Volatility, Jumps and Predictability of Returns: a Sequential Analysis

Bordignon, Silvano ; Raggi, Davide (2008) Volatility, Jumps and Predictability of Returns: a Sequential Analysis. Bologna: Dipartimento di Scienze economiche DSE, p. 38. DOI 10.6092/unibo/amsacta/4611. In: Quaderni - Working Paper DSE (636). ISSN 2282-6483.
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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.

Abstract
Document type
Monograph (Working Paper)
Creators
CreatorsAffiliationORCID
Bordignon, Silvano
Raggi, Davide
Keywords
Stochastic volatility with jumps, leverage, return's predictability, Bayesian estimation, auxiliary particle ¯lters, MCMC
Subjects
ISSN
2282-6483
DOI
Deposit date
15 Feb 2016 13:50
Last modified
15 Feb 2016 13:50
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