Time-varying Conditional Correlations Research Articles

Crises and Stock Market Integration: Evidence from BRICS Countries

This paper examines the stock market integration in BRICS (a group of Brazil, Russia, India, China and South Africa) countries and explores the effect of two major events; COVID-19 and Russia-Ukraine war on the stock market linkages of these countries. The study is based on weekly observations of BSE SENSEX (India), Bovespa Index (Brazil), MOEX Index (Russia), Shanghai Composite Index (China), and JSE All Share Index (South Africa) collected from Yahoo Finance and Wallstreet Journal from January 2016 to December 2023. We utilize multivariate DCC GARCH (dynamic conditional correlation) model to discern the time varying conditional correlations (TVCCs) between these equity markets. Thereafter, these TVCCs are regressed on the dummy variables to capture the existence of contagion effects among the stock markets of BRICS countries during these major events. The findings of this study indicate the existence of co-movement in the stock markets prices of BRICS countries during the sample period. We find existence of significant contagion effects among the BRICS countries during the COVID period. Results show a significant downfall in the TVCCs between BRICS stock market returns following the beginning of Russia - Ukraine war.

Unveiling hidden connections: Spillover among BRICS' cryptocurrency-implied exchange rate discounts and US financial markets

This study employs connectedness approach based on Time-Varying Parameter Vector Autoregression (TVP-VAR) and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) methods, to scrutinize the connectedness between the cryptocurrency implied exchange rate discount in BRICS countries (Brazil, Russia, India, China, and South Africa) and US financial markets. Empirical evidence suggests that Bitcoin market volatility substantially influences other markets. Moreover, given the intimate connections between BRICS countries and developed nations such as the US in today's globalized context, fluctuations in the Bitcoin market within BRICS countries are apt to impact US financial markets. Our empirical findings specifically indicate the following: (i) Among the total spillover effects of the cryptocurrency implied exchange rate discount in BRICS countries, Russia exhibits the most potent total spillover effect and contributes the most to market connectedness. (ii) The Brazil directional spillover index is elevated in both spillover relationship categories, and the spillover effect of the cryptocurrency implied exchange rate discount in Brazil on US financial variables is notably pronounced. (iii) Of the US financial variables, the US Financial Stress Index demonstrates the most robust connectedness with the cryptocurrency implied exchange rate discount in BRICS countries and exhibits heightened sensitivity to uncertainty.

Spillover effects and transmission of shocks in Visegrad equity markets

PurposePaper aims to investigate the interdependencies and spillover effects that the Visegrad (V4 hereafter) Equity Markets hold on each other. The V4 group stands for the political alliance of four Central European countries: Poland, the Czech Republic, Hungary and Slovakia.Design/methodology/approachThe study uses Wavelet coherence, dynamic conditional correlation GARCH (1, 1) and unrestricted vector autoregression (VAR) methodologies. Daily data series (covering the period from January 2, 2006, to February 2, 2023) are analyzed to assess coherence, time-varying conditional correlation and shock transmission among the V4 Equity Markets.FindingsWavelet analysis reveals that the Slovak equity market does not maintain coherence with three other equity markets. The time-varying conditional correlation documents for the high interdependence during the COVID-19 outbreak of the four indexes. The VAR estimates reveal that shocks in the Warsaw equity market are easily transmitted in Prague and Budapest exchanges but not in Bratislava. The results show that the Slovak equity market tends to be isolated from the influence of other three V4 exchanges. This isolation is attributed to its size, limited volume and adoption of the euro in 2009. The study emphasizes the Slovak financial system’s gravitation toward the Eurozone after euro adoption.Originality/valueNotably, the findings provide important signals for local and international investors as the results cover four significant international shocks. The global meltdown of 2008/09, the Greek debt crisis of 2010/11, the COVID-19 pandemic and the Russia-Ukraine war.

Dynamic conditional correlation between green and grey energy ETF markets using cDCC-MGARCH model

ABSTRACTUsing the cDCC form of the multivariate GARCH models (MGARCH), this paper examines the time-varying conditional correlations among green renewable, grey non-renewable, and the conventional investment strategy in the exchange-traded funds (ETFs) markets. Daily excess returns for the largest energy ETFs are used as proxies for the US energy sector over the period of 25 June 2008 to 9 May 2023. The empirical results find that the AR(1)-GARCH(1, 1)-cDCC model with t-distribution to be the best fit. The results indicate that the time-varying correlations between green and grey energy ETFs are between 0.42 and 0.55 and statistically significant at 10%, with lesser degree of persistence in green energy, while there is a high significant co-movement between the grey energy and the traditional investment strategy. This, in turn, implies that investing in green energy ETFs provides better diversification. These results provide important implications for policymakers, portfolio managers and investors on the benefits of portfolio diversification in energy markets amid the current global energy crisis.

On the Time-varying Correlations and Hedging Effectiveness: An Analysis of Crude Oil, Gold, and Stock Market

In this paper, we examine the time-varying conditional correlation between international crude oil and gold prices, along with their volatilities, and Indian stock prices, using the DCC-GARCH model. The results suggest that the relationships between stock/oil, stock/gold, stock/oil volatility, and stock/gold volatility are time-varying. We then use the conditional variance and covariance derived from the DCC-GARCH model to measure the optimal portfolio weight and hedge ratios for stock/oil and stock/gold portfolios. Our findings indicate that, to minimize risk without reducing expected returns, investors should hold 37% and 49% of oil and gold, respectively, in their portfolios, relative to stocks. However, there is wide variation in the hedge ratios over time, suggesting that portfolio managers will need to rebalance portfolios frequently. Finally, we analyze correlation coefficients under various deciles of stock market returns, showing that gold can be used as a hedge against stock market declines. Given India's significant improvement in financial indicators, the results of this study will be useful for portfolio managers, risk managers, policymakers, and researchers.

Do Spot, Futures, and Options Markets Exhibit Price and Volatility Interdependence? Evidence from India

This paper analyses the price- and volatility-based interdependency among spot, futures, and options markets in a unified framework. The paper utilizes the vector error correction model generated under the TGARCH framework, the conventional pair-wise Granger causality test, the block exogeneity and vector Granger causality test, and Schwartz–Szakmary’s factor weights to decipher the price interdependence. The paper also uses the DCC-GARCH model to examine the existence of time-varying conditional correlation in volatility. The study finds evidence of the dependency among these three markets with a stronger leading role of spot against futures and options and significant positive/(negative) influence of the previous day’s spot/(futures) price change on options’ price change. Further, the asymmetric impact of price changes on conditional volatility is observed in the case of the spot and futures market. The results also exhibit the existence of time-varying conditional correlation among these markets with spillover effect.

The Dynamic Return and Volatility Spillovers among Size-Based Stock Portfolios in the Saudi Market and Their Portfolio Management Implications during Different Crises

This study contributes to the ongoing debate on the size effect and size-based investment styles by investigating the return and volatility spillovers and time-varying conditional correlations among Saudi large-, mid-, and small-cap indices. To this end, we utilize the weekly returns on the MSCI Saudi large-, mid-, and small-cap indices over a long sample period, spanning several crises. The econometric approach that we use is a VAR-asymmetric BEKK-GARCH model which accounts for structural breaks. On the basis of the VAR-asymmetric BEKK-GARCH model estimation results, we calculate portfolio weights and hedge ratios, and discuss their risk management implications. The empirical results confirm the presence of unilateral return spillovers running from mid- to small-cap stocks, while multilateral volatility spillovers are documented, albeit substantially weakened when accounting for structural breaks. The time-varying conditional correlations display clear spikes around crises, which translate to higher hedge ratios, increasing the cost of hedging during turbulent times. The optimal portfolio weights suggest that investors generally overweight large caps in their portfolios during uncertain times to minimize risk without lowering expected returns. The main takeaway from our results is that passively confining fund managers to a particular size category regardless of the prevailing market conditions may lead to suboptimal performance.

Dynamic relationship of volatility of returns across different markets: evidence from selected next 11 countries

PurposeThis paper aims to examine whether the volatility of returns in commodity (gold, oil), bond and forex markets is related over time to the volatility of returns in equity markets of Bangladesh, Indonesia, Pakistan, Philippines, Turkey and Vietnam. In addition, the authors analyze the integration of the commodity, bond, forex and equity markets across these markets.Design/methodology/approachThe dynamic conditional correlation GARCH (DCC-GARCH) model is used to capture the time-varying conditional correlation among markets. The authors use daily data of stock prices, oil prices, gold prices, exchange rates and 10 years' bond yields of the six countries from Datastream and investing.com from January 2001 to April 2021.FindingsFindings reveal that the parameters of dynamic correlation are statistically significant which indicates the importance of time-varying co-movements. Estimation of the DCC-GARCH model suggests that the stock market is significantly correlated with bond, forex, gold and oil markets in all six countries.Practical implicationsThis study has practical implications for policymakers and investment professionals. A better understanding of dynamic linkages among the markets would help in constructing effective hedging and portfolio diversification strategies. Policy makers can get insight to build proper strategies in order to insulate the economy from factors that cause volatility.Originality/valueSeveral studies have investigated the linkage between commodity and stock markets and the volatility spillover effect, but very little attention is given to study the interrelationship between groups of market segments of different economies. No study has comparatively examined the dynamic relationship of multiple markets of a group of emerging countries simultaneously.

Volatility Spillovers among Crude Oil, EUR/USD and Major ETS Markets during 2013-2017: A Trivariate cDCC-GARCH Application

This paper examines the time-varying conditional correlations among Crude oil. EUR/USD and major ETS markets. We apply a trivariate dynamic conditional correlation (cDCC) GARCH models in order to capture potential contagion effects between the markets for the period 2013-2017. Empirical results reveal contagion during the under investigation period regarding the trivariate models, showing potential volatility transmission channels among the markets. Findings have crucial implications for policymakers who provide regulations for the above derivative markets.

Diplomatic Associations and Exchange Rates : A Study on Russia-Ukraine War

Purpose : This paper aimed to analyze the impact of the Russia-Ukraine war on the currencies of two major groups of nations based on their diplomatic association with Russia, viz., those against invasion and the non-aligned group. Methodology : The methodology adopted in this paper could be divided into two parts. In the first part, the event study method was used to analyze the aggregated impact of war on the currencies of the two groups of nations. In the second part, a dynamic conditional correlation–generalized autoregressive conditional heteroskedastic model (DCC-GARCH) was used to analyze the dynamic correlation between the two groups of currencies in aggregate. Findings : From the first part of the analysis, it was found that the Russian ruble declined for both groups after the initial economic sanctions, but the recovery afterward was more robust against the currencies of the non-aligned group. The second part of the analysis revealed a significant increase in the time-varying dynamic conditional correlation between the returns of the two groups after the war, indicating a contagion. Practical Implications : Managers are recommended to be cautious of the impact of the increased interrelation of exchange rates among the two diplomatic groups on international business transactions. Policymakers are recommended to consider the fact that economic sanctions not uniformly supported by all major nations cannot have their intended impact. Originality : This paper is the first to examine the impact of the Russia-Ukraine war on foreign exchange rates from the perspective of diplomatic associations of nations.

Financial Contagion between Crude Oil, Gold, and Equity Sectors in India during COVID

Purpose : This study examined the financial contagion between crude oil and gold prices with the equity prices of different sectors in the Indian equity market during the recent COVID crisis. Design/Methodology/Approach : Dynamic conditional correlation (DCC) GARCH model was employed to analyze the behavior of time-varying conditional correlation during the time of COVID-19. For examining the financial contagion, regression analysis was performed on the dynamic conditional correlation and the conditional volatilities of the different markets. Findings : The DCC model showed a sharp increase in correlations between markets during the COVID19 wave. It also suggested the presence of financial contagion between the crude oil and gold markets and the different equity sectors. It also indicated that the COVID-19 effect on the conditional correlation between gold and equity sectors was temporary. In contrast, it increased the correlation between crude oil and the equity sectors. Practical Implications : The findings of this study have implications for portfolio diversification methods because higher correlations lower the benefits of diversification. Originality : This study examined the financial contagion during COVID-19 from crude oil and gold to equity sectors. Not all sectors react in the same way to changes in the prices of these commodities, and some may witness less impact compared to others during the crisis period, which makes it interesting for the study.

Dynamic Conditional Correlation and Volatility Spillover between Conventional and Islamic Stock Markets: Evidence from Developed and Emerging Countries

This study aims to investigate the dynamic conditional correlation and volatility spillover between the conventional and Islamic stock markets in developed and emerging countries in order to develop better portfolio and asset allocation strategies. We used both multivariate GARCH (MGARCH) and multi-scales-based maximal overlap discrete wavelet transform (MODWT) approaches to investigate dynamic conditional correlation and volatility spillover between conventional and Islamic stock markets in developed and emerging countries. The results show that conventional and Islamic markets move together in the long run for a specific time horizon and present time-varying volatility and dynamic conditional correlation, while volatility movement changes due to financial catastrophes and market conditions. Further, the findings point out that Chinese conventional and Islamic stock indexes showed higher volatility, whereas Malaysian conventional and Islamic stock indexes showed comparatively lower volatility during the global financial crisis. This study provides fresh insights and practical implications for risk management, asset allocation, and portfolio diversification strategies that evaluate stock market reactions to the crisis in the international avenues of finance literature.

Exploring the Contagion Effect from Developed to Emerging CEE Financial Markets

The paper aims to analyze the contagion effect coming from the developed stock markets of the US and Germany to the emerging CEE stock markets of Romania, Czech Republic, Hungary, and Poland using daily data for the period April 2005–April 2021. The paper also captures the level of integration of these emerging stock markets by analyzing the volatility spillover phenomenon. The quantification of the contagion effect coming from the developed to the emerging stock markets consisted of an empirical analysis based on the DCC-GARCH (Dynamic Conditional Correlation) model. Through this multivariate model, the time-varying conditional correlations were analyzed, both in periods of normal economic development and in times of economic instability, when there was a significant increase in the correlation coefficients between developed and emerging stock market indices. Furthermore, the level of connectedness between these markets has been analyzed using the volatility spillover index developed by Diebold and Yilmaz. The empirical results surprised the high level of integration of the analyzed stock markets in Central and Eastern Europe, with the intensity of volatility transmission between these markets increasing significantly during times of crisis. All stock market indices analyzed show periods during which they transmit net volatility and periods during which they receive net volatility, indicating a bidirectional volatility spillover phenomenon. Mostly, the BET, PX, and WIG indices are net transmitters of volatilities, whereas the BUX index is net recipient, except during the COVID-19 crisis, when it transmitted net volatility to the other three indices. Finally, using a Markov switching-regime VAR approach with two regimes, we explored the contagion effect between emerging CEE and developed stock markets during the COVID-19 pandemic. The empirical results proved a shift around the outbreak of the health crisis, after which the high volatility regime dominates the CEE markets. The contagion effects from developed stock markets to emerging CEE markets significantly increased during the first stage of the health crisis.

Stock Market Volatility Response to COVID-19: Evidence from Thailand

This study investigated how stock market volatility responded dynamically to unexpected changes during the COVID-19 pandemic and the resulting uncertainty in Thailand. Using a multivariate GARCH-BEKK model, the conditional volatility dynamics, the interlinkages, and the conditional correlations between stock market volatility and the increasing rate of COVID-19 infection cases are examined. The increased rate of COVID-19 infections impacts stock returns detrimentally; in Thailand, stock market volatility responses are asymmetric in the increase and decline situations. This disparity is due to the unfavourable impact of the pandemic’s volatility. Finally, we acknowledge that directional volatility spillover effects exist between the increase in COVID-19 cases and stock returns, suggesting that time-varying conditional correlations occur and are generally positive. Using this study’s results, governments and financial institutions can devise strategies for subsequent recessions or financial crises. Furthermore, investment managers can manage portfolio risk and forecast patterns in stock market volatility. Academics can apply our methodology in future investment trend studies to analyse additional variables in the economic system, such as the value of the US dollar, the price of commodities, or GDP.

Do commodity assets hedge uncertainties? What we learn from the recent turbulence period?

This study analyses the impact of different uncertainties on commodity markets to assess commodity markets' hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.

The impact of the COVID-19 pandemic on the nexus between the investor’s sentiment and the financial market dynamics: evidence from the Chinese market

PurposeThis study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.Design/methodology/approachIn this study, the ADCC-GARCH model was used to analyze the asymmetric volatility and the time-varying conditional correlation among the Chinese stock market, the investors' sentiment and its variation. The authors relied on Diebold and Yilmaz (2012, 2014) methodology to construct network-associated measures. Then, the wavelet coherence model was applied to explore the co-movements between these variables. To check the robustness of the study results, the authors referred to the RavenPack COVID sentiments and the Chinese VIX, as other measures of the investor's sentiment using daily data from December 2019 to December 2021.FindingsUsing the ADCC-GARCH model, a strong co-movement was found between the investor's sentiment and the Shanghai index returns during the COVID-19 pandemic. The study results provide a significant peak of connectivity between the investor's sentiment and the Chinese stock market return during the 2015–2016 and the end of 2019–2020 turmoil periods. These periods coincide, respectively, with the 2015 Chinese economy recession and the COVID-19 pandemic outbreak. Furthermore, the wavelet coherence analysis confirms the ADCC results, which revealed that the used proxies of the investor's sentiment can detect the Chinese investors' behavior especially during the health crisis.Practical implicationsThis study provides two main types of implications: on the one hand, for investors since it helps them to understand the economic outlook and accordingly design their portfolio strategy and allocate decisions to optimize their portfolios. On the other hand, for portfolios managers, who should pay attention to the volatility spillovers between investor sentiment and the Chinese stock market to predict the financial market dynamics during crises periods and hedge their portfolios.Originality/valueThis study attempted to examine the time-varying interactions between the investor's sentiment proxies and the stock market dynamics. Findings showed that the investor's sentiment is considered a prominent channel of shock spillovers during the COVID-19 crisis, which typically confirms the behavioral contagion theory.

Volatility Behaviour of Bitcoin as a Digital Asset

The objective of this study is to use the South African financial markets (Johannesburg Stock Exchange or JSE and USD/ZAR) as a case study to understand the volatility spillover dynamics of Bitcoin as a digital asset. Methodologically, the study applies the exponential generalized autoregressive conditional heteroskedastic (EGARCH) model, followed by a robustness check by applying the time-varying conditional correlation multivariate GARCH (VCC-MGARCH) model. The study utilizes the data set for the period 2011 to 2019, a period before the COVID‑19 pandemic. The research outcome revealed three interesting observations. First, Bitcoin and the South African stock market are independent of each other. Second, there is a bidirectional shock transmission between Bitcoin and USD/ZAR in the mean returns only, but not variance. Lastly, results confirm the existence of a bidirectional volatility spillover in both the mean and variance between the JSE stock market and the USD/ZAR market. The study outcome should enlighten investors who may want to consider Bitcoin as a diversifier in their investment and portfolio strategies.

Studying the relationship among vaccination for Covid-19, new tests and new deaths in USA: A DCC-EGARCH approach

This paper examines the time-varying conditional correlations among new deaths because of covid-19, new tests for covid-19 and new vaccination for covid-19. We apply a trivariate dynamic conditional correlation (DCC) EGARCH model to capture potential contagion effects between the markets for the period 2020-2022. Empirical results reveal contagion. Findings have crucial implications for policymakers who provide regulations for the above derivative markets and for investors, who apply massive strategies to convince the general public to be vaccinated.

COVID-19 pandemic’s impact on intraday volatility spillover between oil, gold, and stock markets

This study examines the volatility spillovers between the US stock market (S&P500 index) and both oil and gold before and during the global health crisis (GHC). We apply the FIAPARCH-DCC model to the 15-minute intraday data. The results showed negative (positive) conditional correlations between the S&P500 and gold (oil). The time-varying conditional correlations between markets were higher during COVID-19 spread. Moreover, gold offers more diversification gains than oil does during the pandemic. Hedging is more expensive during a pandemic than before. Oil provides higher hedging effectiveness (HE) than gold for all sub-periods. HE was lower during the COVID-19 outbreak for both oil and gold. These findings have important implications for both equity investors and policymakers.

Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships

The paper is focused on comparing the forecasting performance of two relatively new types of Vector Error Correction - Multiplicative Stochastic Factor (VEC-MSF) specifications: VEC-MSF with constant conditional correlations, and VEC-MSF-SBEKK with time-varying conditional correlations. For the sake of comparison, random walks, vector autoregressions (VAR) with constant conditional covariance matrix, and VAR-SBEKK models are also considered. Based on daily quotations on three exchange rates: PLN/EUR, PLN/USD, and EUR/USD, where the cointegrating vector may be assumed to be known a priori, we show that in econometric models it can be more important to allow for cointegration relationships than for time-varying conditional covariance matrix.