Premium
Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns
Author(s) -
Warty Samir P.,
Lopes Hedibert F.,
Polson Nicholas G.
Publication year - 2017
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2258
Subject(s) - particle filter , stochastic volatility , markov chain monte carlo , bayesian probability , econometrics , computer science , volatility (finance) , bayesian inference , sequential estimation , bayes estimator , algorithm , mathematics , artificial intelligence , kalman filter
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.