2015•Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities researchOpen access

Research on Portfolio Risk Prediction Based on Copula-GJR-Skewt Model

Xiangqing Wei

Open full text 0 citations

Abstract

For risk prediction of diversified investment portfolio, we use the thick tail and the biased characteristics of GJR-Skewt model to depict a single asset and using Copula model to depict a diversified investment portfolio non-linear correlation structure, simulating the random distribution of financial assets with Monte Carlo method and combining with rolling time window method to conduct the sample dynamic forecast for the future portfolio risk.The empirical results show that Copula-GJR-Skewt model can achieve satisfactory results of predicting the risk of asset returns.For the VaR forecast performance, we use the GJR-Skewt model as the edge distribution functions and even if there is a system error, it can also achieve optimal prediction.

Open-access reader

About this research paper

What this paper is about

For risk prediction of diversified investment portfolio, we use the thick tail and the biased characteristics of GJR-Skewt model to depict a single asset and using Copula model to depict a diversified investment portfolio non-linear correlation structure, simulating the random distribution of financial assets with Monte Carlo method and combining with rolling time window method to conduct the sample dynamic forecast for the future portfolio risk.The empirical results show that Copula-GJR-Skewt model can achieve satisfactory results of predicting the risk of asset returns.For the VaR forecast performance, we use the GJR-Skewt model as the edge distribution functions and even if there is a system error, it can also achieve optimal prediction.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

For risk prediction of diversified investment portfolio, we use the thick tail and the biased characteristics of GJR-Skewt model to depict a single asset and using Copula model to depict a diversified investment portfolio non-linear correlation structure, simulating the random distribution of financial assets with Monte Carlo method and combining with rolling time window method to conduct the sample dynamic forecast for the future portfolio risk.The empirical results show that Copula-GJR-Skewt model can achieve satisfactory results of predicting the risk of asset returns.For the VaR forecast performance, we use the GJR-Skewt model as the edge distribution functions and even if there is a system error, it can also achieve optimal prediction.

Key concepts: Copula (linguistics), Portfolio, Econometrics, Computer science, Portfolio optimization, Monte Carlo method, Economics, Mathematics

Related papers

Back to paper searchBrowse research topicsOriginal source
Research on Portfolio Risk Prediction Based on Copula-GJR-Skewt Model — Research Paper | ScholarLens