A Method for Predicting VaR by Aggregating Generalized Distributions Driven by the Dynamic Conditional Score

TL;DR

This paper introduces a novel dynamic conditional score-based model using generalized distributions and high-frequency data to improve daily VaR predictions in financial markets.

Contribution

It presents a new GD-DCS model that effectively incorporates intraday data and dynamic parameters for enhanced VaR forecasting accuracy.

Findings

The Weibull-Pareto-DCS model outperforms traditional models like RGARCH.

High-frequency data integration improves VaR prediction at high risk levels.

Empirical results from Chinese stock market data validate the model's effectiveness.

Abstract

Constructing a more effective value at risk (VaR) prediction model has long been a goal in financial risk management. In this paper, we propose a novel parametric approach and provide a standard paradigm to demonstrate the modeling. We establish a dynamic conditional score (DCS) model based on high-frequency data and a generalized distribution (GD), namely, the GD-DCS model, to improve the forecasts of daily VaR. The model assumes that intraday returns at different moments are independent of each other and obey the same kind of GD, whose dynamic parameters are driven by DCS. By predicting the motion law of the time-varying parameters, the conditional distribution of intraday returns is determined; then, the bootstrap method is used to simulate daily returns. An empirical analysis using data from the Chinese stock market shows that Weibull-Pareto -DCS model incorporating high-frequency data is superior to traditional benchmark models, such as RGARCH, in the prediction of VaR at high risk levels, which proves that this approach contributes to the improvement of risk measurement tools.