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

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 t$$ t $$ 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 t$$ t $$ 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.