Dynamic Conditional Correlation Model Research Articles

Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling.

Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF status, and offer key insights into dynamic functional connectivity analysis in AD.

The Impact of COVID-19 Epidemic on the Hedging Islamic and Conventional Stock Markets with Financial Assets

This study investigates optimal hedging ratios for Islamic and conventional stock markets during the COVID-19 pandemic using the DCC (Dynamic Conditional Correlation), ADCC (Asymmetric Dynamic Conditional Correlation), and GO-GARCH (Generalized Orthogonal GARCH) models. The effectiveness of various financial assets as hedges is evaluated, and findings indicate that the DJCOM (Dow Jones Commodity), VISTOXX (Euro STOXX 50 Volatility Index), and VIX (Chicago Board Options Exchange Volatility Index) indices exhibit superior effectiveness across both market types, particularly DJCOM showing exceptional performance during the COVID-19 period. The hedging analysis indicates that the hedge ratios vary and depend upon the hedge instrument included in the portfolio. Furthermore, the empirical results indicate that the global impact of the pandemic diminishes the viability of one of the six assets as a safe haven instrument. In conclusion, these findings provide valuable insights for investors and portfolio managers aiming to utilize Gold, Brent, VISTOXX, VIX, CDS (Credit Default Swap), and DJCOM for portfolio rebalancing to mitigate risks associated with volatile Islamic and conventional stock returns. These conclusions contribute significantly to helping investors adjust their investment strategies more effectively and adapt to changing market conditions.

VOLATILITY AND CONDITIONAL MARKET CORRELATIONS IN PERIODS OF CRISIS

The study analyzed co-movements and contagions in Latin American stock markets, testing the benefits of diversification in times of crisis. The period investigated was from January 2000 to December 2018, testing the correlations through Asymmetric Dynamic Conditional Correlation models. The results indicate that correlations increase in periods of crisis in all markets and sectors, thus reducing the benefits of portfolio diversification. It was also possible to observe that the increase in correlations during an internal crisis is of smaller magnitude than the increase in correlations during external crises, such as Subprime.

The implications of non‐synchronous trading in G‐7 financial markets

AbstractWe investigate the effects of non‐synchronous trading on volatility spillover for the G‐7 equity markets during the Eurozone sovereign debt crisis (ESDC) and the Covid‐19 pandemic crisis. For data synchronisation we utilise ΜΑ(1) adjusted return series to estimate the Baba‐Engle‐Kraft‐Kroner (BEKK) and the dynamic conditional correlation (DCC) models. We also consider the use of realised kernels as explanatory variables in the variance equation. In this set up, the contagion effects during crises periods are more perceptible, as the spikes are easier to interpret. We also check the robustness of our main results by applying, wavelet coherence analysis to G‐7 major equity indices with realised kernels, as well as local Gaussian correlations (LGC). Our findings suggest the empirical significance of the synchronisation effects for the US and the other G‐7 equity markets. We also conclude that realised kernels is an effective tool for mitigating non‐synchronous effects. These results underline the significance of quantifying the synchronisation effects in equity markets as well as international portfolio diversification strategies.

The Wax and Wane of CPO Prices in India: A DCC Approach to Understanding Market Sentiment

This study provides a detailed analysis of Crude Palm Oil (CPO) price dynamics, focusing on the relationship between spot and future prices during significant global events, notably the COVID-19 pandemic and the Ukraine-Russia conflict. Utilizing Dynamic Conditional Correlation (DCC) models, the research examines conditional returns and covariance, as well as the correlation trends in the Crude Palm Oil (CPO) market. The study reveals critical insights into market behavior during these tumultuous times. For instance, sharp increases in covariance during key periods suggest strong movement synchronicity between spot and future prices, potentially driven by market-altering events. The onset of COVID-19 and the escalation of the Ukraine-Russia conflict are identified as significant influencers, disrupting global supply chains and impacting global food supplies, which in turn affect both spot and future Crude Palm Oil (CPO) prices. Additionally, the research finds a dynamic correlation trend between the future and spot prices of Crude Palm Oil (CPO). Starting with a strong positive correlation in 2018, the study observes a gradual decrease in this correlation, especially after 2020. This trend indicates diverging market forces or perceptions, particularly in response to the COVID-19 pandemic and the ongoing Ukraine-Russia conflict. Such fluctuations in correlation suggest varying market sentiments and expectations, which have implications for market risk assessment, hedging strategies, and speculative activities

Examining Sustainable Stock Markets with the Dynamic Conditional Correlation (DCC) Model and the Impact of Oil Prices on this Relationship

Mevcut çalışma, ülkeler arası sürdürülebilir hisse senedi piyasaları arasındaki dinamik ilişkileri incelemeyi amaçlamakta; aynı zamanda enerji piyasaları ile sürdürülebilir hisse senedi performansları arasındaki ilişki dinamiklerini ortaya koymayı hedeflemektedir. İlk olarak, ABD ve Türkiye piyasaları için sürdürülebilir hisse senedi getirileri arasındaki etkileşim, zamana bağlı değişen korelasyonu ve volatiliteyi dikkate alan dinamik koşullu korelasyon modeli ile incelenmektedir. Ardından, petrol fiyatlarının, elde edilen dinamik koşullu korelasyonlar üzerindeki etkileri Granger nedensellik analizi kullanılarak ortaya konmaktadır. Araştırmanın sonuçları, ABD ve Türkiye sürdürülebilir hisse senedi piyasaları arasındaki dinamik koşullu korelasyon modelinin stabil olduğunu ortaya koymakta; bir önceki dönemdeki şokun bugünkü dinamiklere etkisinin ve dinamik korelasyon serisindeki geciken dinamiklerin bugünkü dinamiklere olan etkisinin anlamlı olduğunu göstermektedir. Bunun yanı sıra, petrol fiyatlarının, ABD ve Türkiye sürdürülebilir hisse senedi piyasaları arasındaki dinamik koşullu korelasyonlar üzerinde anlamlı bir nedensel etkisi olduğunu göstermektedir. Son olarak, Etki-Tepki analizi sonucu, petrol fiyatlarında meydana gelen bir şoka karşılık, piyasalar arasındaki dinamik korelasyonun şok ile ters yönde bir tepki verdiği görülmekte; bu da ABD ve Türkiye sürdürülebilir hisse senedi piyasalarının ayrıştığına işaret etmektedir.

Spillover of volatility among financial instruments: ASEAN-5 and GCC market study.

The research examines a comovement and spillover of volatility among foreign exchange, conventional and shariah stock markets in Association of South East Asian Nation-5 (ASEAN) countries and Gulf Cooperation Council (GCC) countries. Generalized Autoregressive Conditional Heteroskedasticity-Baba, Engle, Kraft and Kroner (GARCH-BEKK) and Dynamic Conditional Correlation (GARCH-DCC) models are used to capture the correlation and transmission volatility of the markets. The overall results show that both the Shariah and the conventional stock indices respond similarly to each country's currency. A bidirectional (two-way relationship) volatility spillover exists only in Malaysia and a unidirectional (one-way relationship) volatility is observed in Indonesia, Singapore, Thailand, and Bahrain. The rest of the markets-the Philippines, Saudi Arabia, and the United Arab Emirates (UAE)-do not have any volatility spillover evidence. Based on DCC outcomes, the conventional and Shariah stock in ASEAN-5 countries and GCC countries reveal the market efficiency, i.e., a positive high conditional correlation. Only Bahrain shows less efficiency than the other countries. It implies no portfolio diversification advantage in conventional and Shariah stock indices. Contrarily, currency and stock (conventional and Shariah) markets provide portfolio diversification benefits for all ASEAN-5 and GCC countries.

Volatility impacts on global banks: Insights from the GFC, COVID-19, and the Russia-Ukraine war

This study analyzes the volatility impact of the Chicago Board Options Exchange Volatility Index (VIX) on the global banking sector during the Global Financial Crisis (GFC), COVID-19, and the Russia-Ukraine War. Using a Dynamic Conditional Correlation (DCC) model with asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) volatility, we examine three geographical regions, focusing on large banks. The key findings include significant symmetric Granger causality between volatility changes and negative bank returns during the GFC, asymmetric impacts of volatility increases and decreases in the lower quartile of bank returns, with COVID-19 exhibiting the strongest asymmetry, and volatility shocks affecting the downside risk of the banking sector, where the highest value-at-risk (VaR) levels occur in the GFC and the lowest during the war period. Finally, Asian banks demonstrated greater resilience to volatility impacts than European banks, which were the most affected by COVID-19 and the war. Overall, we find that volatility has less impact on the global banking sector in the war sample than in other crises. Our findings provide valuable insights for policymakers, investors, and regulators to help effectively manage future crises and ensure the stability of the global banking sector.

A Study of Islamic Portfolio Diversification Opportunities for Investors of Pakistan in various stock holding periods

This study attempts to find out the possible opportunities for the investors of Pakistan Islamic market to maximize the benefits of divergence. Dow Jones Islamic market returns (DJIMR) of four different regions named as Dow Jones Islamic Americas, Dow Jones Islamic European, Dow Jones Islamic Asian Pacific and Dow Jones Islamic Middle East and North Africa Regions across the Pakistan Karachi Meezan Index-30 have been used to capture the approach of diversification benefits. According to objectives, Multivariate GARCH— Dynamic Conditional Correlation (DCC) model is applied and it is suggested that local investors of Pakistan should prefer to invest in Asian pacific region, then to Americas region and further Middle East and North Africa region and least preference should be given to European region to maximize benefits from diversification. In order to capture the change in diversification benefits during various stock holding horizons, Continuous wavelet transformation- CWT is applied and results have indicated insignificant relationship/correlation between two markets (local and international markets of all regions) for short period of holdings rather medium and long term holdings period. Indeed, this study has focused on advance concept of incorporating the role of stock holding horizons while determining the benefits of diversification which has greater implications in the field of portfolio diversification

Potential diversification benefits: A comparative study of Islamic and conventional stock market indexes

The study aims to contribute to the better understanding of potential diversification benefits for US and its major trading partners, namely Canada, Japan, and the UK, across both Islamic and conventional indexes. We applied a Dynamic Conditional Correlations (DCC) model to examine the dynamic correlation and volatility of returns during 2001–2017. The findings put in evidence that, unlike Canadian peers, Japanese indexes exhibit a low dynamic correlation with US indexes, which ultimately suggests diversification opportunities for US-based investors. Understanding volatilities and correlations patterns through international markets would help investors, policymakers, and market participants to take informed decisions, mitigate risks, and anticipate potential spillover effects.

Connexion Between Cryptocurrencies Liquid and Futures Markets: Discernment from Static, Dynamic and Wavelet Approach

The knowledge of the interconnectedness between liquid and futures markets of cryptocurrencies amidst dynamic contemporary environment can be enriched through the full characterization of the direction, persistence and intensity of information flows between these markets. So, the present study attempts to investigate the static and dynamic connexions between liquid and futures markets of Bitcoin, Ethereum, Litecoin, Ripple XRP and Bitcoin Cash from June 2018 to June 2022. The connexion between their liquid and futures markets is first investigated using unconditional correlation, Johansen’s cointegration, vector error correction and Wald’s block exogeneity. Their estimates discern connexions encompassing significant long-run relationships between their liquid and futures markets; momentous unidirectional long-run causality from their futures market to liquid market; and momentous bidirectional short-run causality from their liquid market to futures market and from their futures market to liquid market. The present treatise encompasses methodological advancement in the investigation of interconnectedness between these markets by employing a dynamic conditional correlation model and a wavelet transform framework. Their discerned estimates indicate that the markets of Bitcoin, Litecoin, Ethereum and Bitcoin Cash have only momentous long-run perseverance, lingering and spillover effects of shocks’ sway on conditional correlations. However, there is momentous short- and long-run perseverance, lingering and spillover effects in the case of Ripple XRP. The wavelet coherence analysis also confirms these results by indicating a bidirectional short-run causal relation and a long-run positive comovement between liquid returns and futures returns of these cryptocurrencies. These discernments may help investors, portfolio managers and policymakers to enhance hedging effectiveness through optimal portfolio allocation and monitor financial contagion to attain and sustain financial stability in economies.

Dynamic Market Timing in Mutual Funds

We use the dynamic conditional correlation (DCC) model to estimate daily frequency mutual fund betas. Compared with traditional estimates, daily betas better capture changes in fund risk stemming from daily fund trading activity. Based on these beta estimates and a two-stage estimation procedure, we find significant evidence of market timing ability among actively managed U.S. equity funds that is not apparent via standard approaches. Unlike traditional measures, our timing estimates correlate positively with fund performance. Market timing is especially evident during down markets, with successful timers exhibiting low downside risk. Timing ability persists across time and attracts investor flows. This paper was accepted by David Sraer, finance. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72173127 and 72121001] and by the Institute for Industrial Innovation and Finance at Tsinghua University. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4857 .

Analyzing commodity futures and stock market indices: Hedging strategies using asymmetric dynamic conditional correlation models

The focus of this paper is on analyzing the behavior of commodity futures indices and stock market indices in terms of price volatility and hedging. Specifically, we explore the weekly hedging strategies generated by two types of asymmetric dynamic conditional correlation (DCC) processes: return-based and range-based. We evaluate the effectiveness of these strategies for both short and long hedgers, using measures such as semi-variance, low partial moment, and conditional value-at-risk. Our findings reveal that, in general, the range-based DCC model is more effective than the return-based DCC model for hedging purposes.

Uncertainty in systemic risks rankings: Bayesian and frequentist analysis

We propose efficient Bayesian Hamiltonian Monte Carlo method for estimation of systemic risk measures, LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest US financial institutions over the period of 2000–2020. The advantage of the Hamiltonian method is an efficient estimation of all parameters jointly in high dimensional models and providing posterior distributions incorporating parameter uncertainty. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric volatility. We estimate the systemic risks posterior distributions and two-step maximum likelihood distributions with bootstrap simulations for LRMES. The systemic risk rankings at different quantiles of the distributions vary considerably using bootstrap approach for computation of LRMES and SRISK, and are more stable with Bayesian posterior distributions using a parametric model. A policymaker may choose to rank the firms using some quantile of their systemic risk distributions such as 90, 95, or 99% depending on risk preferences with higher quantiles being more conservative.

Economic Activities and Management Issues for the Environment: An Environmental Kuznets Curve (EKC) and STIRPAT Analysis in Turkey

The emission of air pollutants from energy production and consumption is a major cause of environmental problems. In addition, urbanisation and CO2 emissions have become major environmental concerns that are closely related to climate change and sustainable economic growth. The purpose of this paper is to investigate the long-run relationship among CO2 emissions, energy consumption, economic activities, and management issues for Turkey for the period between 1980 and 2021. The STIRPAT hypothesis and the environmental Kuznets curve (EKC) hypothesis were employed by using dynamic conditional correlation (DCC) and ARDL bound methodologies for these goals. The findings indicate that there is a long-run relationship between variables of the STIRPAT model. The coefficient of economic expansion and energy consumption affected CO2 emissions positively, which means that energy consumption and the expansion of economic activity have significant effects on environmental degradation. Those results are also confirmed by the environmental Kuznets curve (EKC) model. In addition, the N-shaped environmental Kuznets curve (EKC) is developed for Turkey. The DCC model also shows that economic growth increases CO2 emissions significantly, and energy productivity can be considered for decreasing CO2 emissions.

An Improved DCC Model Based on Large-Dimensional Covariance Matrices Estimation and Its Applications

The covariance matrix estimation plays an important role in portfolio optimization and risk management. It is well-known that portfolio is essentially a convex quadratic programming problem, which is also a special case of symmetric cone optimization. Accurate covariance matrix estimation will lead to more reasonable asset weight allocation. However, some existing methods do not consider the influence of time-varying factor on the covariance matrix estimations. To remedy this, in this article, we propose an improved dynamic conditional correlation model (DCC) by using nonconvex optimization model under smoothly clipped absolute deviation and hard-threshold penalty functions. We first construct a nonconvex optimization model to obtain the optimal covariance matrix estimation, and then we use this covariance matrix estimation to replace the unconditional covariance matrix in the DCC model. The result shows that the loss of the proposed estimator is smaller than other variants of the DCC model in numerical experiments. Finally, we apply our proposed model to the classic Markowitz portfolio. The results show that the improved dynamic conditional correlation model performs better than the current DCC models.

Volatility transmission dynamics between energy and financial indices of emerging markets: a comparison between the subprime crisis and the COVID-19 pandemic

PurposeThis paper aims to analyze the volatility transmission between an energy stock index and a financial stock index in emerging markets during recent high instability periods. The study considers the impact of both the period under analysis and the data frequency on the direction and intensity of the contagion, as well as the effect of the potential spillovers on the risk measures. These questions still lack definitive answers and have become more relevant in a context of financially unsettling events such as COVID-19 crises.Design/methodology/approachThis study employs an extension of the dynamic conditional correlation (DCC) model that allows for the time-varying dependence relationship between the variables. This dependence is analyzed at daily, weekly and monthly basis using data from the Bloomberg platform on energy and stock market indices for emerging markets between 2001 and 2021.FindingsThe results for a sample spanning from 2001 to mid-2021 show bidirectional volatility transmission on a daily basis, whereas only evidence of volatility transmission from the financial to the energy exists for weekly and monthly frequencies. However, considering different subsamples of daily data, the authors only find volatility transmission from financial (energy) index to the energy (financial) during the Great Recession (COVID-19) as a consequence of the different source of the shock and transmission channels.Originality/valueThis study reveals that volatility transmission between energy and stocks in emerging markets has changed and presents a unidirectional pattern from energy to financial markets during the COVID-19 period in contrast to calm and the sub-prime crisis intervals. These results differ from previous studies, focused on global markets, that show bidirectional spillovers during this period.

Investigating volatility spillover of energy commodities in the context of the Chinese and European stock markets

This paper investigates spillover from energy commodities to Shanghai stock exchange and European Stock market, and identifies possible risks transmission and portfolio diversification opportunities. The study is conducted on daily spot prices of carbon (CO2) emission, natural gas and crude oil from 16 December 2010 to 29 December 2022, employing Granger causality test, dynamic conditional correlation (DCC), Diebold-Yilmaz (2012) and Barunik-Krehlic (2017) models. Results identify higher volatility and imply greater connectedness in the longer run. Additionally, natural gas is witnessed as the highest contributor of the shocks and crude oil as the highest receiver of the shocks from the network connection. Further results suggest for investment in energy commodities in shorter run rather than long run for efficient portfolio diversification. Results from this study are expected to have practical implications for portfolio managers, investors, and market regulators, given the suggestion of this study to incorporate energy stocks for efficient diversification of risk.

Portfolio Selection under Systemic Risk

AbstractThis paper proposes a modified Sharpe ratio to construct optimal portfolios under systemic events. The portfolio allocation problem is solved analytically under the absence of short‐selling restrictions and numerically when short‐selling restrictions are imposed. This approach is made operational by embedding it in a multivariate dynamic setting using dynamic conditional correlation and copula models. We evaluate the out‐of‐sample performance of our portfolio empirically over the period 2007 to 2020 using ex post final wealth paths and systemic risk metrics against mean–variance, equally weighted, and global minimum variance portfolios. Our portfolio outperforms all competitors under market distress and remains competitive in noncrisis periods.

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.