Value at risk estimation under stochastic volatility models using adaptive PMCMC methods |
| |
Authors: | Xinxia Yang Ratthachat Chatpatanasiri Pairote Sattayatham |
| |
Affiliation: | 1. School of Mathematics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand;2. School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China |
| |
Abstract: | In this paper, we propose a value-at-risk (VaR) estimation technique based on a new stochastic volatility model with leverage effect, nonconstant conditional mean and jump. In order to estimate the model parameters and latent state variables, we integrate the particle filter and adaptive Markov Chain Monte Carlo (MCMC) algorithms to develop a novel adaptive particle MCMC (A-PMCMC) algorithm. Comprehensive simulation experiments based on three stock indices and two foreign exchange time series show effectiveness of the proposed A-PMCMC algorithm and the VaR estimation technique. |
| |
Keywords: | Adaptive MCMC Bayesian statistics Particle filter Stochastic volatility Value-at-risk |
|
|