Bayesian analysis of stochastic volatility models with Lévy jumps: application to risk analysis
AbstractIn this paper I analyze a broad class of continuous-time jump diffusion models of asset returns. In the models, stochastic volatility can arise either from a diffusion part, or a jump part, or both. The jump component includes either compound Poisson or Lévy alpha-stable jumps. To be able to estimate the models with latent Lévy alpha-stable jumps, I construct a new Markov chain Monte Carlo algorithm. I estimate all model specifications with S&P500 daily returns. I find that models with Levy alpha-stable jumps perform well in capturing return characteristics if diffusion is a source of stochastic volatility. Models with stochastic volatility from jumps and models with Poisson jumps cannot represent excess kurtosis and tails of return distribution. In density forecast and VaR analysis, the model with Levy alpha-stable jumps and joint stochastic volatility performs the best among all other specifications, since both diffusion and infinite activity jump part provide information about latent volatility.
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Bibliographic InfoPaper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2009-40.
Date of creation: 2009
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-11-21 (All new papers)
- NEP-ECM-2009-11-21 (Econometrics)
- NEP-ETS-2009-11-21 (Econometric Time Series)
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