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Keywords = extreme downside hedge

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17 pages, 2582 KB  
Article
Risk Dependence and Risk Spillovers Effect from Crude Oil on the Chinese Stock Market and Gold Market: Implications on Portfolio Management
by Bin Mo, Juan Meng and Guannan Wang
Energies 2023, 16(5), 2141; https://doi.org/10.3390/en16052141 - 22 Feb 2023
Cited by 8 | Viewed by 3006
Abstract
We analyze crude oil’s dependence and the risk spillover effect on the Chinese stock market and the gold market. We compare both static and dynamic copula functions and calculate the average upward and downward spillover effect using the time-varying Copula model and the [...] Read more.
We analyze crude oil’s dependence and the risk spillover effect on the Chinese stock market and the gold market. We compare both static and dynamic copula functions and calculate the average upward and downward spillover effect using the time-varying Copula model and the conditional value-at-risk approach. By utilizing daily data on crude oil prices, China’s stock market, and the gold market, we observe an asymmetric spillover effect: the downside spillover effects from crude oil prices on the Chinese stock market and gold market are larger than the upside spillover effect. We then identify changes in the structure of the sample periods and calculate the dynamic conditional correlation between them. In addition, we explore the optimal weight and hedge ratios in diversified portfolios to mitigate potential risks. Our results suggest that investors and portfolio managers should frequently adjust their portfolio strategies, particularly during extreme events like COVID-19, when financial assets become more volatile. Furthermore, crude oil can help reduce the risk in the Chinese stock market and gold market to some extent during different sub-periods. Full article
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10 pages, 1077 KB  
Article
Statistical Modelling of Downside Risk Spillovers
by Daniel Felix Ahelegbey
FinTech 2022, 1(2), 125-134; https://doi.org/10.3390/fintech1020009 - 1 Apr 2022
Viewed by 2739
Abstract
We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application [...] Read more.
We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application examines whether downside risk connections among the major stock markets are merely anecdotal or provide a signal of contagion and the nature of sensitivity among major equity markets during the global financial crisis and the coronavirus pandemic. The result showed that the COVID-19 crisis recorded the historically highest spike in the downside risk interconnectedness among the major equity market indices, suggesting higher financial market vulnerability in the coronavirus pandemic than during the global financial crisis. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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17 pages, 613 KB  
Article
Tail Risk Transmission: A Study of the Iran Food Industry
by Fatemeh Mojtahedi, Seyed Mojtaba Mojaverian, Daniel F. Ahelegbey and Paolo Giudici
Risks 2020, 8(3), 78; https://doi.org/10.3390/risks8030078 - 20 Jul 2020
Cited by 2 | Viewed by 4284
Abstract
This paper extends the extreme downside correlation (EDC) and extreme downside hedge (EDH) methodology to model the interdependence in the sensitivity of assets to the downside risk of other financial assets under severe firm-level and market conditions. The model is applied to analyze [...] Read more.
This paper extends the extreme downside correlation (EDC) and extreme downside hedge (EDH) methodology to model the interdependence in the sensitivity of assets to the downside risk of other financial assets under severe firm-level and market conditions. The model is applied to analyze both systematic and systemic exposures in the Iranian Food Industry. The empirical application investigates (1) which company is the safest for investors to diversify their investment, and (2) which companies are the “transmitters” and “receivers” of downside risk. We study the return series of 11 companies and the Food Industry index publicly listed on the Tehran Stock Exchange. The data covers daily close prices from 2015–2020. The result shows that Mahram Manufacturing is the safest to hedge equity risk, and Glucosan and Behshahr Industries are the riskiest, while Gorji Biscuit is central to risk transmission, and Pegah Fars Diary is the main “receiver” of risk in turbulent times. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)
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