Artzner, P., Delbaen, F., Eber, J. M. and Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3), pp. 203- 228
Baffes, J. and Haniotis, T. (2010). Placing the recent commodity boom into perspective. Food prices and rural poverty, pp. 40-70.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), pp. 307-327.
Cai, J. (1994). A Markov model of unconditional variance in ARCH. Journal of Business and Economic Statistics, 12(3), pp. 309-316.
Chang, K. L. (2012). Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market. Energy Economics, 34(1), pp. 294-306.
Cheong, C. W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy policy, 37(6), pp. 2346-2355.
Chiarucci, R., Loffredo, M. I. and Ruzzenenti, F. (2017). Evidences for a structural change in the oil market before a financial crisis: the flat horizon effect. Research in International Business and Finance, 42,
pp. 912-921.
Chkili, W., Hammoudeh, S. and Nguyen, D. K. (2014). Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Energy Economics, 41, pp.1-18.
Crisostomo, R. (2015). An analysis of the Heston stochastic volatility model: Implementation and calibration using MATLAB. arXiv Preprint. https://arxiv.org/abs/02963/1502.
Di Sanzo, S. (2018). A Markov switching long memory model of crude oil price return volatility. Energy Economics, 74, pp. 351-359.
Elliott, R. J., Hunter, W. C. and Jamieson, B. M. (1998). Drift and volatility estimation in discrete time. Journal of Economic Dynamics and Control, 22(2), pp. 209-218.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, pp. 987-1007.
Fan, Y., Zhang, Y. J., Tsai, H. T. and Wei, Y. M. (2008). Estimating ‘Value at Risk’of crude oil price and its spillover effect using the GED-GARCH approach. Energy Economics, 30(6), pp. 3156-3171.
Glosten LR, Jaganathan R, Runkle DE (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48(5), pp. 1779-1801.
Gray, S. F. (1996). Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics, 42(1),
pp. 27-62.
Hamilton, J. D. and Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of econometrics, 64(1-2), pp.307-333.
Herrera, A. M., Hu, L. and Pastor, D. (2018). Forecasting crude oil price volatility. International Journal of Forecasting, 34(4), pp.622-635.
Hull, J. and White, A. (1987). The pricing of options on assets with stochastic volatilities. Journal of Finance, 42, pp.281-300.
Iglesias, E. M. and Rivera-Alonso, D. (2022). Brent and WTI oil prices volatility during major crises and Covid-19. Journal of Petroleum Science and Engineering, 110182.
Kang, S. H. and Yoon, S. M. (2013). Modeling and forecasting the volatility of petroleum futures prices. Energy Economics, 36, pp.354-362.
Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH (pp. 223-254). Physica-Verlag HD.
Kristoufek, L. (2014). Leverage effect in energy futures. Energy Economics, 45, pp. 1-9.
Lin, Y., Xiao, Y. and Li, F. (2020). Forecasting crude oil price volatility via a HM- model. Energy Economics, 87, 104693.
Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics and Econometrics, 9(4).
Mohammadi, H. and Su, L. (2010). International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models. Energy Economics, 32(5), pp. 1001-1008.
Nakajima, J. (2009). Bayesian analysis of GARCH and stochastic volatility: Modeling leverage, jumps and heavy-tails for financial time series [Technical report Mimeo]. Department of Statistical Science, Duke University.
Nelson, D.B. (1991). Conditional Heteroscedasticity in Asset Returns: A New Approach, Econometrica, 59, pp. 347-370.
Nelson, D.B. and Foster, D.P. (1994). Asymptotic Filtering Theory for Univariate ARCH Models, Econometrica, 62, pp.1-41.
Nomikos, N. and Andriosopoulos, K. (2012). Modelling energy spot prices: Empirical evidence from NYMEX. Energy Economics, 34(4), pp.1153-1169.
Poon, S. H. (2005). A practical guide to forecasting financial market volatility. John Wiley and Sons.
Rabiner, L., & Juang, B. (1986). An introduction to hidden Markov models. ieee assp magazine, 3(1), 4-16.
Rossi, A. and Gallo, G. M. (2006). Volatility estimation via hidden Markov models. Journal of Empirical Finance, 13(2), pp. 203-230.
Salisu, A. A. and Fasanya, I. O. (2013). Modelling oil price volatility with structural breaks. Energy policy, 52, pp.554-562.
Sari, R., Hammoudeh, S., Chang, C. L. and McAleer, M. (2012). Causality between market liquidity and depth for energy and grains. Energy Economics, 34(5), pp.1683-1692.
Tsay, R. S. (2014). An introduction to analysis of financial data with R. John Wiley and Sons.2nd edition.
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), pp. 931-955.
)