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Efficient Bayesian Inference on Asymmetric Jump-Diffusion Models
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 Title & Authors
Efficient Bayesian Inference on Asymmetric Jump-Diffusion Models
Park, Taeyoung; Lee, Youngeun;
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Asset pricing models that account for asymmetric volatility in asset prices have been recently proposed. This article presents an efficient Bayesian method to analyze asset-pricing models. The method is developed by devising a partially collapsed Gibbs sampler that capitalizes on the functional incompatibility of conditional distributions without complicating the updates of model components. The proposed method is illustrated using simulated data and applied to daily S&P 500 data observed from September 1980 to August 2014.
Gibbs sampler;Markov chain Monte Carlo;Pareto-Beta jump diffusion;partial collapse;Wiener process;
 Cited by
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