Selection of Value-at-Risk models

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Estimation of value-at-risk for energy commodities via fat-tailed GARCH models (2008, background): “Many empirical studies on VaR, such as van den Goorbergh and Vlaar (1999) and Giot (2000) have shown that models with a normal distribution usually do not fully consider the fat tails and high peaks of the returns distribution.”
Comparison of Volatility Measures: A Risk Management Perspective (2010, unknown): “Such an approach follows the lines of the methodology proposed by Sarma et al. (2003).”
Forecasting Stock Market Volatility with Regime-Switching GARCH Models (2005, methodology): “V aRkt }, where I is the usual indicator function and δ is the opportunity cost of capital.14 To assess the superiority of one model to another, Sarma et al. (2003) adopt the same one-sided sign test as 14In what follows, we assume that the opportunity cost of capital can be linked to the risk-free…”
Frontiers in VaR forecasting and backtesting (2016, background): “Sarma et al. (2003) and Angelidis and Degiannakis (2007) use the Regulatory Loss function (RLF) in which f(Rt, V aR (m) t ) = ( Rt V aR(m)t )2 and g ( Rt, V aR (m) t ) = 0.”