Generalized Autoregressive Score asymmetric Laplace Distribution and Extreme Downward Risk Prediction

TL;DR

This paper introduces a GAS-ALD model that effectively captures the skewed, heavy-tailed, and asymmetric distribution of financial returns, improving risk prediction for stock indices.

Contribution

The paper develops a novel GAS-ALD model with time-varying parameters to better describe asymmetric, heavy-tailed financial return distributions.

Findings

GAS-ALD captures time-varying distribution characteristics of indices.

GAS-ALD outperforms traditional models in VaR and ES prediction.

Model effectively describes skewness and tail behavior of financial data.

Abstract

Due to the skessed distribution, high peak and thick tail and asymmetry of financial return data, it is difficult to describe the traditional distribution. In recent years, generalized autoregressive score (GAS) has been used in many fields and achieved good results. In this paper, under the framework of generalized autoregressive score (GAS), the asymmetric Laplace distribution (ALD) is improved, and the GAS-ALD model is proposed, which has the characteristics of time-varying parameters, can describe the peak thick tail, biased and asymmetric distribution. The model is used to study the Shanghai index, Shenzhen index and SME board index. It is found that: 1) the distribution parameters and moments of the three indexes have obvious time-varying characteristics and aggregation characteristics. 2) Compared with the commonly used models for calculating VaR and ES, the GAS-ALD model has a high prediction effect.