The dynamic quantile approach for VaR estimation: empirical evidence from Indonesia banking industry

Abstract

This study estimates value-at-risk (VaR) to measure foreign exchange risk in Indonesia’s banking industry using quantile regression (QR) approach. Four large banks whose capital and assets were the biggest were observed, and their selection was based on their market share in the industry. To compute VaR, data on each bank’s day-to-day gain/loss between 1 January 2016 and 9 February 2021 were examined, and they involved records of all transactions involving six foreign currencies. According to results of a backtesting analysis using dynamic quantile (DQ) test at a 95% confidence level performed in this study, VaR estimation of each bank’s gain/loss on foreign currency transactions generated using QR regression approach demonstrates an excellent predictive performance, with an average of 95% accuracy level. The contribution of this article lies in the development of an internal model that produces better risk mea­surements, a model that has not yet gained widespread usage in empirical research within the context of Indonesia.