Nonparametric Estimation of Range Value at Risk

Biswas, Suparna; Sen, Rituparna

doi:10.3390/computation11020028

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…Thus, RVaR 2.5%, 5% ≤ RVaR 1%, 5% ≤ RVaR 1%, 2.5% . Biswas and Sen (2023) and Müller et al (2022) found similar result. This result is in line with the expected; that is, lower significance levels are associated with greater protection.…”

Section: Univariate Resultssupporting

confidence: 65%

“…Notice that improvements from univariate to multivariate models were reported, for instance, in Santos et al (2013) and Wang and Wu (2012). 3 Biswas and Sen (2023) is also closely related to the present paper since they performed a comparison of nonparametric estimators for the RVaR. However, unlike our study, Biswas and Sen (2023) examine estimators using Monte Carlo simulations, and they perform a backtesting exercise of RVaR forecasts inspired in the Acerbi and Szekely's test (Acerbi & Szekely, 2014).…”

Section: Introductionmentioning

confidence: 89%

“…3 Biswas and Sen (2023) is also closely related to the present paper since they performed a comparison of nonparametric estimators for the RVaR. However, unlike our study, Biswas and Sen (2023) examine estimators using Monte Carlo simulations, and they perform a backtesting exercise of RVaR forecasts inspired in the Acerbi and Szekely's test (Acerbi & Szekely, 2014). In addition, although it is not the focus of the study, we also extended the analysis to VaR and ES.…”

Section: Introductionmentioning

confidence: 90%

“…Among alternatives for VaR and ES, the RVaR stands out because it better suits practical purposes while maintaining ES's essence. Such advantage has motivated recent theoretical (Fissler & Ziegel, 2021; Righi & Müller, 2022) and empirical (Biswas & Sen, 2023; Müller et al, 2022) quests. However, when comparing procedures to forecast RVaR, the empirical literature is still incipient, even though RVaR was proposed more than a decade ago.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of Range Value at Risk (RVaR) forecasting models

Maria Müller,

Weber Gössling,

Solgon Santos

et al. 2023

Journal of Forecasting

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Risk forecasting is an important and helpful process for investors, fund managers, traders, and market makers. Choosing an inappropriate risk forecasting model can trigger irreversible losses. In this context, this study aims to evaluate the quality of different models to forecast the Range Value at Risk (RVaR) in univariate and multivariate analyses. The forecasts for other important measures like Value at Risk (VaR) and Expected Shortfall (ES) are also obtained. To assess the performance of both the univariate and multivariate models to RVaR forecasting, we consider an empirical exercise with different asset classes, rolling window estimations, and significance levels. We evaluated the empirical forecasts with the score functions of each risk measure. We identified that different models forecast different assets better, and the GARCH model with Student's and skewed Generalized Error distribution overcame the other distributions. We observed the RVine and CVine copulas as better models in the multivariate study. Besides, we noted that the models with Student's marginal distribution perform better according to realized loss (score function). We also note that RVaR forecasts follow the evolution of financial returns, showing an interesting measure to be used in industry and empirical investigations.

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Section: Univariate Resultssupporting

confidence: 65%