Volatility Forecasting: Downside Risk, Jumps and Leverage Effect
Francesco Audrino () and
Yujia Hu ()
No 1138, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science
We provide new empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Leverage and volatility feedback effects among continuous and jump components of the S&P500 price and volatility dynamics are examined using recently developed methodologies to detect jumps and to disentangle their size from continuous return and continuous volatility. Granted that jumps in both return and volatility are important components for generating the two effects, we find jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improve the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative then positive jump semivariance. The model proposed is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated.
Keywords: High frequency data; Realized volatility forecasting; Downside risk; Leverage effect (search for similar items in EconPapers)
JEL-codes: C13 C22 C51 C53 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mst and nep-rmg
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Journal Article: Volatility Forecasting: Downside Risk, Jumps and Leverage Effect (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:usg:econwp:2011:38
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