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Threshold bipower variation and the impact of jumps on volatility forecasting

Corsi, Fulvio ; Pirino, Davide ; Renò, Roberto

Econometrics, 2010-12, Vol.159 (2), p.276-288 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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Título:
Threshold bipower variation and the impact of jumps on volatility forecasting
Autor: Corsi, Fulvio ; Pirino, Davide ; Renò, Roberto
Assuntos: Applications ; Asset pricing ; Capital market ; Capital market theory ; Economic forecasts ; Estimating techniques ; Estimation ; Estimation bias ; Exact sciences and technology ; Financial engineering ; Financial markets ; Forecasting techniques ; Inference from stochastic processes; time series analysis ; Insurance, economics, finance ; Jump detection ; Limit theorems ; Mathematical finance ; Mathematics ; Probability and statistics ; Probability theory and stochastic processes ; Quantitative Finance ; Sample size ; Sciences and techniques of general use ; Securities markets ; Statistical Finance ; Statistics ; Stochastic processes ; Studies ; Threshold estimation ; Volatility ; Volatility estimation ; Volatility estimation Jump detection Volatility forecasting Threshold estimation Financial markets ; Volatility forecasting
É parte de: Econometrics, 2010-12, Vol.159 (2), p.276-288
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: 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 components using estimators which are not only consistent, but also scarcely plagued by small sample bias. With the aim of achieving this, 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.
Editor: Amsterdam: Elsevier B.V
Idioma: Inglês

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Threshold bipower variation and the impact of jumps on volatility forecasting

Corsi, Fulvio ; Pirino, Davide ; Renò, Roberto

Amsterdam: Elsevier B.V

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