Dynamic Conditional Correlation Coefficients Research Articles

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.

Is there any correlation between digital currency price fluctuation? Based on the DCC-GARCH and wavelet coherence analysis

The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a ‘contagious’ characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main ‘contagious source’ of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new ‘contagious source’. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand; strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the ‘currency’ attribute and serve the real economy.

Financial Contagion in European Equity Markets: Evidence from the US Subprime and the Eurozone Crises

The study explored the financial contagion between the five European stock markets (Germany, Belgium, France, Netherlands, and Spain) and the impact of the US subprime crisis and Eurozone crisis on it by employing multivariate DCC-GARCH methodology. The dynamic conditional correlation coefficient between Germany and other European markets was statistically significant for the period under review. The rise in the value of the conditional correlation coefficient was more evident during the US subprime crisis period, which signified the impact of this crisis on the financial interdependence of the European stock markets. But not much influence of the Eurozone crisis was observed on the dispersion of returns for all the markets, which was an important finding. The mechanism of rolling regression also corroborated these results. The study also validated the presence of financial contagion between the stock markets (except the Netherlands) during the US subprime crisis period. In contrast, the effect of the Eurozone crisis on financial contagion was significant only for Spain and Belgium. Further, the strength of financial contagion was highest for the stock market of Spain, followed by Belgium, and the least for the Netherlands. The timid reaction of the stock market of the Netherlands reflected that it could be a good choice for portfolio diversification during periods of crisis for investors. Further, governments and other regulators can learn from these findings and formulate appropriate policies to absorb the financial shocks coming from foreign markets, especially during the crisis period.

Analysis of Performance Deviation of Wind Power Enterprises in China

Considering increased global emphasis on energy security, low-carbon economy and environmental governance, the proportion of renewable energy will increase in national power grid systems. Wind power generation will play an important role in China's future power systems. Environmental uncertainty will affect the time-varying correlation between carbon efficiency and the performance of the wind power enterprises. Panel data from 2011 to 2018 is used to analyze the development status and existing problems of the wind power industry; further, with the data, the dynamic conditional correlation coefficient is calculated through the DCC-GARCH model, and the breakpoint analysis method is used to analyze the impact of policy and the economic environment on the time-varying correlations between carbon efficiency and the performance of wind power enterprises. The results show that during the China-US trade war, the sudden trade policy changes that increased tariffs led to a sharp rise in interdependence. The relevant polices inspiring the wind power industry are positively correlated with the dynamic conditional correlation coefficient, while the financial performance of alternative fossil fuels is negatively correlated with the dynamic conditional correlation coefficient.

Spillover effects between US ETFs and emerging stock markets

The current paper focuses on return and volatility spillovers between the US ETF market and emerging stock markets using a sample of 40 US-listed iShares, which track several emerging stock markets indices from the Americas, Europe, Asia and South Africa. Advanced econometric and correlation analysis techniques are employed in our investigation. More specifically, a comprehensive correlation analysis, which includes the Pearson's simple correlation coefficient and the conditional constant correlation and dynamic conditional correlation coefficients, is performed to answer whether a significant comovement pattern exists between the two markets. Going further, three alternative models, namely an ARMA model, an ARMA-GARCH model and an ARMA-EGARCH model, are used to assess the existence of material spillover effects on returns. Finally, five models are used to accentuate any significant spillovers on volatilities between US emerging markets ETFs and their benchmarks. These models are the augmented GARCH model, the ARMA-GARCH model, the ARMA-EGARCH model, the scalar-BEKK model, and the ARMA-scalar-BEKK model. The empirical findings of correlation analysis reveal a high degree of comovement between the US ETF market and the underlying emerging stock markets. Furthermore, the results on return spillovers demonstrate that significant bilateral such effects exist between ETFs and benchmarks. This is also the case for volatilities.

Hedging Characteristics of Commodity Investment in the Emerging Markets

Investors derive the greatest risk reduction benefit from portfolio diversification by holding assets with a low co-movement. As correlations between stocks worldwide have been increasing over time, interest in alternative assets such as commodities is also rising. This study examines the risk reducing properties of commodity investment from the perspective of 10 emerging markets from January 1991 through December 2013. Tests of GARCH dynamic conditional correlation coefficients indicate that commodities are portfolio diversifiers, but do not provide a significant long-term hedge. In times of extreme stock market volatility commodities provide a weak safe haven against risk in most countries. During the 1994-95 Mexican peso crisis, the 1997-98 Asian currency crisis, and the 9/11/01 attack, commodities demonstrate significant safe haven properties. However, commodities do not offer significant risk reduction in the 2008 global financial crisis and the 2010 European debt crisis.

CONTAGION AND DOWNSIDE RISK IN THE REIT MARKET DURING THE SUBPRIME MORTGAGE CRISIS

This study empirically tests the contagion effects in stock and real estate investment trust (REIT) markets during the subprime mortgage crisis by using daily stock- and REIT-markets data from the following countries and international bodies: the United States, the European Union, Japan, Hong Kong, Singapore, Australia, and the global REIT market. We found a significant and positive dynamic conditional correlation (DCC) coefficient between stock returns and REIT returns. The results revealed that the REIT markets responded early to market shocks and that the variances were higher in the post-crisis period than in the pre-crisis period. Evidence supporting the contagion effects includes increases in the means of the DCC coefficients during the post-crisis period. The Japanese and Australian REIT markets possess the lowest time-varying downside systematic risks. We also demonstrated that the “DCC E-beta” captures more significant downside linkages between market portfolios and expected REIT returns than does the standard CAPM beta.

Hedging stock sector risk with credit default swaps

This study examines the potential risk reducing benefits of credit default swaps (CDS) against risk in U.S. stock market sectors from 2004 to 2011. Tests of GARCH dynamic conditional correlation coefficients indicate that CDS serve as an effective hedge against risk in all stock sectors. CDS also provide a safe haven in times of extreme stock market volatility and during periods of financial crisis in a limited number of sectors.

Does Shift Contagion Exist Between OECD Stock Markets During The Financial Crisis?

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNormal"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">This study tests whether contagion effects exist, during the financial crisis between the U.S stock market and the OECD ones. We define shift-contagion as a significant increase in correlations in stock returns after a shock. The identification of the break point, the financial crisis, is made by the structural break test of Bai-Perron (2003). Then, time-varying correlation coefficients are estimated by the Dynamic Conditional Correlation (DCC) Multivariate GARCH Model. In order to recognize the contagion effects, we test whether the mean of the DCC coefficients in post-crisis period differs from that in the pre-crisis stable period.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNormal"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;"> </span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNormal"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">Empirical findings show that the OECD stock markets have displayed a significant increase in the means of correlation coefficients between the pre-crisis and post-crisis periods. This proves the existence of contagion between the U.S and the studied markets.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

DCC and Analysis of the Exchange Rate and the Stock Market Returns’ Volatility: An Evidence Study of Thailand Country

This paper studies the relatedness and the model construction of exchange rate volatility and the Thailand’s stock market returns. Empirical results show that we can construct a bivariate IGARCH (1, 1) model with a dynamic conditional correlation (DCC) to analyze the relationship of exchange rate volatility and Thailand’s stock market returns. The average estimation value of the DCC coefficient for these two markets equals to –0.1650, this result indicates that the exchange rate volatility negatively affects the Thailand’s stock market. Empirical result also shows that there do not exist the asymmetrical effect on the Thailand’s exchange rate and Thailand’s stock markets. And the Japan’s stock return volatility truly affects the variation risks of the Thailand stock market. Based on the viewpoint of DCC, the bivariate IGARCH (1, 1) model with a DCC has the better explanation ability compared to the traditional bivariate GARCH (1, 1) model.

Student-t distribution based VAR-MGARCH: an application of the DCC model on international portfolio risk management

Significant second-moment transmission effects and obvious time-varying patterns of correlation coefficients among major equity and currency markets in the US, Japan and the UK are found to exist. Such observations inspire the time-varying setting of dynamic conditional correlation coefficients in MGARCH models. On the other hand, the multivariate Student-t distribution is suitable for analysing the visible leptokurtosis that is common in financial markets. Both are important for international portfolio risk management. Thus, a comparison on the hedging efficiency of hypothetical portfolios consisting of stock and currency future positions is conducted in order to justify the multivariate Student-t distribution based on the DCC-MGARCH model.

Testing for contagion under asymmetric dynamics: Evidence from the stock markets between US and Taiwan

This article is an attempt to test, through the use of forward forecasting test on dynamic conditional correlation (DCC), for contagion between Taiwan and US stocks under asymmetry. The process includes three steps. The first step uses the iterated cumulative sums of squares (ICSS) algorithm to detect the structural breaks of market return. The second step creates dummy variables for breaks, estimates EGARCH model of conditional generalized error distribution, and computes dynamic conditional correlation coefficients of DCC multivariate GARCH model. The third step employs one-step and N-step forecast test to check for contagion effect. The evidences prove the asymmetric leverage effect of Taiwan weighted stock index and New York—NYSE Composite Index. Interestingly, we discovered that there are two kinds of contagion, “positive” and “negative”, between markets.

Does Contagion Effect Exist Between Stock Markets of Thailand and Chinese Economic Area (CEA) during the "Asian Flu?"

This study tests whether contagion effects exist, during the ”Asian flu”, between the stock markets of Thailand and the Chinese economic area (CEA), including China, Hong Kong, and Taiwan. The time points of structural breaks in stock return volatility are detected first, based on the iterated cumulative sums of squares (ICSS) algorithm developed by Inclan and Tiao (1994), to identify the crisis period and to add dummies to avoid the overestimation of volatility. Then, time-varying correlation coefficients are estimated by the dynamic conditional correlation (DCC) multivariate GARCH model of Engle (2002). In order to recognize the contagion effect, we test whether the mean of the DCC coefficients in post-crisis period differs from that in the pre-crisis stable period. Empirical findings show that the stock markets displayed a significant increase in the means of correlation coefficients across markets between the pre-crisis and post-crisis periods. This proves the existence of contagion between the studied markets.