The 14 references without contexts in paper Liyuan Chen, Paola Zerilli, Christopher F Baum (2018) “Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications” / RePEc:boc:bocoec:953

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Basel Committee on Banking Supervision. 1996b. Supervisory framework for the use of backtesting in conjunction with the internal models approach to market risk capital requirements. Publication No. 22, Bank for International Settlements, Basel.
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GED stochastic volatility model.Studies in Nonlinear Dynamics&Econometrics, 8(2).
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Chai, J., Guo, Ju-e., Gong L. and Wang S. Y. (2011). Estimating crude oil price ’Value at Risk’ using the Bayesian-SV-SGT approach.Systems Engineering-Theory&Practice, 31(1).
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Chan, J. C. and Grant, A. L. (2016b). On the observed-data deviance information criterion for volatility modeling.Journal of Financial Econometrics, 14(4), 772-802.
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Chan,J. C. and Hsiao,C. Y.-L. (2013).Estimation of Stochastic Solatility
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ModelswithHeavyTailsandSerialDependence.[Online].Availableat https://papers.ssrn.com/sol3/papers.cfm?abstractid=2359838[Accessed27October 2017].
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Fan, Y., Zhang, Y. -J., Tsai, H. -T. and Wei, Y. -M. (2008). Estimating Value at Riskof crude oil price and its spillover effect using the GED-GARCH approach.Energy Economics, 30(6), 3156-3171.
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Hung, J.-C., Lee, M.-C. and Liu, H.-C. (2008). Estimation of value-at-risk for energy commodities via fat-tailed GARCH models.Energy Economics, 30(3), 1173-1191.
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Koopman, S. J. and Hol Uspensky, E. (2002). The stochastic volatility in mean model: empirical evidence from international stock markets.Journal of Applied Econometrics, 17(6), 667-689.
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The Journal of Derivatives, 3(2), 73-84.
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Zhao, S., Lu, Q., Han, L., Liu, Y. and Hu, F. (2): ∫∞ 0 √ 1 +κ4 1 +κ2 1 σt I(λ > √ 1 +κ4(εt−θ) σt )exp(−λ)dλ − √ 1 +κ4 1 +κ2 1 σt ∫∞ √ 1+κ4(εt−θ) σt exp(−λ)d(−λ) = √ 1 +κ4 1 +κ2 1 σt exp( − √ 1 +κ4(εt−θ) σt ) (29) = Since √ 1+κ4(εt−θ) σt≥0, thus we haveεt≥0, which follows: f+(εt|κ,θ,σt) = √ 1 +κ4 1 +κ2 1 σt exp( − √ 1 +κ4(εt−θ)
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