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Marcel Scharth

Personal Details

First Name:Marcel
Middle Name:
Last Name:Scharth
Suffix:
RePEc Short-ID:psc385
http://www.marcelscharth.com
The University of Sydney Business School

Affiliation

Discipline of Business Analytics
Business School
University of Sydney

Sydney, Australia
http://sydney.edu.au/business/business_analytics

: + 61 2 9351 6584
+ 61 2 9351 6409
Sydney, NSW 2006
RePEc:edi:dxusyau (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Documentos de Trabajo del ICAE 2014-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  2. David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CIRJE F-Series CIRJE-F-693, CIRJE, Faculty of Economics, University of Tokyo.
  3. Marcelo Fernandes & Marcelo Cunha Medeiros & MArcelo Scharth, 2007. "Modeling and predicting the CBOE market volatility index," Textos para discussão 548, Department of Economics PUC-Rio (Brazil).
  4. Marcel Scharth & Marcelo Cunha Medeiros, 2006. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," Textos para discussão 532, Department of Economics PUC-Rio (Brazil).

Articles

  1. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
  2. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 7(2), pages 1-30, June.
  3. Siem Jan Koopman & Marcel Scharth, 2012. "The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(1), pages 76-115, December.
  4. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
  5. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.

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. David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CIRJE F-Series CIRJE-F-693, CIRJE, Faculty of Economics, University of Tokyo.

    Cited by:

    1. Manabu Asai & Michael McAleer, 2016. "A Multivariate Asymmetric Long Memory Conditional Volatility Model with X, Regularity and Asymptotics," Tinbergen Institute Discussion Papers 16-065/III, Tinbergen Institute.
    2. 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.
    3. 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.
    4. Asai, M. & McAleer, M.J. & Medeiros, M.C., 2010. "Asymmetry and Long Memory in Volatility Modelling," Econometric Institute Research Papers EI 2010-60, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. 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.
    6. Mark J. Jensen & John M. Maheu, 2014. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," Working Paper series 31_14, Rimini Centre for Economic Analysis.
    7. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2009. "Asymmetry and Leverage in Realized Volatility," CARF F-Series CARF-F-167, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    8. Asai, M. & Chang, C-L. & McAleer, M.J., 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Econometric Institute Research Papers TI 2017-038/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    10. Cathy Ning & Dinghai Xu & Tony Wirjanto, 2009. "Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data," Working Papers 006, Ryerson University, Department of Economics.
    11. Vincenzo Candila, 2013. "A Comparison Of The Forecasting Performances Of Multivariate Volatility Models," Working Papers 3_228, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    12. Siem Jan Koopman & Marcel Scharth, 2011. "The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures," Tinbergen Institute Discussion Papers 11-132/4, Tinbergen Institute.
    13. Federico M. Bandi & Roberto Reno, 2009. "Nonparametric Stochastic Volatility," Global COE Hi-Stat Discussion Paper Series gd08-035, Institute of Economic Research, Hitotsubashi University.

  2. Marcelo Fernandes & Marcelo Cunha Medeiros & MArcelo Scharth, 2007. "Modeling and predicting the CBOE market volatility index," Textos para discussão 548, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "How are VIX and Stock Index ETF Related?," Documentos de Trabajo del ICAE 2016-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 185-226.
    3. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    4. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, pages 824-839.
    5. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    6. Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Working Papers in Economics 11/17, University of Canterbury, Department of Economics and Finance.
    7. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stober, 2016. "Regime switching vine copula models for global equity and volatility indices," Papers 1604.05598, arXiv.org.
    8. Gloria Gonzalez-Rivera & Joao Henrique Mazzeu & Esther Ruiz & Helena Veiga, 2017. "A Bootstrap Approach for Generalized Autocontour Testing. Implications for VIX Forecast Densities," Working Papers 201709, University of California at Riverside, Department of Economics.
    9. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    10. Liu, Qiang & Guo, Shuxin & Qiao, Gaoxiu, 2015. "VIX forecasting and variance risk premium: A new GARCH approach," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 314-322.
    11. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    12. Campos, I. & Cortazar, G. & Reyes, T., 2017. "Modeling and predicting oil VIX: Internet search volume versus traditional mariables," Energy Economics, Elsevier, vol. 66(C), pages 194-204.
    13. Buncic, Daniel & Gisler, Katja I. M., 2015. "Global Equity Market Volatility Spillovers: A Broader Role for the United States," Economics Working Paper Series 1508, University of St. Gallen, School of Economics and Political Science.
    14. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    15. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    16. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    17. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Effects of the US stock market return and volatility on the VKOSPI," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 9, pages 1-34.
    18. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Modeling and predicting the market volatility index: The case of VKOSPI," Economics Discussion Papers 2015-7, Kiel Institute for the World Economy (IfW).
    19. Guglielmo Maria Caporale & Luis Gil-Alana & Alex Plastun, 2017. "Is Market Fear Persistent? A Long-Memory Analysis," CESifo Working Paper Series 6534, CESifo Group Munich.
    20. 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.
    21. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    22. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2016. "Volatility Jumps and Their Economic Determinants," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 29-80.
    23. 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".
    24. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    25. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    26. Gonçalves Mazzeu, Joao Henrique & González-Rivera, Gloria & Ruiz, Esther & Veiga, Helena, 2016. "A Bootstrap Approach for Generalized Autocontour Testing," DES - Working Papers. Statistics and Econometrics. WS 23457, Universidad Carlos III de Madrid. Departamento de Estadística.
    27. 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.
    28. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 368-380.
    29. Song, Wonho & Ryu, Doojin & Webb, Robert I., 2016. "Overseas market shocks and VKOSPI dynamics: A Markov-switching approach," Finance Research Letters, Elsevier, vol. 16(C), pages 275-282.
    30. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stöber, 2017. "Regime Switching Vine Copula Models for Global Equity and Volatility Indices," Econometrics, MDPI, Open Access Journal, vol. 5(1), pages 1-38, January.
    31. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    32. Ji, Qiang & Fan, Ying, 2016. "Modelling the joint dynamics of oil prices and investor fear gauge," Research in International Business and Finance, Elsevier, pages 242-251.
    33. Konstantinidi, Eirini & Skiadopoulos, George & Tzagkaraki, Emilia, 2008. "Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2401-2411, November.
    34. Smales, L.A., 2016. "Risk-on/Risk-off: Financial market response to investor fear," Finance Research Letters, Elsevier, vol. 17(C), pages 125-134.

  3. Marcel Scharth & Marcelo Cunha Medeiros, 2006. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," Textos para discussão 532, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 7(2), pages 1-30, June.
    2. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, pages 95-109.
    3. Heejoon Han & Myung D. Park & Shen Zhang, 2015. "A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 209-219, April.
    4. David E. Allen & Michael McAleer & Marcel Scharth, 2010. "Realized Volatility Risk," KIER Working Papers 753, Kyoto University, Institute of Economic Research.
    5. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    6. Majewski, A. A. & Bormetti, G. & Corsi, F., 2013. "Smile from the Past: A general option pricing framework with multiple volatility and leverage components," Working Papers 13/11, Department of Economics, City University London.
    7. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    8. Heather M. Anderson & Farshid Vahid, 2013. "Common non-linearities in multiple series of stock market volatility," Monash Econometrics and Business Statistics Working Papers 1/13, Monash University, Department of Econometrics and Business Statistics.
    9. Les Oxley & Marco Reale & Carl Scarrott & Xin Zhao, 2009. "Extreme Value GARCH modelling with Bayesian Inference," Working Papers in Economics 09/05, University of Canterbury, Department of Economics and Finance.
    10. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    11. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    12. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    13. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    14. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
    15. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    16. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-53, January.
    17. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    18. 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.
    19. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 368-380.
    20. Sergii Pypko, 2015. "Volatility Forecast in Crises and Expansions," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 8(3), pages 1-26, August.
    21. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, pages 116-141.
    22. Majewski, Adam A. & Bormetti, Giacomo & Corsi, Fulvio, 2015. "Smile from the past: A general option pricing framework with multiple volatility and leverage components," Journal of Econometrics, Elsevier, vol. 187(2), pages 521-531.
    23. Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, pages 7-15.
    24. ERIC HILLEBRAND & MArcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).

Articles

  1. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
    See citations under working paper version above.
  2. Siem Jan Koopman & Marcel Scharth, 2012. "The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(1), pages 76-115, December.

    Cited by:

    1. Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-949, CIRJE, Faculty of Economics, University of Tokyo.
    2. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2013. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 28/13, Monash University, Department of Econometrics and Business Statistics.
    3. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "Dynamic Asset Price Jumps and the Performance of High Frequency Tests and Measures," Papers 1708.09520, arXiv.org.
    4. António Alberto Santos, 2015. "The evolution of the Volatility in Financial Returns: Realized Volatility vs Stochastic Volatility Measures," GEMF Working Papers 2015-10, GEMF, Faculty of Economics, University of Coimbra.
    5. Asai, M. & Chang, C-L. & McAleer, M.J., 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Econometric Institute Research Papers TI 2017-038/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, pages 618-641.
    7. Shinichiro Shirota & Yasuhiro Omori & Hedibert. F. Lopes & Haixiang Piao, 2016. "Cholesky Realized Stochastic Volatility Model," CIRJE F-Series CIRJE-F-1019, CIRJE, Faculty of Economics, University of Tokyo.
    8. Siem Jan Koopman & Rutger Lit & Thuy Minh Nguyen, 2012. "Fast Efficient Importance Sampling by State Space Methods," Tinbergen Institute Discussion Papers 12-008/4, Tinbergen Institute, revised 16 Oct 2014.
    9. Nick Taylor, 2016. "Realised Variance Forecasting Under Box-Cox Transformations," Bristol Accounting and Finance Discussion Papers 16/4, School of Economics, Finance, and Management, University of Bristol, UK.
    10. Bekierman Jeremias & Gribisch Bastian, 2016. "Estimating stochastic volatility models using realized measures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 279-300, June.
    11. António A. F. Santos, 2015. "On the Forecasting of Financial Volatility Using Ultra-High Frequency Data," GEMF Working Papers 2015-17, GEMF, Faculty of Economics, University of Coimbra.
    12. 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.
    13. 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.
    14. Didit Nugroho & Takayuki Morimoto, 2015. "Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-Based methods," Computational Statistics, Springer, vol. 30(2), pages 491-516, June.
    15. Peter Reinhard Hansen & Pawel Janus & Siem Jan Koopman, 2016. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Tinbergen Institute Discussion Papers 16-061/III, Tinbergen Institute.
    16. Shinichiro Shirota & Yasuhiro Omori & Hedibert. F. Lopes & Haixiang Piao, 2015. "Cholesky Realized Stochastic Volatility Model," CIRJE F-Series CIRJE-F-979, CIRJE, Faculty of Economics, University of Tokyo.
    17. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2016. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 8/16, Monash University, Department of Econometrics and Business Statistics.
    18. Yuta Kurose & Yasuhiro Omori, 2016. "Multiple-block Dynamic Equicorrelations with Realized Measures, Leverage and Endogeneity," CIRJE F-Series CIRJE-F-1024, CIRJE, Faculty of Economics, University of Tokyo.

  3. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.

    Cited by:

    1. Ledermann, Daniel & Alexander, Carol, 2012. "Further properties of random orthogonal matrix simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 83(C), pages 56-79.

  4. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

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 10 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-FOR: Forecasting (8) 2007-01-13 2007-08-27 2010-07-03 2010-09-18 2011-01-03 2013-12-29 2014-08-02 2014-08-16. Author is listed
  2. NEP-RMG: Risk Management (8) 2007-01-13 2007-08-27 2010-07-03 2010-09-18 2011-01-03 2014-01-17 2014-08-02 2014-08-16. Author is listed
  3. NEP-MST: Market Microstructure (7) 2009-12-19 2010-07-03 2010-09-18 2011-01-03 2014-01-17 2014-08-02 2014-08-16. Author is listed
  4. NEP-ETS: Econometric Time Series (5) 2007-01-13 2010-07-03 2010-09-18 2011-01-03 2014-08-02. Author is listed
  5. NEP-FMK: Financial Markets (5) 2007-01-13 2009-12-19 2010-09-18 2013-12-29 2014-01-17. Author is listed
  6. NEP-BEC: Business Economics (4) 2009-12-19 2010-07-03 2010-09-18 2011-01-03
  7. NEP-BAN: Banking (3) 2009-12-19 2010-07-03 2010-09-18
  8. NEP-ORE: Operations Research (3) 2014-08-02 2014-08-16 2014-08-20
  9. NEP-CFN: Corporate Finance (2) 2007-08-27 2014-08-20
  10. NEP-ECM: Econometrics (2) 2009-12-19 2010-07-03
  11. NEP-UPT: Utility Models & Prospect Theory (2) 2009-12-19 2010-07-03
  12. NEP-MAC: Macroeconomics (1) 2007-08-27

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