Value-at-Risk forecasting model based on normal inverse Gaussian distribution driven by dynamic conditional score

TL;DR

This paper introduces a novel VaR forecasting model using the normal inverse Gaussian distribution within a dynamic conditional score framework, effectively incorporating intraday data to improve risk prediction accuracy.

Contribution

It presents a new parametric VaR model that integrates intraday information and leverages NIG distribution properties for enhanced risk forecasting.

Findings

NIG-DCS-VaR outperforms RGARCH models in Chinese stock market data.

The model shows superior coverage and independence at high risk levels.

Empirical results validate the effectiveness of the proposed approach.

Abstract

Under the framework of dynamic conditional score, we propose a parametric forecasting model for Value-at-Risk based on the normal inverse Gaussian distribution (Hereinafter NIG-DCS-VaR), which creatively incorporates intraday information into daily VaR forecast. NIG specifies an appropriate distribution to return and the semi-additivity of the NIG parameters makes it feasible to improve the estimation of daily return in light of intraday return, and thus the VaR can be explicitly obtained by calculating the quantile of the re-estimated distribution of daily return. We conducted an empirical analysis using two main indexes of the Chinese stock market, and a variety of backtesting approaches as well as the model confidence set approach prove that the VaR forecasts of NIG-DCS model generally gain an advantage over those of realized GARCH (RGARCH) models. Especially when the risk level is relatively high, NIG-DCS-VaR beats RGARCH-VaR in terms of coverage ability and independence.