Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution
- 주제(키워드) ARFIMA , Bayesian , GARCH , JAGS , Markov chain Monte Carlo , Skewed-t
- 후원정보 Ministry of Education, Science and Technology
- 등재 SCOPUS, KCI등재
- 발행기관 Korean Statistical Society
- 발행년도 2017
- URI http://www.dcollection.net/handler/ewha/000000155651
- 본문언어 영어
- Published As http://dx.doi.org/10.5351/CSAM.2017.24.5.507
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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. © 2017 The Korean Statistical Society, and Korean International Statistical Society.
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