Value at risk estimation under stochastic volatility models using adaptive PMCMC methods

DOI10.1080/03610918.2016.1235188zbMath1385.62031OpenAlexW2526239836MaRDI QIDQ4607381

Ratthachat Chatpatanasiri, Xinxia Yang, Pairote Sattayatham

Publication date: 13 March 2018

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2016.1235188

zbMATH Keywords

stochastic volatilityBayesian statisticsvalue-at-riskadaptive MCMCparticle filter

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Bayesian inference (62F15)

Related Items

Bayesian analysis of moving average stochastic volatility models: modeling in-mean effects and leverage for financial time series

Cites Work

• The pseudo-marginal approach for efficient Monte Carlo computations
• Optimum consumption and portfolio rules in a continuous-time model
• On leverage in a stochastic volatility model
• On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
• Particle learning and smoothing
• Alternative models for stock price dynamics.
• Optimal scaling for various Metropolis-Hastings algorithms.
• Markov chain Monte Carlo methods for stochastic volatility models.
• Model selection and adaptive Markov chain Monte Carlo for Bayesian cointegrated VAR models
• MODELING STOCHASTIC VOLATILITY: A REVIEW AND COMPARATIVE STUDY
• Filtering via Simulation: Auxiliary Particle Filters
• Particle Markov Chain Monte Carlo Methods
• Option pricing when underlying stock returns are discontinuous
• Adaptive MCMC methods for inference on affine stochastic volatility models with jumps
• Unnamed Item
• Unnamed Item