Realized Volatility Risk
AbstractIn this paper we document that realized variation measures constructed from high- frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
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Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/26.
Length: 39 pages
Date of creation: 01 May 2010
Date of revision:
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Realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity;
Other versions of this item:
- David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CARF F-Series CARF-F-197, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, revised Jan 2010.
- David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CIRJE F-Series CIRJE-F-693, CIRJE, Faculty of Economics, University of Tokyo.
- David E. Allen & Michael McAleer & Marcel Scharth, 2010. "Realized Volatility Risk," KIER Working Papers 753, Kyoto University, Institute of Economic Research.
- NEP-ALL-2010-07-03 (All new papers)
- NEP-BAN-2010-07-03 (Banking)
- NEP-BEC-2010-07-03 (Business Economics)
- NEP-ECM-2010-07-03 (Econometrics)
- NEP-ETS-2010-07-03 (Econometric Time Series)
- NEP-FOR-2010-07-03 (Forecasting)
- NEP-MST-2010-07-03 (Market Microstructure)
- NEP-RMG-2010-07-03 (Risk Management)
- NEP-UPT-2010-07-03 (Utility Models & Prospect Theory)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Neil Shephard & Kevin Sheppard, 2009.
"Realising the future: forecasting with high frequency based volatility (HEAVY) models,"
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- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Papers 2009-W03, Economics Group, Nuffield College, University of Oxford.
- 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.
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- 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.
- Manabu Asai & Michael McAleer, 2012. "Leverage and Feedback E ects on Multifactor Wishart Stochastic Volatility for Option Pricing," Documentos del Instituto Complutense de AnÃ¡lisis EconÃ³mico 2013-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
- Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing," KIER Working Papers 840, Kyoto University, Institute of Economic Research.
- 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.
- 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.
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