Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions

Jianxu Liu,Quanrui Song,Yang Qi,Sanzidur Rahman,Songsak Sriboonchitta

doi:10.3390/su12104000

Abstract

The global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct a trade decision model for investors and financial institutions to assist them in forecasting risk and potential returns based on the results of the analysis of systemic risk. The factor copula-generalized autoregressive conditional heteroskedasticity (GARCH) models and component expected shortfall (CES) were combined for the first time in this study to measure systemic risk and the contribution of individual countries to global systemic risk in global financial markets. The use of factor copula-based models enabled the estimation of joint models in stages, thereby considerably reducing computational burden. A high-dimensional dataset of daily stock market indices of 43 countries covering the period 2003 to 2019 was used to represent global financial markets. The CES portfolios developed in this study, based on the forecasting results of systemic risk, not only allow spreading of systemic risk but may also enable investors and financial institutions to make profits. The main policy implication of our study is that forecasting systemic risk of global financial markets and developing portfolios can provide valuable insights for financial institutions and policy makers to diversify portfolios and spread risk for future investments and trade.

Highlights

With the development of the economy and the flourishing of the stock market, stock indexes have been identified as important social and economic indicators that can comprehensively reflect the overall trends and performance of the stock market
The advantages of factor copula-based models are that any high-dimensional data are easier to fit by reducing the number of parameters, and that the dependence and tail dependence can be measured between stock returns and latent variables, which is why the factor copula model of Krupskii and Joe is superior compared to other factor copulas
The main objectives of this study were to measure and forecast systemic risk in the global financial markets and construct a trade decision model based on the results of component expected shortfall (CES) predictions for investors and financial institutions to assist them in forecasting risk and potential returns

Summary

Introduction

With the development of the economy and the flourishing of the stock market, stock indexes have been identified as important social and economic indicators that can comprehensively reflect the overall trends and performance of the stock market. Based on the sheer size and breadth, global stock markets play a decisive role in global financial performance. Along with the increasingly closer economic ties amongst all countries, rapid capital flows are not rare in modern global stock markets, allowing for fast and frequent transactions, resulting in markets that tend to have higher dependencies on each other. Economic globalization has created accessibility and convenience for investors, managers, and relevant officers in financial markets, with the drawback being the acceleration of the risk contagions. Global stock markets play a decisive role in global financial performance. Along with increasingly closer economic ties amongst all countries, rapid capital flows are not rare in modern global stock markets, which enable fast and frequent transactions, resulting in markets having higher dependencies amongst themselves. The financial crisis of 2008 is considered as the worst financial crisis since the Great

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