High-dimensional CoVaR risk spillover network from oil market to global stock markets—Lessons from the Kyoto Protocol

Sheng, Jiliang; Li, Juchao; Yang, Jun; Wang, Yufan; Li, Jiayu

doi:10.3389/fenvs.2023.1103625

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…(Sathyanarayana, 2018) using linear regression shows that changes in crude oil prices have an impact on Sensex. (Sheng et. al, 2023) reveal positive correlations between the crude oil market and stock markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Market Sensitivity to Crude Oil and Forex Price Movements-a Study on Nifty 50 Index

Sandeep Kumar Patra,

Prof. Prabodha Kumar Hota

2024

epra

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This study examines the impact of the USD/INR Exchange rate, and the per-barrel prices of WTI Crude oil in rupees on the Nifty 50 index, utilizing monthly data spanning from January 1, 2010, to September 30, 2023. The analysis primarily involves the utilization of the closing returns of the nifty index, the West Texas Intermediate (WTI) oil price per barrel in dollars, and exchange rates. The classical OLS method is sued to analyse the relationship between Nifty, crude oil, and crude oil prices over 166 months. The results reveal that exchange rates have a negative coefficient, suggesting that an increase in exchange rates is associated with a decrease in Nifty50 returns. In addition, the CUSUM test confirms the stability and consistency of our model over time, supporting the absence of structural breaks. This study underscores the dynamic nature of financial markets and highlights the importance of continuous monitoring and risk management strategies. KEY WORDS: Nifty 50, Crude Oil, Exchange Rate

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Section: Literature Reviewmentioning

confidence: 99%

Stock Market Sensitivity to Crude Oil and Forex Price Movements-a Study on Nifty 50 Index

Sandeep Kumar Patra,

Prof. Prabodha Kumar Hota

2024

epra

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“…G-E CoVaR mainly relies on the Copula model for estimation. The Copula model can effectively capture the non-linear and asymmetric characteristics of the tail dependence structure between financial markets [14]. In contrast, although linear quantile regression can consider state variables when measuring CoVaR, more is needed to capture the nonlinear tail correlation between financial markets fully.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Systemic Risk Based on the QRDCCNN Model

LI,

SHENG,

HUANG

2024

Preprint

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Measuring and preventing systemic risk have always been core issues in finance. To accurately capture systemic risk, this is the first introduction of the Quantile Regression Dilated Causal Convolution Neural Network (QRDCCNN) model for assessing systemic risk. This model focuses on the causal consistency of financial time series and effectively expands the model's receptive field by increasing the dilation rate layer by layer. The study selects the daily closing prices of the S\&P 500 index and 38 US financial institutions as subjects. The QRDCCNN model is employed to measure the VaR of each financial institution and the CoVaR of the financial system when these institutions are in extreme risk conditions. This paper compares the results of the QRDCCNN model with those from the DCC-GARCH, quantile regression, QRNN, and QRCNN models using the Kupiec test. The research results show that the QRDCCNN model has the highest accuracy, followed by QRNN and QRCNN models, while the DCC-GARCH model has the lowest accuracy.

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Research on the risk spillover effect of carbon emission trading market and various industry markets in China and the European Union

Cui,

Fan,

2024

Energy Science & Engineering

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Widely concerns over the carbon emission problems have been aroused. Prior studies have documented the correlation between the carbon emission market and industries separately. This study compared the tail risks and risk spillover effect of the carbon emission and 11 industries markets in China and the European Union (EU) by using the multivariate multi‐quartile conditional autoregressive at‐risk model. Moreover, to evaluate the risk spillover of each market under extreme risk conditions in time domain and frequency domain, DY spillover index and BK spillover index were constructed via generalized forecast error variance decomposition and generalized causation spectrum, respectively. Findings are as follows: (1) The tail risks and fluctuation of the trend of Chinese industry markets reflects more higher and larger than those in the EU; (2) The EU suffers from smaller external shocks, while China has the opposite result and can recover relatively faster; (3) In China, energy, industrial, information technology, financial, real estate, consumer goods, carbon emissions, and discretionary consumption industries are risk spillover industries, while healthcare, materials, telecommunication services, and utilities industries are risk receiving industries. In contrast, the risk spillover industries in the EU remain consistent with those of the Chinese markets except for the materials, discretionary consumption, consumer goods, information technology and real estate industries. (4) On the short‐term, medium‐term and long‐term scales, the risk spillover of China's carbon emission trading market and various industries is basically consistent with that of the EU. These findings contribute to reducing greenhouse gas emissions and achieving the goal of carbon peak and carbon neutrality.

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High-dimensional CoVaR risk spillover network from oil market to global stock markets—Lessons from the Kyoto Protocol

Cited by 5 publications

References 43 publications

Stock Market Sensitivity to Crude Oil and Forex Price Movements-a Study on Nifty 50 Index

Stock Market Sensitivity to Crude Oil and Forex Price Movements-a Study on Nifty 50 Index

Measurement of Systemic Risk Based on the QRDCCNN Model

Research on the risk spillover effect of carbon emission trading market and various industry markets in China and the European Union

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