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Bayesian inference on multivariate asymmetric jump-diffusion models
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 Title & Authors
Bayesian inference on multivariate asymmetric jump-diffusion models
Lee, Youngeun; Park, Taeyoung;
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Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.
Bayesian analysis;collapsed Gibbs sampler;data augmentation;Markov Chain Monte Carlo;multivariate asymmetric Laplace distribution;
 Cited by
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