Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests

Robust Backtesting Tests for Value-at-risk Models

Backtesting methods are statistical tests designed to uncover excessive risk-taking from financial institutions. We show in this paper that these methods are subject to the presence of model risk produced by a wrong specification of the conditional VaR model, and derive its effect on the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation ...

Dynamic Value-at-Risk

Dynamic selection and combination of conditional quantile forecasts, with application to value-at-risk modeling

We introduce an effective and computationally fast approach to combine conditional quantile forecasts. The approach uses the information of the relevant loss function for the quantile problem associated to each candidate model in order to define forecast combination weights in a dynamic fashion. Two important advantages of the proposed method are that i) does not require numerical optimization ...

Backtesting Parametric Value-at-Risk with Estimation Risk

One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Since then, the capital requirements of commercial banks with trading activities are based on VaR estimates. Therefore, appropriately constructed tests for assessing the out-of-sample forecast accuracy of the VaR model (b...

Backtesting Portfolio Value-at-Risk with Estimation Risk

Nowadays the most extensively used risk measure by financial institution is known as Value-at-Risk (VaR), which is defined as the maximum expected loss on an investment over a specified horizon at a given confidence level. To evaluate the accuracy and quality of the out-of-sample VaR forecast (backtesting procedures) is an important issue in practice. The purpose of this paper is to quantify th...