political Science.
    7. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    8. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, Open Access Journal, vol. 5(2), pages 1-31, June.
    9. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    10. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    11. Yuta Yamauchi & Yasuhiro Omori, 2016. "Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations ," CIRJE F-Series CIRJE-F-1029, CIRJE, Faculty of Economics, University of Tokyo.
    12. 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.
    13. Huang, Zhuo & Liu, Hao & Wang, Tianyi, 2016. "Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model," Economic Modelling, Elsevier, vol. 52(PB), pages 812-821.
    14. Darolles, Serges & Francq, Christian & Laurent, Sébastien, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," MPRA Paper 83988, University Library of Munich, Germany.
    15. Racca, P. & Casarin, R. & Dondio, P. & Squazzoni, F., 2018. "Relating group size and posting activity of an online community of financial investors: Regularities and seasonal patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 458-466.
    16. Jin, Xin & Maheu, John M & Yang, Qiao, 2017. "Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices," MPRA Paper 81920, University Library of Munich, Germany.
    17. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.

  2. 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.
    See citations under working paper version above.
  3. Ingmar Nolte & Valeri Voev, 2011. "Least Squares Inference on Integrated Volatility and the Relationship Between Efficient Prices and Noise," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 94-108, April.
    See citations under working paper version above.
  4. Halbleib Roxana & Voev Valeri, 2011. "Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 134-152, February.
    See citations under working paper version above.
  5. Valeri Voev, 2011. "Trading Dynamics in the Foreign Exchange Market: A Latent Factor Panel Intensity Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(4), pages 685-716.

    Cited by:

    1. Katarzyna Bień-Barkowska, 2014. "“Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market," Bank i Kredyt, Narodowy Bank Polski, vol. 45(3), pages 197-224.
    2. Vasios, Michalis & Payne, Richard & Nolte, Ingmar, 2015. "Profiting from Mimicking Strategies in Non-Anonymous Markets," MPRA Paper 61710, University Library of Munich, Germany.

  6. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
    See citations under working paper version above.
  7. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 68-104.

    Cited by:

    1. Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing," Tinbergen Institute Discussion Papers 13-003/III, Tinbergen Institute.
    2. Yeh, Jin-Huei & Wang, Jying-Nan, 2010. "Correcting microstructure comovement biases for integrated covariance," Finance Research Letters, Elsevier, vol. 7(3), pages 184-191, September.
    3. 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.
    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. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Post-Print hal-00732537, HAL.
    6. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).
    7. A. Saichev & D. Sornette, 2012. "A simple microstructure return model explaining microstructure noise and Epps effects," Papers 1202.3915, arXiv.org.
    8. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    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. 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.
    11. Mancino Maria Elvira & Simona Sanfelici, 2009. "Covariance estimation and dynamic asset allocation under microstructure effects via Fourier methodology," Working Papers - Mathematical Economics 2009-09, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    12. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010. "Bootstrapping realized multivariate volatility measures," MPRA Paper 40123, University Library of Munich, Germany.
    14. Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre.
    15. Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 10(4), pages 591-616, September.
    16. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    17. 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.
    18. Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, Department of Economics and Business Economics, Aarhus University.
    19. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    20. Corsi, Fulvio & Peluso, Stefano & Audrino, Francesco, 2012. "Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation," Economics Working Paper Series 1202, University of St. Gallen, School of Economics and Political Science.
    21. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    22. 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.
    23. 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.
    24. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    25. 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.
    26. 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.
    27. 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.
    28. Bent Jesper Christensen & Rasmus T. Varneskov, 2016. "Dynamic Global Currency Hedging," CREATES Research Papers 2016-03, Department of Economics and Business Economics, Aarhus University.
    29. Tóth, Bence & Kertész, János, 2009. "Accurate estimator of correlations between asynchronous signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1696-1705.
    30. Dave Berger & H. J. Turtle, 2009. "Time Variability In Market Risk Aversion," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 285-307.
    31. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
    32. Hautsch, Nikolaus & Kyj, Lada M. & Hautsch, Nikolaus, 2009. "A blocking and regularization approach to high dimensional realized covariance estimation," CFS Working Paper Series 2009/20, Center for Financial Studies (CFS).
    33. Taro Kanatani, 2007. "Finite Sample Analysis of Weighted Realized Covariance with Noisy Asynchronous Observations," KIER Working Papers 634, Kyoto University, Institute of Economic Research.
    34. 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.
    35. Jozef Barunik & Lukas Vacha, 2016. "Do co-jumps impact correlations in currency markets?," Papers 1602.05489, arXiv.org, revised Oct 2017.
    36. Masato Ubukata & Kosuke Oya, 2008. "A Test for Dependence and Covariance Estimator of Market Microstructure Noise," Discussion Papers in Economics and Business 07-03-Rev.2, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
    37. Bannouh, K. & van Dijk, D.J.C. & Martens, M.P.E., 2008. "Range-based covariance estimation using high-frequency data: The realized co-range," Econometric Institute Research Papers EI 2007-53, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    38. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Impact of the tick-size on financial returns and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4828-4843.
    39. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    40. 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.
    41. 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.
    42. Masato Ubukata & Kosuke Oya, 2007. "Test of Unbiasedness of the Integrated Covariance Estimation in the Presence of Noise," Discussion Papers in Economics and Business 07-03, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
    43. Haugom, Erik & Lien, Gudbrand & Veka, Steinar & Westgaard, Sjur, 2014. "Covariance estimation using high-frequency data: Sensitivities of estimation methods," Economic Modelling, Elsevier, vol. 43(C), pages 416-425.
    44. 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.

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 11 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (8) 2008-06-27 2008-09-05 2009-05-02 2009-12-05 2010-02-05 2010-09-03 2011-01-30 2011-01-30. Author is listed
  2. NEP-ETS: Econometric Time Series (8) 2008-06-27 2008-09-05 2010-02-05 2010-09-03 2010-12-11 2011-01-30 2011-01-30 2012-12-10. Author is listed
  3. NEP-FOR: Forecasting (6) 2008-09-05 2009-12-05 2010-09-03 2011-01-30 2011-01-30 2012-12-10. Author is listed
  4. NEP-MST: Market Microstructure (6) 2008-06-27 2009-05-02 2010-09-03 2011-01-30 2011-01-30 2012-12-10. Author is listed
  5. NEP-ORE: Operations Research (4) 2008-09-05 2009-12-05 2010-02-05 2011-01-30
  6. NEP-FMK: Financial Markets (3) 2008-09-05 2009-05-02 2010-12-11
  7. NEP-RMG: Risk Management (2) 2011-01-30 2012-12-10
  8. NEP-CFN: Corporate Finance (1) 2008-06-27
  9. NEP-UPT: Utility Models & Prospect Theory (1) 2008-09-05

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