Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood

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Subfields: Finance

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Evaluating Volatility and Correlation Forecasts (2009, background): “VaR-based evaluation and comparison of volatility forecast has been examined in [44, 32, 26, 43], amongst others.”
Frontiers in VaR forecasting and backtesting (2016, methodology): “Some applications of SV models to the estimation of VaR can be found in Lehar et al. (2002), Fleming and Kirby (2003), Gónzalez-Rivera et al. (2004) and Chen, Gerlach, Lin and Lee (2012).”
A comprehensive review of Value at Risk methodologies (2014, methodology): “However, in a similar comparison, González-Rivera et al. (2004) found that the SV model had the best performance in estimating VaR.”
Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018, unknown): “, K ), we obtain the Markov-switching GARCH(1, 1) model presented by Haas et al. (2004).5 In this case, θk ≡ (ωk, αk, βk)′.”