The 26 linked references 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

  1. Abanto-Valle, C. A., Bandyopadhyay, D., Lachos, V.H. and Enriquez, I. (2010). Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions.Computational Statistics&Data Analysis, 54(12), 2883-2898.
  2. Aloui, C. and Mabrouk, S. (2010). Value-at-Risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models.Energy Policy, 38(5), 2326-2339.
  3. Artzner, P., Delbaen, F., Eber, J. and Heath, D. (1999). Coherent measures of risk.
  4. Breidt, F. J., Crato, N. and De Lima, P. (1998). The detection and estimation of long memory in stochastic volatility.Journal of Econometrics, 83(1), 325-348.
  5. Cappuccio, N., Lubian, D. and Raggi, D. (2004). MCMC Bayesian estimation of a skew-
  6. GED stochastic volatility model.Studies in Nonlinear Dynamics&Econometrics, 8(2).
  7. Chan, J. C. (2013). Moving average stochastic volatility models with application to inflation forecast.Journal of Econometrics, 176(2), 162-172.
  8. Chan, J. C. (2017). The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling.Journal of Business&Economic Statistics, 35(1), 17-28.
  9. Chen, C. W., Gerlach, R. and Wei, D. (2009). Bayesian causal effects in quantiles: Accounting for heteroscedasticity.Computational Statistics&Data Analysis, 53(6), 19932007.
  10. Chen, Q., Gerlach, R. and Lu, Z. (2012). Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution.Computational Statistics&Data Analysis, 56(11), 3498-3516.
  11. Chib, S., Nardari, F. and Shephard, N. (2002). Markov chain Monte Carlo methods for stochastic volatility models.Journal of Econometrics, 108(2), 281-316.
  12. Christoffersen, P. F. (1998). Evaluating interval forecasts.International Economic Review, 841-862.
  13. Diebold, F. X. and Mariano, R. S. (1995). Comparing predictive accuracy.Journal of Business and Economic Statistics, 13, 253-263.
  14. 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.
  15. Kristoufek, L. (2014). Leverage effect in energy futures.Energy Economics, 45, 1-9.
  16. Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models.
  17. Lardic, S. and Mignon, V. (2008). Oil prices and economic activity: An asymmetric cointegration approach.Energy Economics, 30(3), 847-855.
  18. Lopez, J. A. (1999). Methods for evaluating Value-at-Risk estimates.Federal Reserve Bank of San Francisco Economic Review, 2, 3-17.
  19. Louzis, D. P., Xanthopoulos-Sisinis, S. and Refenes, A. P. (2014). Realized volatility models and alternative Value-at-Risk prediction strategies.Economic Modelling, 40, 101-116.
  20. Marimoutou, V., Raggad, B. and Trabelsi, A. (2009). Extreme value theory and value at risk: application to oil market.Energy Economics, 31(4), 519-530.
  21. Papapetrou, E. (2001). Oil price shocks, stock market, economic activity and employment in Greece.Energy Economics, 23(5), 511-532.
  22. Sarma, M., Thomas, S. and Shah, A. (2003). Selection of Value-at-Risk models.Journal of Forecasting, 22, 337-358.
  23. So, M. E. P., Lam, K. and Li, W. K. (1998). A stochastic volatility model with Markov switching.Journal of Business&Economic Statistics, 16(2), 244-253.
  24. Takahashi, M., Omori, Y. and Watanabe, T. (2009). Estimating stochastic volatility models using daily returns and realized volatility simultaneously.Computational Statistics&Data Analysis, 53(6), 2404-2426.
  25. Wichitaksorn, N., Wang, J. J., Boris Choy, S. T. and Gerlach, R. (2015). Analyzing return asymmetry and quantiles through stochastic volatility models using asymmetric Laplace error via uniform scale mixtures.Applied Stochastic Models in Business and Industry, 31(5), 584-608.
  26. Yu, J. and Yang, Z. (2002). A class of nonlinear stochastic volatility models. Univ. of