Dynamic Conditional Correlation Research Articles

Exchange rate volatility transmission in emerging markets

Exchange rate volatility, or a continuous fluctuation in the currency rate has been a major concern in recent years due to its impact on economic activities. No wonder concerns have been raised regarding the connection between exchange rate fluctuations and their effects on the overall economy. The motivation for the study is based on the fact that most emerging economies experiencing inflationary tendencies are more likely to experience a high degree of exchange rate volatility persistence. Such a scenario seems catastrophic to developing economies where large currency movement are more frequent. BEKK-GARCH and DCC-GARCH models were utilized to estimate volatility transmission and persistence respectively in selected African countries. Results show there is presence of spill-over effect in exchange rates of all countries. BEKK-GARCH estimates show that negative effects of exchange rate of one country had deleterious effect on exchange rate of another. We found evidence in favour of bidirectional exchange rate volatility transmission amongst all exchange rates of countries in the study. Dynamic conditional correlation (DCC) model estimates further revealed Ghanaian cedi top list of countries exchange rate volatility persistence followed by naira with a value of 1.0974. Efficient structural transformation is needed to mitigate structural problems that generate inflation in these countries.

Dynamic Linkages of Energy Commodities with Bullion and Metal Market: Evidence of Portfolio Hedging

This paper examines the dynamic linkages of volatility of energy commodities with bullion and the metal market. The proxies of energy commodities are crude oil and natural gas; bullion markets are Gold, silver and platinum and metal markets are copper and zinc. We collect daily data extending from March 18, 2010, to January 15, 2021, a period for about 12 years and employ Granger causality, Dynamic Conditional Correlation (DCC), Diebold Yilmaz (2012), Baruník & Křehlík (2018), and Network analysis for the purpose of examining spillover effect in the data considered. It is observed that there are short-run dynamic spillovers from energy (crude oil) to metal (copper) while long-run linkage is witnessed among all the constituent series. Further, Baruník & Křehlík (2018) test reveals that the total connectedness of the seven data series under study are found to be higher in frequency 2 (6 days to 15 days) than in the short run and long run. Referring to the network analysis, negative correlations are found between each pair of indices considered, i.e., Gold, silver, platinum, zinc, copper with crude oil while positive correlation is witnessed between Gold and silver. In addition, we determine portfolio hedge ratios and portfolio weights for the investors and portfolio managers. It is found that the Crude /Zinc pair had the most expensive optimal hedge ratio, while Crude/Gold had the least expensive hedging.

Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices

Models for variances and covariances of asset returns are crucial in risk management and asset allocation. Traditionally, these models were based on daily returns. Daily opening, high, low and closing (OHLC) prices have been sometimes used in multivariate volatility models for variances, but not for correlations. We therefore suggest a new version of the Dynamic Conditional Correlation (DCC) model wherein information from daily OHLC prices is utilized in both variance and correlation equations. The model is evaluated for two datasets: five exchange traded funds and five currencies. The results show that in terms of conditional covariance matrix estimates and forecasts the proposed model significantly outperforms, not only the standard DCC model, but also models that incorporate OHLC prices only in the variance equation.

The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).

Contagion between selected European indexes during the Covid-19 pandemic

The main aim of this study is to examine dynamic dependence and proof of contagion during the Covid-2019 pandemic. The empirical data are daily prices from six European indexes. The FTSE, DAX and CAC indexes represent the largest and most developed stock markets in Europe, while the Austrian ATX index represents small developed markets. The WIG and BUX indexes represent emerging European markets. This empirical study, based on the Dynamic Conditional Correlation model, which is applied to different pairs of indexes, aims to convince the reader of the increase in the correlation between the time of the pandemic (after 30 December 2019) and the period before the beginning of the pandemic. For all pairs, the mean value of the conditional correlations in the pre-Covid period was statistically below the values in the Covid period. The results indicate contagion in Europe after the outbreak of the Covid-2019 pandemic.

Volatilidade do S&P500 e o contágio do Brexit

Abstract This paper investigated the existence of contagion between S&P500 and FTSE100 stock indexes, the two major stock exchange markets in the world, due to Brexit. Brexit caused a wave of volatility in international financial markets and the immediate reaction in US market has brought instability among investors, who remained cautious regarding the unexpected unfolds over the global economy. Dynamic conditional correlation model (DCC GARCH) was applied to analyze the shift-contagion phenomenon in the time series data. The results showed that there was no evidence of shift-contagion between the two markets during the Brexit period. It was possible to observe a moderate increase in the conditional correlation during the month of the Brexit referendum, which may be due to the high interdependence between the two asset markets.

Dynamic Conditional Correlation GARCH: A Multivariate Time Series Novel Using a Bayesian Approach

Dynamic Conditional Correlation GARCH: A Multivariate Time Series Novel Using a Bayesian Approach

Dynamic correlations of bond and equity futures and macroeconomic determinants: international evidence

This paper examines whether the dynamic co-movements between stock-bond futures markets may be driven by domestic and international macroeconomic factors. The empirical analysis also investigates whether economic uncertainty and geopolitical risks have an impact on the dynamic conditional correlations of bond and equity futures markets. The results pointed to significance of domestic inflation and industrial production, while the 3M USD Libor and 3M Euribor surfaced as determinants of the dynamic equity-bond futures correlations. Finally, the paper examines the impact of the pandemic on the dynamic correlations with the split of the sample in pre- and post-pandemic periods and it was found that neither the uncertainty nor the geopolitical risk indices emerged as statistically significant in any country.

Hedging role of stablecoins

Wild price fluctuations of cryptocurrencies make it difficult for investors to maintain stable asset values. This study investigates the hedging properties of US dollar (USD)-pegged stablecoins against bitcoin returns. We analyzed the hedging abilities of the three largest stablecoins—namely, Tether, USD Coin, and Binance USD—using the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity, dummy variable regression, vector autoregression, and impulse response functions. We found that stablecoins are generally negatively correlated with bitcoin returns, indicating that they can be effective hedging instruments against high-volatility crypto assets. Among the stablecoins, Binance USD offers the largest risk reduction, and Tether was a weak safe haven during the COVID-19 crisis period. Crypto investors can diversify their portfolios by holding stablecoins.

Dynamic conditional correlation analysis of green and grey investments: Evidence from energy ETFs

Dynamic conditional correlation analysis of green and grey investments: Evidence from energy ETFs

The Characteristics of Cryptocurrency Market Volatility: Empirical Study For Five Cryptocurrency

In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions.
 This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.

Cryptocurrencies and Portfolio Performance. Does Cryptocurrency Help Improve the Portfolio Performance?

This paper investigates the performance of cryptocurrencies and market indices. Using the dynamic conditional correlation (DCC) model, the result shows that cryptocurrencies and market indices, contrary to much of the literature, tend to move in the same direction, resulting in little or no benefits in portfolio management. Dividing into the sub-sample period, cryptocurrencies have moved even more strongly with market indices during the recent period after the COVID-19 pandemic, indicating the possibility of no hedging benefit. This paper shows that inclusion of cryptocurrency in a portfolio increases the return as well as volatility, as the risk-adjusted return does not show any sign of improvement. A portfolio comprising the FTSE 100 Index seems to receive the greatest benefit of including cryptocurrencies as the risk-adjusted performance improves.

The COVID-19 pandemic and financial markets in Central Europe: Macroeconomic measures and international policy spillovers

This paper investigates the effects of macroeconomic policy announcements on financial markets in three Central European economies: Czechia, Hungary, and Poland (CE–3). We focus on the unprecedented stabilisation policies implemented from March to December 2020 during the COVID-19 pandemic, including unconventional monetary measures and large stimulus programs. Detailed categories of monetary and fiscal measures are introduced into vector autoregressions with exogenous regressors and dynamic conditional correlations, which we estimate using daily data. This allows us to control for policy spillovers from abroad, as well as global risk factors and pandemic-related variables. We find that, in general, macroeconomic policy measures implemented in the CE–3 countries played an important role in stabilising financial markets during the pandemic. We uncover several notable patterns in the reaction of markets to anti-crisis measures across the region. The impact of the monetary policy announcements on 10-year sovereign bond yields was more substantial than on stock market returns and exchange rate returns. The communication of the unconventional tools proved effective in lowering the bond yields. Interestingly, we document that the effects of non-standard measures for some variables, such as the exchange rate, can be qualitatively different from those resulting from a conventional monetary expansion. Even though the domestic monetary events became more important than the fiscal ones, the latter proved relevant for financial market returns, especially when large-scale immediate fiscal measures and tax deferrals were introduced. We also show that the CE–3 economies were subject to the cross-border transmission of policy announcement effects from the Euro Area and the US, although the magnitude of these effects was smaller than expected and varied across the CE–3 countries.

Dynamic Correlation between the Chinese and the US Financial Markets: From Global Financial Crisis to COVID-19 Pandemic

As China’s economy and the U.S. economy have shown a definite interaction, there is considerable interest in studying the correlation between the Chinese stock market and the US financial markets. This paper uses an Asymmetric Dynamic Conditional Correlation (ADCC)-GARCH to investigate the correlation between the Shanghai Composite Index (SHCI) and the U.S. financial markets, including SP500, NASDAQ, and US dollar indexes. The empirical results show that the time-varying daily and the lag-one correlation between China and the US stock markets have different performances during global events and national events. Compared with the complicated effect of negative events on the correlation of the stock market, SHCI and USD are negatively correlated with higher negative correlation during the global negative events. In addition, we found Chinese investors are more contagious to the news than American investors, indicating that the Chinese government’s policy are more indicated to Chinese investors. Finally, some policy suggestions are provided, and are beneficial to risk prevention and control, and investment.

VOLATILITY SPILLOVER AND PANDEMIC – ANALYSIS OF SELECTED SECTORAL INDICES IN INDIA

The COVID-19 pandemic has impacted economies worldwide, and it has been reflected in their stock markets, as well. The effect was evident in the Indian stock markets, yet the nature and level of this impact are not very clear. This paper examines the short- and long-term spillover in the volatility between coronavirus cases on the broader market index, Nifty 50, and the indices of selected sectors: information technology, healthcare, and pharmaceuticals. Data for the period from January 2020 to July 2022 has been analyzed. The Dynamic Conditional Correlation GARCH model was used for analyzing the volatility spillover of coronavirus cases on Nifty 50, Nifty IT, Nifty Healthcare, and Nifty Pharma. The results show that there has been a significant long-term volatility spillover of infections on the broader market index, Nifty 50. However, there is no long-term persistence of COVID-19 on the sectoral indices. Only Pharma and Healthcare have exhibited significant short-term persistence. All the indices were negatively correlated with case numbers. Even though the sectoral indices did not exhibit significant long-term volatility spillover, they were positively correlated with the broader market index, Nifty 50, which in turn showed the significant long-term persistence of COVID-19. The results of the study are useful to policymakers and investors to understand the level of market impact due to the pandemic.

Spatial Differences in Rice Price Volatility: A Case Study of Pakistan 1994–2011

Prices of agricultural commodities tend to be more volatile in comparison to other commodities. Volatility can result in inefficient allocation of the resources by the farmers, traders and consumers. Rice is the second major staple and export item of Pakistan. This study presents the trends in volatility of regional rice markets of Pakistan and analyses spatial differences in volatility across regional rice markets in Pakistan from 1994 to 2011, and also draws comparison of volatility with the international market. ARCH-LM tests are applied to check the presence of volatility and volatility clustering is found in all the markets. Tests for equality of variance and dynamic conditional correlations (DCC) GARCH model are employed to analyse the spatial differences across the regional rice markets of Pakistan. The results indicate the presence of spatial differences in volatility. Positive conditional correlations in the dynamic conditional correlations (DCC) GARCH model are found which indicate positive association of volatility across markets. Spatial differences in volatility and its persistence reflect the differences in market forces, infrastructure and information flow which leads to varying degree of risk across markets and some regions are exposed to higher risk. The study found out that Hyderabad and Sukkur are the most volatile markets and their volatility levels are highly persistent and require highest time to return to its long-term mean which makes them the riskiest rice markets. Investments in infrastructure, particularly in transportation and controlling the market power of middlemen may reduce price risk across markets particularly in the most risky markets. JEL Classification: C22, C32, Q11, Q13, Q18 Keywords: Rice Prices Volatility, Regional Markets, Pakistan. DCC-GARCH-models

Estimation and Effectiveness of Optimal Hedge Ratios of Cryptocurrencies Based on Static and Dynamic Methodologies

The emergence of cryptocurrencies futures market is an innovative platform for prudent investors to hedge risk contained in their portfolio. However, the dicey environment of cryptocurrencies and mandatory requirement of Ind AS 39 has aroused the need for estimating hedging effectiveness of their futures market. This treatise is an attempt to investigate the hedging effectiveness of Bitcoin, Ethereum, XRP and Bitcoin Cash covering the period from June 2018 to May 2022. The interconnectedness of their spot and futures markets is initially studied using Johansen cointegration test, dynamic conditional correlation model, vector error correction model and block exogeneity Wald test. Their empirical results indicate interconnectedness in these markets having significant long-term relationship; persistent volatility correlations; significant unidirectional long-term causality from futures to spot; and bidirectional short-term causality in all cryptocurrencies. So, investors can hedge their risk by engaging position in cryptocurrencies’ futures. The OLS, VECM, GARCH and TARCH methodologies are applied to estimate static optimal hedge ratios and their estimates indicate that all cryptocurrencies have negative and significant ratios except XRP. The symmetric as well as asymmetric diagonal VECH and diagonal BEKK methodologies are applied to estimate dynamic-hedge ratios and their estimates depict negative mean dynamic-hedge ratios of all cryptocurrencies except XRP. These estimations imply that investors having long position in spot contracts of Bitcoin, Ethereum and Bitcoin Cash should hedge by taking short position in their futures contracts, respectively. However, XRP investors should hedge by taking long position in XRP future contracts. The empirical results clearly indicate the outperformance of static hedge strategies over dynamic hedge strategies as variance reduction framework of Ederington favours static OLS hedge strategy and the risk–return framework of Howard and D’Antonio favours static VECM hedge strategy for all cryptocurrencies. So, the long-run considerations have played a more crucial role as compared to short-run information. These findings may guide investors having different objective functions in understanding the effectiveness of different hedge strategies and their usage for achieving their objective functions. Policymakers, treasurers and auditors may also be benefitted from the insights provided in the present treatise on different estimation methodologies for hedging effectiveness.

The impact of the Russia–Ukraine conflict (2022) on volatility connectedness between the Egyptian stock market sectors: evidence from the DCC-GARCH-CONNECTEDNESS approach

PurposeThis study investigates the impact of the Russia–Ukraine war (2022) on the volatility connectedness between Egyptian stock market sectors.Design/methodology/approachThis study employs the newest dynamic conditional correlation (DCC)-generalized autoregressive conditional heteroskedasticity (GARCH)-CONNECTEDNESS approach to examine volatility connectedness in a sample of ten sectors in the Egyptian stock market, namely banks, education, food, healthcare, industry, information technology, real estate, resources, transportation and travel, ranging from February 1, 2019 to May 31, 2022.FindingsThe findings show that connectedness among the Egyptian stock market sectors varies depending on the time. The average dynamic connectedness measure among sectors in Egypt is 73.24%. This average was 85.63% during the Russia–Ukraine War (2022). The author also shows that the transportation sector is the most significant net transmitter of volatility in the remaining sectors during the Russia–Ukraine War (2022).Practical implicationsThis study intends for policymakers to examine the co-movements, market variations and volatility spillover of stock markets, particularly during crises. Furthermore, the results help investors gain insight into diversifying the investors' portfolio assets to optimize profits.Originality/valueTo the best of the authors' knowledge, no study has investigated the implications of the war between Russia and Ukraine (2022) on sectoral interconnectedness within the stock markets in any country and discussion and empirical evidence from African countries are lacking. This study fills this gap in the literature. Additionally, the author uses the newest approach, the DCC-GARCH-CONNECTEDNESS approach, to describe the time-varying volatility spillover between economic sectors in Egypt.

The dynamics of Australian stock indices and commodities based on MGARCH-DCC and wavelet techniques

This paper aims to investigate the extent of hedging and diversification opportunities available for an Australian investor who holds a portfolio consisting of Australian conventional and Islamic indices, crude oil, gold, Bitcoin, and the Australia–US exchange rate of daily data from 2011 to 2021. The relevant time-varying and time-scale dependent techniques, such as MGARCH dynamic conditional correlation (MGARCH-DCC), continuous wavelet transform (CWT), and maximal overlap discrete wavelet transform (MODWT) are employed to discern the correlation and volatilities of the variables. The paper’s unique contribution lies in unveiling an Australian investor’s diversification opportunities of a portfolio containing Bitcoin, gold, oil, Islamic indices, conventional indices, and exchange rates. Findings from MGARCH-DCC analysis suggest that Bitcoin is the least correlated variable. However, investors may be discouraged from choosing Bitcoin as a diversification instrument due to its high volatility. The evidence further suggests that Australian investors may benefit from diversification with gold. Nonetheless, the CWT result shows that the diversification benefits effects will last for a holding period no longer than 64 days. Our research findings tend to indicate the importance of finding diversification opportunities for investors with heterogeneous investment holdings over various periods by utilising relatively advanced techniques.

The hedging role of US and Chinese stock markets against economic and trade policy uncertainty: lessons from recent turbulences

PurposeThis study analyses the impact of economic and trade policy uncertainty on US and Chinese stock markets. Also, this study examines the hedge and safe-haven properties of US and China stocks against both US and Chinese economic and trade policy uncertainty.Design/methodology/approachTo achieve the desired goals, the authors employ Dynamic Conditional Correlation through Glosten et al. (1993) model based on the Generalized Autoregressive Conditional Heteroscedasticity (DCC-GJR-GARCH (1, 1)) and Quantile cross-spectral (QS) models. The study uses monthly observations spanning from March 2010 to June 2022.FindingsThis study evidence that the economic and trade policy uncertainty between USA and China is extremely sensitive and has high volatility clustering effects on DJChina88 and DJUS, respectively. Conversely, against the Chinese economic and trade policy uncertainty, the US stock market indexes show both hedging properties across the period and safe-haven during COVID-19 and Russia–Ukraine crises. In contrast, among the Chinese stock markets, only DJShenzhen and DJShanghai stock indices might provide strong hedging and safe-haven properties against the US economic and trade policy uncertainties; however, DJShenzhen (DJChina88) stock shows weak hedge and safe-haven properties (hedging benefits) against Chinese trade policy uncertainty (CTPU) (Chinese economic policy uncertainty [CEPU]).Practical implicationsThe findings have significant implications for investors, portfolio managers and regulators in hedging and making proper decisions under uncertain circumstances.Originality/valueThe study extends the literature on stock market performance to cover the economic and trade policy uncertainty by providing novel evidence during the recent COVID-19 and Russia–Ukraine invasion.