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Bayesian estimation of an extended local scale stochastic volatility model

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Abstract

A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and an exact simulation smoother are presented. The model is applied to simulated data and to publicly available exchange rate and asset return data. Simulation smoothing turns out to be essential for the accurate interval estimation of volatilities. Bayes factors show that the new model is competitive with GARCH and Lognormal stochastic volatility formulations. Its forecasting performance is comparable to GARCH.

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Bibliographic Info

Paper provided by Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland in its series DQE Working Papers with number 15.

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Length: 54 pages
Date of creation: 04 Aug 2009
Date of revision: 12 Nov 2011
Publication status: Published (in revised form) in Journal of Econometrics, 2011, vol. 162, pp. 369-382
Handle: RePEc:fri:dqewps:wp0015

Note: Scheduled for presentation at the ESEM Barcelona meeting, August 2009
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Keywords: State space models; Markov chain Monte Carlo; simulation smoothing; generalized error distribution; generalized t distribution;

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