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Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm

Guardat en:
Publicat a:arXiv.org (May 14, 2013), p. n/a
Autor principal: Takaishi, Tetsuya
Publicat:
Cornell University Library, arXiv.org
Matèries:
Monte Carlo simulation
Stochastic models
Model accuracy
Markov chains
Bayesian analysis
Volatility
Algorithms
Autoregressive models
Empirical analysis
Stock exchanges
Statistical inference
Computer simulation
Accés en línia:Citation/Abstract
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Resum:The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model.
ISSN:2331-8422
DOI:10.1088/1742-6596/423/1/012021
Font:Engineering Database