Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/5961
Type: Working Paper
Title: Sequential bayesian learning for stochastic volatility with variance-gamma jumps in return
Author: Warty, Samir P.
Lopes, Hedibert Freitas
Polson, Nicholas G.
Publication Date: 2014
Abstract: In this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance-gamma jumps in returns (SVVG). We develop an estimation algorithm that adapts the sequential learning auxiliary particle filter proposed by Carvalho, Johannes, Lopes, and Polson (2010) to SVVG. Simulation evidence and empirical estimation results indicate that this approach is able to filter latent variances, identify latent jumps in returns, and provide sequential learning about the static parameters of SVVG. We demonstrate comparative performance of the sequential algorithm and offline Markov Chain Monte Carlo in synthetic and real data applications.
Keywords (english terms): Auxiliary particle filtering
Bayesian learning
sequential Monte Carlo
stochastic volatility
variance gamma
Language: Inglês
CNPq Area: Ciências Exatas e da Terra
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR
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