Dynamic Conditional Correlation GARCH Model Research Articles

Mimicking crypto portfolios in sustainable investment

In this paper, considering the difference in the energy demand level, we utilize the daily pricing data from 10/01/2019 to 06/30/2023 to construct mimicking crypto portfolios with 12 clean cryptocurrencies to replace the dirty cryptocurrency, Bitcoin (BTC). With a monthly rebalancing strategy, the mimicking portfolio closely matches the exposures to the risk factors of the BTC but with fewer specific risks. Furthermore, relying on the bivariate dynamic conditional correlation (DCC-) GARCH model, we compare the hedging capability of BTC and the corresponding mimicking crypto portfolio against movements of returns of sustainable assets. The empirical results show that the mimicking crypto portfolio provides ESG investors with a cheaper hedge tool of higher hedge effectiveness compared to the BTC. Moreover, we find that the mimicking crypto portfolio can act as a strong safe haven for the S&P Global Clean Energy Index and S&P Latin America Emerging LargeMidCap ESG Index during periods of market stress. Therefore, the mimicking crypto portfolio is a more attractive option for ESG investors due to its superior hedging efficiency and the added environmental advantages.

Oil in crisis: What can we learn

This study focuses on the Chicago Board Options Exchange Oil Volatility Index (CBOEOVX)'s volatility transmission to the European stock markets. In the first section, to determine the contingent connection between market returns and CBOEOVX and to examine if CBOEOVX return Granger causes economic capital rates of return, the Dynamic Conditional Correlation (DCC) GARCH model has been used. Then, the study examined the asymmetric effects of fluctuations in estimated unpredictability on markets' profitability as the last step of the methoology. In this regard, the study applies quantile regression to examine the asymmetrical effect of the CBOEOVX on the market's daily returns. We have discovered a statistically significant inverse link that changes over time across our various sample markets. This hints at and supports CBOEOVX ‘s influence on the European stock market as a source of primary energy risk. In addition, we find substantial evidence of an asymmetric effect of fluctuations in the CBOEOVX on very negative market profits in the lowest quartile. Our findings not only build on and support previous research and add to the existing body of knowledge but also have evident consequences for future research, regulatory authorities, and practitioners. Practitioners and regulators may use these findings to better comprehend the connection between all crises and pave the path for the future.

A-DCC GARCH모형을 이용한 주식, 채권 및 외환시장의 변동성 간의 동적 관계

Purpose - This research aims to analyze the interrelationship among the volatility of stock price, interest rate, and exchange rate in Korea. The main topics are volatility spillovers and the asymmetric behaviors of each market’s volatility and correlation.
 Design/Methodology/Approach - This research adopts Engle et al.’s (2006) asymmetric dynamic conditional correlation (A-DCC) GARCH models with student t error distribution. The models are a GJR GARCH model with spillovers and a revised Koutmos (1996) EGARCH model. The full sample is divided into four sub-sample periods, including the global financial crisis and the COVID-19 pandemic.
 Findings - The results show no volatility spillovers among the returns of stock price, interest rate, and exchange rate. However, there are unidirectional spillovers originating from the exchange rate in the global crisis period, and from stock price to exchange rate in the COVID-19 period. Volatility asymmetry is consistently shown in stock price, while the others vary by period. Finally, the asymmetric correlation effects are found only in recent times.
 Research Implications - In the long-run, the volatility spillovers among financial markets are not shown. However, there is also evidence that volatility spillovers are time-varying, intensifying in times of stress. In the global financial crisis period, the exchange rate influences other markets, while in recent the COVID-19 period stock price seems to play that role. Asymmetric volatility is also time-varying, except in stock price, and recently correlation asymmetry is shown very significantly.

Uncovering Systematic Risk in Crypto currency Markets: An Empirical Investigation Using DCC-GARCH Model.

This study presents an analysis of the occurrence of structural flaws and spillovers of volatility among eight popular digital currencies, such as Bit coin (BTC), Litecoin (LTC), Ripple (XRP), BNBPrice, DOGECOINPrice,ETHEREUMPrice, TETHERPrice, and USDCOINPrice. The analysis covers the period from December 25, 2019, to August 25, 2022, utilizing various statistical tests such as the Chow Breakpoint Test, Cumulative Sum test, The Granger Causality Test, the LM test for ARCH, and Dynamic Conditional Correlation (DCC) GARCH model. The results of this research reveal being present structural breaks in all the evaluated cryptocurrencies, highlighting the unpredictable nature of the cryptocurrency market. Additionally, these cryptocurrencies exhibit notable volatility spillovers and substantial positive correlations, which point to limited benefits of diversification within the cryptocurrency market. (Chowdhury, 2020; Treiblmaier, 2018; Quispe, 2023). These results have implications for investors, policymakers, and other stakeholders in the cryptocurrency market. The study recommends including cryptocurrencies as an important component in investment portfolios to stimulate returns and reduce overall portfolio risks, it is noted that direct investment incryptocurrencies can generate higher abnormal returns, but this comes with increased risk due to their inherent volatility. Investor preference for firms involved in cryptocurrency is influenced by factors such as legal protection and familiarity. Thus, policymakers should prioritize financial stability and implement careful regulation of cryptocurrency-related announcements to prevent artificial premiums and fraudulent activities (Chowdhury, 2020; Treiblmaier, 2018; Quispe, 2023). Furthermore, the analysis highlights the presence of high volatility spillover effects among certain cryptocurrencies, particularly Bitcoin, Ethereum, and Litecoin.. While volatility offersdiversification advantages, concerns arise due to the lack of intrinsic value and dividends in cryptocurrencies (Özdemir, 2022). The presence of systematic structural breaks suggests the possibility of manipulative behaviors and potential trading strategies that warrant further investigation. The DCC GARCH analysis reveals a high correlation and significant volatility spillover effects among most cryptocurrencies. These findings emphasize the need for a more diversified cryptocurrency market to mitigate risk and promote stability within this emerging financial sector.

Using Multivariate Dynamic Conditional Correlation GARCH model to analysis financial market data

Using Multivariate Dynamic Conditional Correlation GARCH model to analysis financial market data

Using US Stock Sectors to Diversify, Hedge, and Provide Safe Havens for NFT Coins

This paper explores risk management strategies for investments in Nonfungible Token (NFT) coins through their diversification within the S&P 500 industry sectors. Given the significant decline in NFT coin values in 2022, understanding these strategies is critical for investors. This study focused on four major NFT coins (Enjin coin (ENJ), MANA, Theta coin (THETA), and the Tezos coin (XTZ)) and employed ETFs representing the major S&P 500 sectors for analysis. Dynamic conditional correlation GARCH models have been used, to estimate correlations between the NFT coins and US industry sector ETFs. Our findings showed that while most S&P 500 sectors offered diversification benefits in the pre-COVID period, all of them did during the COVID period. However, these sectors are generally weak safe havens and poor hedges. Portfolio analysis suggests an optimal NFT coin weighting of 10–30%, based on the Sharpe ratio. This study aims to pave the way for informed decision-making in the dynamic NFT market.

The time-varying correlation between popular narratives and TRY/USD FX rate: Evidence from a DCC-GARCH model

Abstract Understanding the effects of people's interactions on social media on economic fluctuations is essential for analyzing economic dynamics and making predictions. ‘Time-varying’ and ‘time-scale dependent’ volatilities between tweets sent from Turkey containing the terms "economic crisis", "inflation", "unemployment", "economic recession", "#dolar" (also their lagged series), and TL/USD FX rate was examined with dynamic conditional correlation (DCC) GARCH model. 7.402.035 Tweet data were used for the study, and their count was averaged between the dates 01.10.2020 and 11.03.2022, and a time series of 15, 30 and 60 minutes was obtained. These series of tweets were compared with the USD/TL FX rate data for the same periods. The results show that examining -delayed relationships of up to 10 lags- 6th and 10th lag of 60 min frequency Twitter data have high level of conditional correlations with TL/USD FX rate. However, except for these series 12 of that is not dynamic but a CC process and for 105 series are statistically not significant to explain CC and DCC relationship. JEL classification numbers: G12, G17, G41. Keywords: Twitter narratives, DCC-GARCH, USD/TL FX rate, narrative economics.

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.

Brazilian REITs: Are They an Opportunity for Diversification and Performance?

We study the dynamic diversification benefits of Brazilian real estate investment trusts (BR-REITs) by analyzing their complex dynamic relationship with stock and government bond indices. We estimate the assets’ conditional variance–covariance matrix using a vector autoregressive multivariate dynamic conditional correlation GARCH model with Student’s t distribution (VAR-DCCt MGARCH) and a global minimum variance portfolio. We find that BR-REITs provide strong risk-adjusted performance and persistent diversification benefits for Brazilian investors. Furthermore, these diversification benefits are not impacted by the market performance of stocks and bonds. Nevertheless, our results show that the BR-REITs’ conditional correlations with stocks and bonds are dynamic; thus, BR-REIT investors should employ dynamic investment strategies that account for time variation in REITs’ diversification benefits. Our results suggest that Brazilian investors, particularly long-term investors, would secure significant performance and diversification benefits, lowering their portfolios’ short-term volatility, by investing in BR-REITs.

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of “green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019.

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.

Volatility in tanker freight markets

Baltic tanker index studies have become vital for the crude oil and petroleum product trade. Volatility has constantly pressured vessel owners and charterers to reduce costs and decide on new ship-building orders. This study attempts to find volatility in the Baltic Dirty Tanker Index and Baltic Clean Tanker Index prices, which are sub-indices of the Baltic Tanker Index. The asymmetric corrected dynamic conditional correlation GARCH model is deployed to find the spillovers and volatility in both time series. The results indicate the strong presence of spillovers at regular intervals. This study helps vessel owners and charterers make appropriate decisions in tanker markets.

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.

The linkage between Bitcoin and foreign exchanges in developed and emerging markets

This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries: the developed G7 and the emerging BRICS. The methodology adopts the regular (R)-vine copula and compares it with two benchmark models: the multivariate t copula and the dynamic conditional correlation (DCC) GARCH model. Moreover, this study examines whether the Bitcoin meltdown of 2013, selloff of 2018, COVID-19 pandemic, 2021 crash, and the Russia-Ukraine conflict impact the linkage with conventional currencies. The results indicate that for both currency baskets, R-vine beats the benchmark models. Hence, the dependence is better modeled by providing sufficient information on the shock transmission path. Furthermore, the cross-market linkage slightly increases during the Bitcoin crashes, and reaches significant levels during the 2021 and 2022 crises, which may indicate the end of market isolation of the virtual currency.

Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies

In this paper, we investigate the co-dependence and portfolio value-at-risk of cryptocurrencies, with the Bitcoin, Ethereum, Litecoin and Ripple price series from January 2016 to December 2021, covering the crypto crash and pandemic period, using the generalized autoregressive score (GAS) model. We find evidence of strong dependence among the virtual currencies with a dynamic structure. The empirical analysis shows that the GAS model smoothly handles volatility and correlation changes, especially during more volatile periods in the markets. We perform a comprehensive comparison of out-of-sample probabilistic forecasts for a range of financial assets and backtests and the GAS model outperforms the classic DCC (dynamic conditional correlation) GARCH model and provides new insights into multivariate risk measures.

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.

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.

Regime-switching angular correlation diversification

A regime-switching dynamic conditional angular correlation GARCH (RSAC) model is proposed for optimal portfolio diversification. RSAC specifies a regime-switching angular correlation dynamic for estimating the instantaneous state-dependent correlation matrix with a single multivariate realization. RSAC is applied to investigate the diversification benefit of precious metal and energy futures for stock sector indices traded on Shenzhen stock market in China. The empirical results reveal that fuel oil futures is an effective diversifier for stock sector holdings and angular correlation GARCH is superior to conventional varying-correlation GARCH under both state-dependent and state-independent specifications in terms of risk adjusted return and reward to semivariance ratio.

Contagion effect of migration fear in pre and European refugee’s crisis period: evidence from multivariate GARCH and wavelet empirical analysis

To test the contagion effect of fear migration between countries, and to show its causality direction, our paper contributes to the economic literature by providing a new study based on migration fear indices quarterly data of France, Germany, United Kingdom and United States spanning period 1990–2019. Our empirical strategy is based on dynamic conditional correlation GARCH model and continuous wavelet transform analysis. Our results show a significant contagion effect of fear migration between the selected countries in pre and at the European Refugees crisis. The main findings of this work are changes level of conditional correlation in the two subsample periods, and changes in the arrow’s directions in red space of the phase difference between each two fear migration series. These findings indicate that European Refugees crisis changes the relationship between European Union countries and USA, and the Brexit changes the European people behavior towards migrants and refugees. Our findings offer a new directions and tracks in the international relations to the policy makers, moreover it calls into question the various studies examining economic interdependence and the contagion effect of financial crises and policies events on the different markets.

The Impact of Monetary Policy on China’s Stock and Bond Markets

This study investigates how monetary policies impacted China’s capital (stock and bond) markets during 2010–16 by employing the dynamic conditional correlation GARCH (DCC-GARCH) model. The results indicate that both the 2013 credit crisis and the 2015 stock market crash are closely related to monetary policies. In 2013, excessive lending in the banking sector and the government’s monetary policies affected the bond market. Consequently, the credit crisis occurred in the bond market and then expanded to the stock market. To solve the problem of money shortages, the government began to use traditional monetary policies and new monetary instruments to frequently release liquidity. The released liquidity quickly entered capital markets, causing an investment frenzy on the stock market, increasing risk and eventually resulting in a stock market crash. Furthermore, although the co-movement between capital markets changed over time, it would maintain positive or negative correlation for a period. Therefore, when the government implements policies in either of the markets, these policies not only affect the target market, but also impact the other market through their co-movement.