Ranking the predictive performances of value-at-risk estimation methods

Measuring the market risk of freight rates: A forecast combination approach

“…First, we examine in detail the statistical accuracy of each method by applying a battery of backtesting criteria. Secondly, we evaluate the forecasting performance by utilizing the newly proposed methodology of Sener et al () in order to rank the VaR methods. These steps are considered independently, while the final results are produced by ranking only the statistically accepted methods.…”

“…In order to conduct a more reliable and in‐depth evaluation, we implement a battery of newly developed backtesting criteria, namely Engle and Manganelli's () quantile regression approach, Christoffersen and Pelletier's () duration approach, and Colletaz, Hurlin, and Perignon's (2013), test along with the standard Christoffersen () Christoffersen (1998) approach. More importantly, we complement statistical evaluation with the performance evaluation methodology proposed by Sener, Baronyan, and Menguturk () in order to rank the implemented methods.…”

The new hybrid value at risk approach based on the extreme value theory

“…They glorify the EVT approach for dealing with fat tails and extreme returns, which are otherwise typical for the emerging markets. For that reason, ener, Baronyana and Mengütürk (2012) advocate that Conditional Autoregressive Value at Risk by regression quantiles (CAVaiR), proposed by Engle and Manganelli (2004), should be used combined with the EVT approach, but Louzis, Xanthopoulos-Sisinis and Refenes (2014) suggest that Asymmetric Heterogeneous Autoregressive (Asym. HAR) model, proposed by Louzis, Xanthopoulos-Sisinis and Refenes (2012) and Corsi and Reno (2012), should be used together with the EVT approach.…”

Hull-White’s Value at Risk Model: Case Study of Baltic Equities Market

“…The common feature of these models is that they do not behave as it would be expected based on the procedures behind the models (see Stancic et al 2013); they are computationally very intensive because they require a relatively large number of parameters that cannot be solved in a closed, analytical form and can result in negative values, where both problems have a negative influence on the maximum estimated likelihood. On the other hand, the numerous empirical researches (Zikovic 2007;Diamandis et al 2011;Şener et al 2012;Rossignolo et al 2012Rossignolo et al , 2013Cui et al 2013;Louzis et al 2014;Del Brio et al 2014) show that filtered historical simulation which was proposed by Hull and White (further marked as FHS) performs better than the commonly used and the most popular VaR models at both markets (developed and emerging) in the context of meeting the backtesting rules of the Basel Committee. Theoretically, the FHS model represents improvement of the HS model, concerning the ability to capture time-varying volatility (conditional heteroscedasticity), without a significant increase in computational complexity.…”