Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting
Fulvio Corsi (),
Davide Pirino and
LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy
This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not only consistent, but also scarcely plagued by small-sample bias. To this purpose, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower vari- ation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic varia- tion in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.
Keywords: volatility estimation; jump detection; volatility forecasting; threshold estimation; financial markets (search for similar items in EconPapers)
JEL-codes: G1 C1 C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mst
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Journal Article: Threshold bipower variation and the impact of jumps on volatility forecasting (2010)
Working Paper: Threshold bipower variation and the impact of jumps on volatility forecasting (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:ssa:lemwps:2010/11
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