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Please use this identifier to cite or link to this item: http://purl.org/purl/2856

Title: Computationally Efficient Bootstrap Prediction Intervals for Returns and Volatilities in ARCH and GARCH Processes
Authors: Chen, Bei
Gel, Yulia R
Balakrishna, N
Abraham, Bovas
Keywords: financial time series
volatility forecasting
bootstrap
non- Gaussian distribution
Issue Date: Jan-2011
Publisher: John Wiley & Sons
Abstract: We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.
URI: http://dyuthi.cusat.ac.in/purl/2856
ISSN: 1099-131X
Appears in Collections:Dr.N. Balakrishna

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