Volatility, Jumps and Predictability of Returns: a Sequential Analysis
S. Bordignon and
Davide Raggi
Working Papers from Dipartimento Scienze Economiche, Universita' di Bologna
Abstract:
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effects and non constant conditional mean and jumps. We are interested in estimating the time invariant parameters and the non-observable dynamics involved in the model. Our idea relies on the auxiliary particle filter algorithm mixed together with Markov Chain Monte Carlo (MCMC) methodology. Adding an MCMC step to the auxiliary particle filter prevents numerical degeneracies in the sequential algorithm and allows sequential evaluation of the fixed parameters and the latent processes. Empirical evaluation on simulated and real data is presented to assess the performance of the algorithm.
Date: 2008-05
New Economics Papers: this item is included in nep-fmk and nep-rmg
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Related works:
Journal Article: Volatility, Jumps, and Predictability of Returns: A Sequential Analysis (2011) 
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Persistent link: https://EconPapers.repec.org/RePEc:bol:bodewp:636
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