Dynamic Conditional Correlation Research Articles

Long-Run Volatility Memory Dynamics and Inter-Market Linkages in GCC Equity Markets: Application of DCC-FIGARCH Models

Abstract The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

Break a peg! A study of stablecoin co-instability

Following the increasing adoption of stablecoins, their robustness has become an ongoing concern for investors and regulators alike. This article studies the co-instability of a large panel of stablecoins by identifying their structural breaks and co-occurrences. Moreover, we use Dynamic Time Warping (DTW) and Dynamic Conditional Correlation DCC-GARCH models to assess spillover effects in the wake of four major black swan events in the crypto world. Similar to prior literature, we conclude that algorithmic stablecoins were the major shock receivers of the IRON TITAN and TERRA LUNA crashes, highlighting the importance of allowing for sufficient margins of safety in their design. We also observe that the FTX bankruptcy and the Silicon Valley Bank collapse had repercussions of a different nature and affected more categories of stablecoins, from algorithmic to fiat money-backed. We also highlight how, irrespective of the idiosyncratic or systemic nature of the shocks, well-known custodial stablecoins, as well as the most successful crypto-collateralized tokens, showed resilience during or in the immediate aftermath of these events.

The impact of risk aversion on dynamic conditional correlations across markets: Application of DCC-EGARCH and quantile regression

The impact of risk aversion on dynamic conditional correlations across markets: Application of DCC-EGARCH and quantile regression

Credit spread drivers and cross-country connectedness: a study of emerging economies in Asia

PurposeWhile the existing literature lacks a holistic approach to determining credit spreads and is limited to mostly developed countries, this study investigates credit spread determinants and their cross-country connectedness in the context of four emerging economies in Asia by incorporating bonds, market risk, macroeconomic and global factors.Design/methodology/approachThis study utilizes principal component analysis for dimensionality reduction and variable representation. Furthermore, we employ the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity model to capture the cross-country credit spread connectedness between the variables.FindingsThe findings indicate that market volatilities are the most significant drivers of credit spreads, while global factors play a moderating role. Furthermore, the results provide compelling evidence of cross-country credit spread connectedness, with China as the primary transmitter and Malaysia as the primary receiver among the selected emerging economies.Originality/valueThis study addresses the limitations of previous research by extending the analysis beyond the commonly studied developed economies and focusing on emerging economies in Asia. It also employs a comprehensive approach to determine credit spread and explores cross-country credit spread connectedness in developing economies, thereby shedding light on financial risks and vulnerabilities within interconnected global financial systems.

Time–Frequency Co-Movement of South African Asset Markets: Evidence from an MGARCH-ADCC Wavelet Analysis

The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets.

Intersubject Dynamic Conditional Correlation: A Novel Method to Track the Framewise Network Implication during Naturalistic Stimuli.

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC). Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets. Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension. Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.

Dynamic DeFi-G7 stock markets interactions and their potential role in diversifying and hedging strategies

AbstractThis study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.

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.

Portfolio Diversification Opportunities between Europe and India do Exist

In this article, a humble effort has been made to check the volatility transmission from European stock markets (FTSE100, DAX30, CAC40 and SMI20) to the Indian stock market (NIFTY50) to determine whether there is any opportunity for portfolio managers to diversify their portfolio. Various statistical tools and econometric models, such as vector autoregression (VAR) and dynamic conditional correlation (DCC)-multivariate generalised autoregressive conditional heteroscedasticity (MGARCH), are used to detect the volatility transmission from the European market to the Indian market. The FTSE100 and the NIFTY50 have been found to have bidirectional causality. VAR results suggest that only the DAX30 and SMI20 (Germany and Switzerland) markets have an impact on the Indian market. Finally, using DCC-MGARCH, the researcher found that information spillover takes place from all European countries to India over a longer period of time, but portfolio diversification between the London market (FTSE) and the Indian market (NIFTY) and German market (DAX) with Indian market (NIFTY) is possible over a shorter period of time. JEL Codes: C01, G1, G17

Exploring the influence of Silicon Valley Bank default and U.S. financial stress on sectoral interactions and effective hedging strategies

ABSTRACT The SVB implosion could trigger a domino effect, shaking confidence in U.S. banks. This research estimates the response of good and bad cumulative abnormal returns (G-CARs, B-CARs) on the event day, pre-event and post-event periods, using financial stress, oil, geopolitical and equity uncertainty as controls. We also examine the moderating impact of the SVB implosion and U.S. financial stress indices (US-FSI) on the good and bad dynamic conditional correlations between 132 U.S. sectoral pairings. Additionally, we assess the best portfolio distribution to determine if short positions in specific U.S. sectors offer better protection against SVB’s long-term volatility using the DCC-GARCH-t copulas. Overall findings suggest that B-CARs are more adversely affected than G-CARs. Therefore, investors need to consider bearish and bullish market conditions before devising hedging strategies. The Financials, Health Care, Industrial, Materials, Oil and Gas, Real Estate and Transportation sectors of U.S. experienced significant losses due to higher B-CARs around the SVB default event. Furthermore, the SVB default’s moderating impact on the U.S. sectoral stock returns is more pronounced during bearish market conditions compared to bullish ones following a Black Swan event. This suggests a differential moderating impact of SVB on good and bad conditional connectedness and a reduction in diversification benefits.

Carbon emission allowances and Nordic electricity markets: Linkages and hedging analysis

Unlike previous studies which tend to consider only return linkages between European emission allowances and electricity markets, this paper examines return and volatility linkages between the two markets and importantly conducts a hedging analysis. Using a dynamic conditional correlation model and daily price data covering European emission allowances and Nord Pool electricity markets from 28th January 2009 to 22th February 2022, which comprise the second and third phases of the European emission allowances trading system, the main results are summarized as follows: Firstly, volatility and returns flow from the Nordic electricity market to the European emission market, with no evidence of a feedback effect, suggesting that rapid changes in demand for electricity may have forced producers to ramp up carbon-intensive facilities. Secondly, the conditional dynamic correlation between the two markets is weak but varies with time, and its sensitivity to crisis periods is noted. Thirdly, the hedging analysis indicates that Nordic electricity market participants can lower their downside risk by including carbon emission allowances in their portfolios, as evidenced by the effectiveness of hedging returns of the Nordic electricity markets with European allowances, irrespective of the COVID-19 sub-periods.

Are Indian markets insulated from the impact of cryptocurrencies? Unveiling the volatility linkages through multi‐index dynamic multivariate GARCH analysis

AbstractThis paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be short‐lived, indicated by a relatively low DCC‐GARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCC‐GARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through real‐world examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the short‐lived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.

Sustainable development and investor confidence: The safe‐haven appeal of green‐bond issuing firms

AbstractThis study investigates the performance of a green‐linked portfolio as a safe haven and hedge against traditional assets during periods of market turmoil, such as the COVID‐19 pandemic and the Russia‐Ukraine war. Utilizing a dynamic conditional correlation (DCC)‐generalized autoregressive conditional heteroscedasticity (GARCH) approach, the study analyzes the portfolio's correlations with global asse like SENSEX 30, BSE ESG 100, Gold, S&P Green Bond Index, and US 10‐year Treasury bonds, demonstrating its stability. Our findings demonstrate that the green‐linked portfolio outperforms both individual stocks and major indices as well as traditional safe havens like gold and US Treasury bonds. The portfolio exhibits superior risk‐adjusted returns and lower volatility, particularly during crisis periods. The portfolio's strong positive correlation with the S&P Green Bond Index highlights its alignment with the broader green market. Interestingly, with a 95% hedge effectiveness, it emerged as a viable safe haven investment, surpassing gold in stability during market uncertainties. Further, forecast matrix results and VAR analysis confirm the portfolio's potential as a diversifier and safe haven asset, characterized by significant positive returns. Additionally, the SAP‐LAP framework provides clarity to stakeholders on safe‐haven options within green markets. Moreover, while green bond markets are more mature in developed economies, our results suggest that green‐linked investments in emerging markets, such as India, offer a promising avenue for investors seeking both financial returns and environmental impact. Overall, the study provides empirical evidence supporting the inclusion of green‐linked assets in diversified portfolios to enhance risk management and capture long‐term growth opportunities.

Carbon exchange-traded funds market and economic policy uncertainty

This study empirically analyses the relationship between economic policy uncertainty (EPU) and three carbon exchange-traded funds (ETFs) using wavelet coherence analysis as well as dynamic conditional correlation (DCC) from a multivariate GARCH model. The analysis techniques employed allow us to analyze the nature of the relationship which may depend on time and frequency. Our findings indicate that there exists a significant negative correlation between EPU and the ETFs for almost all times and frequencies. However, for some times and frequencies, the identified negative relationship is not significant. Interestingly, we also find that for some times and frequencies, EPU leads the ETFs and for some other times and frequencies ETFs lead the EPU.

Relationship between Bitcoin and the stock market – can bitcoin serve as a safe haven for investors?

Aim: As a new asset class, Bitcoin and other cryptocurren-cies can be interesting for investors in the context of return stabilization, especially in times of crisis. We aimed to anal-yse whether Bitcoin can serve as a safe haven for investors in times of crisis.Methods: The data covers the period from September 17, 2014, to April 29, 2021, with 382 observations. Yahoo! Finance served as the source for the Bitcoin prices and Investing.com for the values of the Standard & Poor’s 500 (S&P500) Index. We used the maximum likelihood method to estimate the dynamic conditional correlation model.Results: Due to the high volatility during the analysed peri-od, Bitcoin achieved a higher risk-adjusted return compared to the S&P500 Index. The DCC model showed a positive cor-relation between the returns of the S&P500 and Bitcoin during the analysed period.Conclusions: Our results suggest that Bitcoin may not serve as a safe haven for investors in times of crisis. However, its role in this context should be further evaluated by examin-ing its relationship with other traditional asset classes (gold, commodities) and other types of cryptocurrencies such as stablecoins.

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.

The influential impacts of international dynamic spillovers in forming investor preferences: a quantile-VAR and GDCC-GARCH perspective

ABSTRACT This study investigates whether representative sectoral stock indices, gold, oil, Bitcoin, and wheat can mitigate risk and improve portfolio performance during normal times versus crises. The cutting-edge Quantile Vector Autoregressive model and the Generalized Dynamic Conditional Correlations (Generalized-DCC) framework are adopted covering from 9 January 2017 until 30 August 2022. Econometric findings by the Q-VAR reveal that oil presents the strongest connection with commodities and stock indices and that Bitcoin and wheat despite their significant linkages with financial markets fail to act as safe havens. Moreover, GDCC-GARCH indicates that the returns of sectoral indices are weakly related but display powerful volatility co-movements. Gold serves efficiently as a hedger and oil follows and both act as better shelters during crises. Nevertheless, Bitcoin partly abides by conventional markets in stressed periods. Notably, wheat reliably works as a hedger overall but does not become a safe haven during crises.

From scam to heist: the impact of cybercrimes on cryptocurrencies

ABSTRACT We analyse the impact of cybercrime, particularly cryptocurrency heists and scams, on dynamic conditional correlations and abnormal returns in cryptocurrency. Our high-frequency, hourly data set covers three years, from January 2020 to December 2022, and our results show that certain hacking events have a negligible negative impact on investors, especially when focusing on less popular tokens or coins. This new perspective has significant implications for the investment community. Existing literature generally assumes that the impact of cybercrime is situational and argues for increased awareness, proactive cybersecurity measures and multi-stakeholder collaboration in traditional financial markets such as equities and currencies. However, the cryptocurrency market – a relatively young and still developing area – has a unique dynamic. Less popular tokens or coins often have lower market integration and liquidity, indicating a lower impact of cybercrime. If such a token or coin already has a limited reputation or investor base, the overall negative sentiment may be further mitigated, lessening the expected negative impact.

Crypto-Mining and Electricity Market Nexus: Assessing the Evidence from the Nordic Electricity Prices

This paper examines the the dynamic relationship between the cryptocurrency market, through two major cryptocurrencies namely Bitcoin and Ethereum, and the Nordic electricity market. Specifically, it examines how the two main cryptocurrencies’ prices volatilities affect the Nordic electricity prices. Our analysis is based on daily spot prices from 08 August 2015 to 16 June 2020, using the dynamic conditional correlation DCC-GARCH model. Our results confirm the existence of a medium conditional correlation between cryptocurrencies and Nordic electricity market. Especially at the end of 2018, this correlation is significantly important. During this period, a peak in Ethereum prices was noticed which impact electricity prices via the high demand of power by miners who are attracted by such low prices, in the Nordic region, to increase the profitability of their activity.

Time-Varying Correlations between JSE.JO Stock Market and Its Partners Using Symmetric and Asymmetric Dynamic Conditional Correlation Models

Time-Varying Correlations between JSE.JO Stock Market and Its Partners Using Symmetric and Asymmetric Dynamic Conditional Correlation Models