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

Title
Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution
Authors
Oh R.Shin D.W.Oh M.-S.
Ewha Authors
오만숙신동완
SCOPUS Author ID
오만숙scopus; 신동완scopus
Issue Date
2017
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843JCR Link
Citation
Communications for Statistical Applications and Methods vol. 24, no. 5, pp. 507 - 518
Keywords
ARFIMABayesianGARCHJAGSMarkov chain Monte CarloSkewed-t
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI scopus
Document Type
Article
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. © 2017 The Korean Statistical Society, and Korean International Statistical Society.
DOI
10.5351/CSAM.2017.24.5.507
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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