Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution

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Abstract

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

Original languageEnglish
Pages (from-to)507-518
Number of pages12
JournalCommunications for Statistical Applications and Methods
Volume24
Issue number5
DOIs
StatePublished - 1 Sep 2017

Keywords

  • ARFIMA
  • Bayesian
  • GARCH
  • JAGS
  • Markov chain Monte Carlo
  • Skewed-t

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