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Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods
Open AccessArticle

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

by 1,2,†, 2,*,†, 1,†, 1,3,† and 2,†
1
Faculty of Economics, Shandong University of Finance and Economics, Jinan 250000, China
2
Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand
3
Plymouth Business School, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2020, 12(10), 4000; https://doi.org/10.3390/su12104000
Received: 22 March 2020 / Revised: 9 May 2020 / Accepted: 9 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Forecasting Financial Markets and Financial Crisis)
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. View Full-Text
Keywords: stock markets; factor copula; dependence; forecasting risk; financial crisis stock markets; factor copula; dependence; forecasting risk; financial crisis
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MDPI and ACS Style

Liu, J.; Song, Q.; Qi, Y.; Rahman, S.; Sriboonchitta, S. Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions. Sustainability 2020, 12, 4000. https://doi.org/10.3390/su12104000

AMA Style

Liu J, Song Q, Qi Y, Rahman S, Sriboonchitta S. Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions. Sustainability. 2020; 12(10):4000. https://doi.org/10.3390/su12104000

Chicago/Turabian Style

Liu, Jianxu; Song, Quanrui; Qi, Yang; Rahman, Sanzidur; Sriboonchitta, Songsak. 2020. "Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions" Sustainability 12, no. 10: 4000. https://doi.org/10.3390/su12104000

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