Conditional Correlation Model Research Articles

Comparison of the asymmetric multifractal behavior of green and U.S. bonds against benchmark financial assets

Presently, financial portfolio managers lack a solid basis for building a reliable risk management strategy for green debt instrument investments due to the lack of compelling growth and resilience data. Therefore, this study assesses the role of green bonds in financial markets by assessing and correlating their complex scaling behaviors across multiple periods with those of key benchmark assets (e.g., conventional bonds, high-yield bonds, Euro-Dollar exchange, Dow Jones Industrial Index, Bitcoin, and Gold). Specifically, we explore linear and nonlinear correlation patterns using cross-correlation tests and the dynamic conditional correlation model, focusing on bond interactions under various degrees of freedom. Our analysis reveals that although most assets exhibit nonlinear correlations, Bitcoin uniquely aligns linearly with U.S. bonds under certain conditions. Green bonds, however, display nonlinear correlations with Bitcoin and stand out for their distinct upward financial persistence. We find also that green bonds are primary drivers in the financial domain, highlighted by their pronounced interactions and the consistent cross-correlation with the Euro-Dollar exchange rate. Moreover, green bonds have the lowest multifractality, showing persistent upward trends and antipersistent downward trends, rendering them quite resilient during periods of high volatility. These results imply that green bonds may be advantageous to portfolio risk management strategies, especially during crises when diversification and hedging tactics are needed.

Is there a relationship between financial development and geopolitical risk indices and sustainable development goals? A study on E3 countries

This paper explores the correlation between the Financial Development Index (FDI) and the Geopolitical Risk Index (GPR) in relation to Sustainable Development Goals (SDGs) within the E3 countries: Germany, France, and the United Kingdom. With limited research available on the interaction between these indices and the SDGs, this study aims to bridge that gap by focusing on SDG 13 (Climate Action) and SDG 7 (Affordable and Clean Energy). Using data spanning from 2000 to 2021, the analysis employs ARDL and Dynamic Conditional Correlation (DCC) models to assess the impact of geopolitical risks and financial development on these sustainability targets. Findings reveal that in Germany, there are significant long-term relationships between FDI and both SDG 13 and SDG 7, with geopolitical risks negatively impacting both goals. Economic growth was observed to have a stronger influence on SDG 13. In France, FDI positively affected SDG 13 but had no statistically significant impact on SDG 7. Geopolitical risks had adverse effects on both goals, while economic growth?s impact on SDG 7 was found to be insignificant. In the UK, no significant long-term relationships were identified; however, short-term analysis highlighted notable effects of FDI and geopolitical risks on SDG 7. The DCC analysis revealed that the relationship between SDG 13 and SDG 7 was relatively strong around 2000 but weakened over time.

Trivariate-ARMA–GARCH type–Vine Copula model for time series forecasting

The present study proposes trivariate-autoregressive moving average-generalized autoregressive conditional heteroscedasticity (TV-ARMA–GARCH) type Vine Copula model. The algorithm for generating a one-step-ahead forecast has been developed based on simulation. Detailed analysis of price volatility for three major vegetable crops across three geographically diverse markets in India has been carried out. The dataset comprises monthly price data for Tomato, Onion and Potato from January 2010 to July 2024, which is employed to assess the model’s forecasting accuracy. Comparative assessments are made against traditional time series models, including the multivariate-GARCH (MV-GARCH)-dynamic conditional correlation (DCC) model and the univariate (UV)-ARMA–GARCH model. Empirically, the results indicate that the TV-ARMA–GARCH type–Vine Copula model significantly outperforms these conventional models, as evidenced by evaluation metrics such as MAE (mean absolute error), RMSE (root mean square error), and MAPE (mean absolute percentage error). Additionally, the Diebold–Mariano test is utilized to validate the superior forecasting accuracy of the proposed model. The integration of Vine Copulas with the ARMA–GARCH type model significantly enhances price forecasting accuracy. This approach provides valuable insights for managing India’s complex agricultural markets.

AI companies' strategies with traditional vs. digital assets amid geopolitical and banking crises

The financial industry evolves rapidly, offering numerous opportunities but also introducing inherent risks that require careful navigation by institutions. This paper explores the performance of traditional and digital assets as tools for hedging, diversification, and safe-haven strategies during the Russian-Ukrainian crisis and the Silicon Valley Bank (SVB) collapse. Utilizing daily data from April 30, 2021, to September 15, 2023, and employing the Asymmetric Dynamic Conditional Correlation (ADCC) model, which combines four Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models and two residual distributions, we conduct a comprehensive analysis. The results indicate that Bitcoin's role as a safe haven is limited during the geopolitical crisis, impacting specific stocks, while gold's effectiveness varies. Digix Gold (DGX) demonstrates robust safe-haven properties, and Paxos Gold (PAXG) proves notably effective for certain stocks. Before the SVB crisis, gold primarily acts as a diversifier but later emerges as a significant safe haven during the banking crisis. These findings provide valuable insights to portfolio managers, emphasizing the importance of adaptive asset allocation in addressing financial uncertainties.

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.

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.

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.

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.

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

Quantitative easing and correlation dynamics in the aftermath of the Great Recession: A dynamic conditional correlation with exogenous variables approach

AbstractIdentifying the effects of quantitative easing (QE) on asset return correlations is critical to assessing such policies’ impact across financial markets. In this paper, we use a dynamic conditional correlation model that allows us to measure the impact of unconventional monetary policy on time‐varying correlations. Our results suggest that QE significantly affected correlations between stocks and bonds after the Great Recession via short‐term portfolio balance effects. The findings are critical for policy‐makers and practitioners alike. Central banks should consider the impact of monetary policy on asset correlations in their cost–benefit analyses. Likewise, portfolio managers are encouraged to factor in the effects of monetary policy on correlations to optimize portfolios and reduce potential losses strategically.

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.

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

An adaptive long memory conditional correlation model

We propose a conditional correlation model with long memory dependence and smooth structural change. Previous literature has considered correlation and covariance models with structural change or long memory, but this is the first paper to jointly model both features. The correlation matrix is decomposed into long and short run components. Short run correlations converge hypergeometrically towards a slow moving long run correlation matrix that evolves according to one or more flexible Fourier forms. The model is applied to two data sets: a US equity portfolio; and a US equity, bond, gold and oil portfolio. Model fit and out of sample forecasts over 1 to 60 day horizons support the proposed approach.

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.

Can Cryptocurrencies be a Safe Haven During the 2022 Ukraine Crisis? Implications for G7 Investors

This article attempts to assess the hedging, diversification and safe haven characteristics of gold, Bitcoin and Tether for G7 investors during the political and health crises. For this end, we use the Generalized Autoregressive Conditional Heteroskedasticity-A-Dynamic Conditional Correlation model. The findings prove that gold can be considered as a strong safe haven asset for the G7 investors during the Russia–Ukraine crisis. In contrast, cryptocurrencies fail to retain their safe haven features for Japanese investors during the COVID-19 pandemic. But, they act as diversifier assets for the rest of the G7 stock markets. The computed optimal hedge and hedging effectiveness reveal that Bitcoin displays the best hedging instrument for the United States, British, Japanese and Canadian investors during the Russia–Ukraine crisis whereas gold is considered as the best instrument for German, French and Italian investors.

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