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Title of Journal: Stat Comput

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Abbravation: Statistics and Computing

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Springer US

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DOI

10.1016/0378-4363(82)90102-4

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1573-1375

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Bayesian inference for HestonSTAR models

Authors: Osnat Stramer Xiaoyu Shen Matthew Bognar
Publish Date: 2016/03/26
Volume: 27, Issue: 2, Pages: 331-348
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Abstract

The HestonSTAR model is a new class of stochastic volatility models defined by generalizing the Heston model to allow the volatility of the volatility process as well as the correlation between asset logreturns and variance shocks to change across different regimes via smooth transition autoregressive STAR functions The form of the STAR functions is very flexible much more so than the functions introduced in Jones J Econom 116181–224 2003 and provides the framework for a wide range of stochastic volatility models A Bayesian inference approach using data augmentation techniques is used for the parameters of our model We also explore goodness of fit of our HestonSTAR model Our analysis of the SP 500 and VIX index demonstrates that the HestonSTAR model is more capable of dealing with large market fluctuations such as in 2008 compared to the standard Heston model

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