Please use this identifier to cite or link to this item:
Corsi, Fulvio
Pirino, Davide
Reno, Roberto
Year of Publication: 
Series/Report no.: 
LEM Working Paper Series 2010/11
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.
volatility estimation
jump detection
volatility forecasting
threshold estimation
financial markets
Document Type: 
Working Paper

Files in This Item:
531.99 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.