Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns

Warty Samir P.; Lopes Hedibert F.; Polson Nicholas G.

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Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns

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摘要

In this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance‐gamma (SVVG) jumps in returns. We develop an estimation algorithm that combines the sequential learning auxiliary particle filter with the particle learning filter. 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 off‐line Markov Chain Monte Carlo in synthetic and real data applications.

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《Applied stochastic models in business and industry》 |2018年第4期|460-479|共20页
作者
Warty Samir P.; Lopes Hedibert F.; Polson Nicholas G.;
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作者单位

Insper ‐ Institute of Education and ResearchS?o Paulo,Brazil;

The University of Chicago Booth School of BusinessChicago,IL,USA;

Analysis Group, Inc.180 N. Stetson Ave.,Chicago,IL,60601,USA;

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原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
auxiliary particle filtering; Bayesian learning; sequential Monte Carlo; stochastic volatility; variance gamma;

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Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns

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