NDLI: Threshold variable selection of asymmetric stochastic volatility models

Content Provider	SpringerLink
Author	Chen, Cathy W. S. Liu, Feng Chi So, Mike K. P.
Copyright Year	2013
Abstract	A threshold stochastic volatility (SV) model is used for capturing time-varying volatilities and nonlinearity. Two adaptive Markov chain Monte Carlo (MCMC) methods of model selection are designed for the selection of threshold variables for this family of SV models. The first method is the direct estimation which approximates the model posterior probabilities of competing models. Using parallel MCMC sampling to estimate these probabilities, the best threshold variable is selected with the highest posterior model probability. The second method is to use the deviance information criterion to compare among these competing models and select the best one. Simulation results lead us to conclude that for large samples the posterior model probability approximation method can give an accurate approximation of the posterior probability in Bayesian model selection. The method delivers a powerful and sharp model selection tool. An empirical study of five Asian stock markets provides strong support for the threshold variable which is formulated as a weighted average of important variables.
Starting Page	2415
Ending Page	2447
Page Count	33
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	28
Issue Number	6
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-04-23
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Model selection Deviance information criterion Markov chain Monte Carlo method Posterior model probability Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Computational Mathematics Statistics, Probability and Uncertainty

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