Asymmetric Volatility Spillovers Between Crude Oil and China's Financial Markets

Energy ◽  
2021 ◽  
pp. 121168
Author(s):  
Hu Wang ◽  
Shouwei Li
Keyword(s):  
Financial Markets ◽  
Crude Oil ◽  
Asymmetric Volatility ◽  
Volatility Spillovers

THE ZERO LOWER BOUND AND CRUDE OIL AND FINANCIAL MARKETS SPILLOVERS

2016 ◽  
Vol 22 (3) ◽  
pp. 654-665 ◽  
Author(s):  
Apostolos Serletis ◽  
Libo Xu
Keyword(s):  
Monetary Policy ◽  
Lower Bound ◽  
Financial Markets ◽  
Crude Oil ◽  
Zero Lower Bound ◽  
Conditional Mean ◽  
Volatility Spillovers ◽  
Dummy Variable ◽  
Oil Market ◽  
Mean And Variance

We investigate mean and volatility spillovers between the crude oil market and the debt, stock, and foreign exchange markets. In doing so, we estimate a four-variable VARMA–GARCH model with a BEKK representation and also examine the possible effects of monetary policy at the zero lower bound by including a dummy variable in both the conditional mean and variance equations. We find that the crude oil market and the financial markets are tightly interconnected and that monetary policy at the zero lower bound has strengthened their linkages.

scholarly journals Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1475 ◽  
Author(s):  
Chang ◽  
McAleer ◽  
Tian
Keyword(s):  
Financial Markets ◽  
Crude Oil ◽  
Stock Index ◽  
Financial Asset ◽  
Delayed Effect ◽  
Volatility Spillovers ◽  
Hedging Strategies ◽  
Oil Markets ◽  
The Usa ◽  
The Uk

The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK and the USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in the UK and the USA. Given the importance of the Chinese financial and economic systems, the paper also analyzes Chinese financial markets, where the data are more recent. The USA and China are the world's two largest economies and the UK is the world's sixth largest economy (and second in the existing EU) behind the USA, China, Japan, Germany, and India. Moreover, the USA and the UK are associated with WTI and Brent oil, respectively.  One of the purposes of the paper is to examine how China might be different from the USA and the UK, which seems to be borne out in the empirical analysis. Based on the conditional covariances to test the co-volatility spillovers, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets.

Dynamic and frequency connectedness across Islamic stock indexes, bonds, crude oil and gold

2019 ◽  
Vol 12 (3) ◽  
pp. 306-321 ◽  
Author(s):  
Nader Trabelsi
Keyword(s):  
Crude Oil ◽  
The United States ◽  
Asia Pacific ◽  
Volatility Spillover ◽  
Content Type ◽  
Time Frequency ◽  
Volatility Spillovers ◽  
Long Run ◽  
Portfolio Managers ◽  
Stock Indexes

Purpose This paper aims to investigate the connectedness of Islamic Stock Markets in five regional financial systems, namely, the United States, the United Kingdom, Europe (EU), GCC (Gulf Cooperation Council) and APAC (Asia-Pacific Countries), and across different asset classes (i.e. bonds, gold and crude oil). Design/methodology/approach This methodology is inspired by Diebold and Yilmaz (2012) and Barunlik and Krehlik (2017) for performing dynamic variance decomposition network and for studying time–frequency dynamics of connectedness at different frequencies. Findings Results show that the nature of connectedness over the past decade is time–frequency dynamics. The decomposition of the total volatility spillovers is mostly dominated by the long-run component. Furthermore, dominant regions are the largest contributors of spillover index, with the lowest contribution in the system coming from the GCC market. Results also reveal a slightly higher volatility spillover index of Islamic than conventional equity indexes. Finally, the system that encompasses commodities and Islamic finance instruments, generates the much lower volatility spillover. Originality/value The findings have significant implications for portfolio managers who are interested in being able to predict asset returns, as well as for policymakers who are concerned with market stability.

‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects

2019 ◽  
Vol 18 (2_suppl) ◽  
pp. S183-S212 ◽  
Author(s):  
Suparna Nandy (Pal) ◽  
Arup Kr. Chattopadhyay
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Volatility Spillover ◽  
Exchange Market ◽  
Causality Test ◽  
Asymmetric Volatility ◽  
Indian Stock Market ◽  
Domestic Stock

The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multi­variate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17

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