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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;
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응용통계연구, 2016. vol.29. 1, pp.99-112 crossref(new window)
Bayesian inference on multivariate asymmetric jump-diffusion models, Korean Journal of Applied Statistics, 2016, 29, 1, 99  crossref(new windwow)
Andersen, T. G., Benzoni, L. and Lund, J. (2002). Empirical investigation of continuous-time equity return models, Journal of Finance, 57, 1239-1284. crossref(new window)

Besag, J. and Green, P. J. (1993). Spatial statistics and Bayesian computation, Journal of the Royal Statistical Society, Series B, 55, 25-37.

Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1995). Efficient parameterization for normal linear mixed models, Biometrika, 82, 479-488. crossref(new window)

Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulations using multiple sequences (with discussion), Statistical Science, 7, 457-472. crossref(new window)

Kou, S. G. (2002). A jump-diffusion model for option pricing, Management Science, 48, 1086-1101. crossref(new window)

Liu, J. S. (1994). The collapsed Gibbs sampler in Bayesian computations with applications to gene regulation problem, Journal of the American Statistical Association, 89, 958-966. crossref(new window)

Liu, J. S., Wong, W. H. and Kong, A. (1994). Covariance structure of the Gibbs sampler with applications to comparisons of estimators and augmentation schemes, Biometrika, 81, 27-40. crossref(new window)

Liu, J. S. and Wu, Y. N. (1999). Parameter expansion for data augmentation, Journal of the American Statistical Association, 94, 1264-1274. crossref(new window)

Meng, X. L. and van Dyk, D. A. (1999). Seeking efficient data augmentation schemes via conditional and marginal augmentation, Biometrika, 86, 301-320. crossref(new window)

Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous, Journal of Financial Economics, 3, 125-144. crossref(new window)

Park, T. (2009). Statistical challenges in spectral analysis in high-energy astrophysics, ISBA Bulletin, 16, 13-17.

Park, T. (2011). Bayesian analysis of individual choice behavior with aggregate data, Journal of Computational and Graphical Statistics, 20, 158-173. crossref(new window)

Park, T. and Bae, W. (2014). Bayesian semi-parametric regression for quantile residual lifetime, Communications for Statistical Applications and Methods, 21, 285-296. crossref(new window)

Park, T., Jeong, J. and Lee, J. (2012a). Bayesian nonparametric inference on quantile residual life function: Application to breast cancer data, Statistics In Medicine, 31, 1972-1985. crossref(new window)

Park, T., Krafty, R. T. and Sanchez, A. I. (2012b). Bayesian semi-parametric analysis of Poisson change-point regression models: Application to policy making in Cali, Colombia, Journal of Applied Statistics, 39, 2285-2298. crossref(new window)

Park, T. and Min, S. (2014). Partially collapsed Gibbs sampling for linear mixed-effects models, Communications in Statistics - Simulation and Computation, DOI:10.1080/03610918.2013.857687. crossref(new window)

Park, T. and van Dyk, D. A. (2009). Partially collapsed Gibbs samplers: Illustrations and applications, Journal of Computational and Graphical Statistics, 18, 283-305. crossref(new window)

Park, T., van Dyk, D. A. and Siemiginowska, A. (2008). Searching for narrow emission lines in X-ray spectra: Computation and methods, The Astrophysical Journal, 688, 807-825. crossref(new window)

Ramezani, C. A. and Zeng, Y. (1998). Maximum likelihood estimation of asymmetric jump-diffusion process: Cpplication to security prices, Working Paper, Department of Mathematics and Statistics, University of Missouri, Kansas City, Available from:

Ramezani, C. A. and Zeng, Y. (2007). Maximum likelihood estimation of the double exponential jump diffusion process, Annals of Finance, 3, 487-507. crossref(new window)

Roberts, G. O., Gelman, A. and Gilks, W. (1997). Weak convergence and optimal scaling of random walk Metropolis algorithms, The Annals of Applied Probability, 7, 110-120. crossref(new window)

Roberts, G. O. and Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms, Statistical Science, 16, 351-367. crossref(new window)

Roberts, G. O. and Rosenthal, J. S. (2007). Coupling and ergodicity of adaptive MCMC, Journal of Applied Probability, 44, 458-475. crossref(new window)

van Dyk, D. A. (2000). Nesting EM algorithms for computational efficiency, Statistical Sinica, 10, 203-225.

van Dyk, D. A. and Park, T. (2008). Partially collapsed Gibbs samplers: Theory and methods, Journal of the American Statistical Association, 193, 790-796.

van Dyk, D. A. and Park, T. (2011). Partially collapsed Gibbs sampling and path-adaptive Metropolis-Hastings in high-energy astrophysics, Handbook of Markov Chain Monte Carlo, Chapman & Hall/CRC Press, 383-400.

Xu, X., Meng, X. L., and Yu, Y. (2013). Thank God that regressing Y on X is not the same as regressing X on Y: Direct and indirect residual augmentations, Journal of Computational and Graphical Statistics, 22, 598-622. crossref(new window)

Yu, Y. and Meng, X. L. (2011). To center or not to center, that is not the question: An ancillarity-sufficiency interweaving strategy(ASIS) for boosting MCMC efficiency, Journal of Computational and Graphical Statistics, 20, 531-570. crossref(new window)