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Threshold bipower variation and the impact of jumps on volatility forecasting

  • Fulvio Corsi

    (University of Lugano and Swiss Finance Institute - University of Lugano and Swiss Finance Institute)

  • Davide Pirino
  • Roberto Renò

    ()

    (Dipartimento di Economia Politica - Università di Siena)

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 variation) 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 variation 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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Paper provided by HAL in its series Post-Print with number hal-00741630.

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Date of creation: 15 Oct 2010
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Publication status: Published, Journal of Econometrics, 2010, 159, 2, 276
Handle: RePEc:hal:journl:hal-00741630
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