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1

School of Mathematics and Statistics, University of Sydney, and Centre for Applied Financial Studies,UniSA Business School, University of South Australia, Adelaide SA 5000, Australia

2

Department of Quantitative Finance, National Tsing Hua University, Taichung 402, Taiwan

3

Econometric Institute, Erasmus University Rotterdam, Rotterdam 3000, The Netherlands

4

Tinbergen Institute, Rotterdam 3000, The Netherlands

5

Department of Quantitative Economics, Complutense University of Madrid, Madrid 28040, Spain

6

Australian School of Business, University of New South Wales, Sydney NSW 2052, Australia





*

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Abstract In 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. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the 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 SandP 500 index and eight other indexes and stocks. View Full-Text

Keywords: realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity





Author: David E. Allen 1,* , Michael McAleer 2,3,4,5 and Marcel Scharth 6

Source: http://mdpi.com/



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