Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution
분야
자연과학 > 통계학
저자
( Rosy Oh ) , ( Dong Wan Shin ) , ( Man-suk Oh )
발행기관
한국통계학회
간행물정보
CSAM(Communications for Statistical Applications and Methods) 2017년, 제24권 제5호, 507~518쪽(총12쪽)
파일형식
02707089.pdf [무료 PDF 뷰어 다운로드]
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    영문초록
    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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