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

Rosy Oh; Dong Wan Shin; Man Suk Oh

doi:10.5351/CSAM.2017.24.5.507

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

Rosy Oh, Dong Wan Shin, Man Suk Oh

Department of Statistics

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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 language	English
Pages (from-to)	507-518
Number of pages	12
Journal	Communications for Statistical Applications and Methods
Volume	24
Issue number	5
DOIs	https://doi.org/10.5351/CSAM.2017.24.5.507
State	Published - 1 Sep 2017

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A2 B4008914) (Rosy Oh and Man-Suk Oh) and (2016R1A2B4008780) (Dong Wan Shin).

Publisher Copyright:
© 2017 The Korean Statistical Society, and Korean International Statistical Society.

Keywords

ARFIMA
Bayesian
GARCH
JAGS
Markov chain Monte Carlo
Skewed-t

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10.5351/CSAM.2017.24.5.507

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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.",

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AB - 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.

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

Abstract

Bibliographical note

Keywords

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