Value-at-Risk Prediction in R with the GAS Package

TL;DR

This paper demonstrates how financial risk managers can use the GAS package in R for effective Value-at-Risk prediction, including practical implementation and empirical evaluation on Dow Jones Index data.

Contribution

It introduces the GAS package for R tailored for VaR prediction and provides detailed guidance and empirical analysis for its application in finance.

Findings

GAS models effectively predict VaR for Dow Jones Index constituents.

The GAS package facilitates prediction, comparison, and backtesting of VaR models.

Empirical results show competitive forecasting performance.

Abstract

GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and backtesting with GAS models are presented. An empirical application considering Dow Jones Index constituents investigates the VaR forecasting performance of GAS models.