Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm

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Detalles Bibliográficos
Publicado en:	arXiv.org (May 14, 2013), p. n/a
Autor principal:	Takaishi, Tetsuya
Publicado:	Cornell University Library, arXiv.org
Materias:	Monte Carlo simulation Stochastic models Model accuracy Markov chains Bayesian analysis Volatility Algorithms Autoregressive models Empirical analysis Stock exchanges Statistical inference Computer simulation
Acceso en línea:	Citation/Abstract Full text outside of ProQuest
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Descripción
Resumen:	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
Fuente:	Engineering Database