Contagion Effects Among Stock Markets, Treasury Bill, Petroleum, Gold, and Cryptocurrency During the COVID-19 Pandemic: A Dynamic Conditional Correlation Approach

2021 ◽  
pp. 514-529
Author(s):  
Worrawat Saijai ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Stock Markets ◽  
Conditional Correlation ◽  
Treasury Bill ◽  
Dynamic Conditional Correlation ◽  
Contagion Effects

scholarly journals Time-Scale Comovement Between The Indian And World Stock Markets

2013 ◽  
Vol 29 (3) ◽  
pp. 765 ◽  
Author(s):  
Rahul Deora ◽  
Duc Khuong Nguyen
Keyword(s):  
Empirical Analysis ◽  
Time Scale ◽  
Stock Markets ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Time Horizons ◽  
Heterogeneous Groups

We propose a wavelet-based dynamic conditional correlation GARCH approach to investigate the time-scale comovement between the Indian and world stock markets. Our empirical analysis reveals the existence of time-scale-dependent comovement between Indian and world stock markets. The results can thus be used by heterogeneous groups of foreign and Indian investors who trade in different time horizons to actively manage and hedge against the risk of their portfolios.

The contagion effect between the oil market, and the Islamic and conventional stock markets of the GCC country

2018 ◽  
Vol 11 (2) ◽  
pp. 157-181 ◽  
Author(s):  
Taicir Mezghani ◽  
Mouna Boujelbène
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Strong Increase ◽  
Conditional Correlation ◽  
Content Type ◽  
Dynamic Conditional Correlation ◽  
Dynamic Correlation ◽  
Oil Market ◽  
Oil Crisis ◽  
Forecasting Errors

PurposeThis study aims to investigate the transmission of shock between the oil market and the Islamic and conventional stock markets of the Gulf Cooperation Council (GCC) countries during the oil shocks of 2008 and 2014.Design/methodology/approachThis study uses two models. First, the dynamic conditional correlation–generalized autoregressive conditionally heteroskedastic model has been used to capture the fundamental contagion effects between the oil market and the Islamic and conventional stock markets during the tranquil and turmoil-crisis periods of 2008-2014. Second, the filter of Kalman has been used to capture the effects of pure contagion between the oil market and the GCC Islamic and conventional stock markets. The authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices.FindingsThe main findings of this investigation are: first, the estimation of the dynamic conditional correlation– generalized autoregressive conditionally heteroskedastic model for oil market and the Islamic and conventional stock markets proves that the Islamic and conventional stock markets and oil market displayed a significant increase in the dynamic correlation during the turmoil period, from mid-2008 and mid-2014. This proves the existence of contagion between the markets studied. Second, the authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices. They show a strong increase in the correlation coefficients between the oil market and the conventional GCC stock markets, and between the conventional and Islamic GCC stock markets during the oil crisis of 2014. However, there is no change in regime in the figure of the correlation coefficient between the oil market and the GCC Islamic stock markets during the 2008 financial crisis. This pure contagion is mainly attributed to the herding bias in 2014 oil crisis.Originality/valueThis study contributes to identifying the contribution of herding bias on the volatility transmission between the oil markets, and the GCC Islamic and conventional stock market, especially during two controversial shocks: the 2008 oil-price increase and the 2014 oil drop.

scholarly journals Contagion in Futures FOREX Markets for the Post- Global Financial Crisis: A Multivariate FIGARCHcDCC Approach

2020 ◽  
Vol 4 (1) ◽  
pp. 1-1 ◽  
Author(s):  
Konstantinos Tsiaras
Keyword(s):  
Financial Crisis ◽  
Global Financial Crisis ◽  
Jel Classification ◽  
Time Varying ◽  
Conditional Correlation ◽  
Market Returns ◽  
Dynamic Conditional Correlation ◽  
Contagion Effects ◽  
Investigation Period ◽  
Conditional Correlations

This paper seeks to investigate the time-varying conditional correlations to the futures FOREX market returns. We employ a dynamic conditional correlation (DCC) Generalized ARCH (GARCH) model to find potential contagion effects among the markets. The under investigation period is 2014-2019. We focus on four major futures FOREX markets namely JPY/USD, KRW/USD, EUR/USD and INR/USD. The empirical results show an increase in conditional correlation or contagion for all the pairsof future FOREX markets. Based on the dynamic conditional correlations, KRW/USD seems to be the safest futures FOREX market. The results are of interest to policymakers who provide regulations for the futures FOREX markets. JEL Classification Codes: C58, C61, G11, G15

scholarly journals Volatility spillover and contagion effects between EURODOLLAR future and zero coupons markets: Evidence from Italy

2020 ◽  
Vol 17 (2) ◽  
pp. 67-88
Author(s):  
Konstantinos Tsiaras
Keyword(s):  
Futures Market ◽  
Time Varying ◽  
Volatility Spillover ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Contagion Effects ◽  
Eurodollar Futures ◽  
Bivariate Models ◽  
Investigation Period ◽  
Derivative Markets

This paper examines the time-varying conditional correlations between the Eurodollar futures market and the zero coupons of Banca Fideuram. We apply a bivariate dynamic conditional correlation (DCC) GARCH model in order to capture potential contagion effects between the markets for the period 2005-2017. Empirical results reveal contagion during the under-investigation period regarding the twenty-one bivariate models, showing that the Eurodollar futures market has a major impact on the zero coupons of Banca Fideuram. Findings have crucial implications for policymakers who provide regulations for the above-mentioned derivative markets.

ARE STOCK MARKETS AND CRYPTOCURRENCIES CONNECTED?

2020 ◽  
pp. 1-16
Author(s):  
MUHAMMAD UMAR ◽  
NGO THAI HUNG ◽  
SHIHUA CHEN ◽  
AMJAD IQBAL ◽  
KHALIL JEBRAN
Keyword(s):  
Operating System ◽  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Wavelet Coherence ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Nasdaq Composite Index

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies

2018 ◽  
Vol 14 (2) ◽  
pp. 245-262 ◽  
Author(s):  
Ajaya Kumar Panda ◽  
Swagatika Nanda
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
South American ◽  
Conditional Correlation ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
Dynamic Conditional Correlation ◽  
Auto Regressive

Purpose The purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region. Design/methodology/approach The time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and dynamic conditional correlation multivariate GARCH model approaches, the study finds evidence of returns and volatility linkages along with the degree of connectedness among the markets. Findings The findings of this study are consistent with increasing market connectedness among a group of leading South American economies. Stocks exhibit relatively fewer asymmetries in conditional correlations in addition to conditional volatility; yet, the asymmetry is relatively less apparent in integrated markets. The results demonstrate that co-movements are higher toward the end of the sample period than in the early phase. The stock markets of Argentina, Brazil, Chile, and Peru are closely and strongly connected within the region followed by Colombia, whereas Venezuela is least connected with the group. Practical implications The implication is that foreign investors may benefit from the reduction of the risk by adding the stocks to their investment portfolio. Originality/value The unique features of the paper include a large sample of national stock returns with updated time series data set that reveals the time series properties and empirical evidence on volatility testing. Unlike other studies, this paper uncovers the relation between the stock markets within the same region facing the same market condition.

scholarly journals Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110057
Author(s):  
Fahim Afzal ◽  
Pan Haiying ◽  
Farman Afzal ◽  
Asif Mahmood ◽  
Amir Ikram
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Stock Markets ◽  
Value At Risk ◽  
Stock Exchange ◽  
Integrated Approach ◽  
Actual Behavior ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

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