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Statistics and Computing

, Volume 27, Issue 2, pp 331–348 | Cite as

Bayesian inference for Heston-STAR models

  • Osnat StramerEmail author
  • Xiaoyu Shen
  • Matthew Bognar
Article
  • 258 Downloads

Abstract

The Heston-STAR 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 log-returns 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 116:181–224, 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 Heston-STAR model. Our analysis of the S&P 500 and VIX index demonstrates that the Heston-STAR model is more capable of dealing with large market fluctuations (such as in 2008) compared to the standard Heston model.

Keywords

Stochastic volatility model Heston model Smooth transition autoregressive (STAR) model Bayesian inference Data augmentation 

Notes

Acknowledgments

We would like to thank the coordinating editor and referee for their valuable comments. Their feedback helped to significantly improve the manuscript.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Statistics and Actuarial ScienceUniversity of IowaIowa CityUSA

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