2010/11 | LEM Working Paper Series | |
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting |
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Fulvio Corsi, Davide Pirino, Roberto Reno' |
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Keywords | ||
volatility estimation, jump detection, volatility forecasting, threshold estimation, financial markets
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JEL Classifications | ||
G1,C1,C22,C53
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Abstract | ||
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.
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