Dynamic Conditional Correlation Research Articles

Examining time–frequency quantile dependence between green bond and green equity markets

In the context of the rapidly growing demand for green investments and the need to combat climate change, this study contributes to the emerging literature on green investments by exploring the time–frequency connectedness between green bonds (GBs) and green equities. Specifically, we examine the degree of connection between GBs and green equities, the extent to which these markets influence each other, and which one is the primary net transmitter versus the net receiver of shocks under diverse market conditions. To accomplish these objectives, we use the wavelet-based Quantile-on-Quantile (QQ), dynamic conditional correlation (DCC), portfolio implications, and Quantile VAR approaches. The results show that GBs and green equities have a strong positive connection, depending on time and frequency domains. However, a negative association between GBs and green equities is observed during periods of crisis, highlighting GBs’ ability to hedge green equity portfolios. The portfolio strategies demonstrate that investors require to invest in the Green Economy equity and S&P GB portfolio to reach the highest level of hedging effectiveness. The findings further imply that the Global Water Equity Index transmits the highest spillover to other green assets, while the Green Economy Equity Index receives the most spillover from other assets. The pairwise volatility connectivity reveals that most pairs have minimal quantile dependence, indicating the potential for diversification across the GB and green equity pairs. These findings have significant implications for investors and policymakers concerned with green investments and climate change mitigation.

Impact of Climate Policy Uncertainty (CPU) and global Energy Uncertainty (EU) news on U.S. sectors: The moderating role of CPU on the EU and U.S. sectoral stock nexus

This article accounts for the impact of positive and negative shocks of the news-related Climate Policy Uncertainty (CPU) and the novel Economist Intelligence Unit's report-based global Energy Uncertainty (EU) on the U.S. sectoral stock returns by using the ARDL and NARDL approaches with dynamic multiplier simulations. We also utilize both the DCC-GARCH and ADCC-GARCH approaches to extract the symmetric and asymmetric dynamic conditional correlations between the EU and the U.S. sectoral stock returns and then regress these conditional correlation series on the CPU through series of quantile regressions. Overall, the findings suggest that only the positive CPU shocks negatively impact the U.S. sectoral stock returns of Consumer Services, Financials, Industries, Telecommunication and Utilities in the long-term, whereas the negative CPU shocks insignificantly predict the U.S. sectoral returns. The findings also report that only the negative EU shocks increase the U.S. sectoral stock returns of Consumer Services, Financials, Health Care, Industries, Moreover, the positive (negative) EU shocks cause the U.S. sectoral returns of Materials and Technology to decrease (increase) in the long-term. Portfolio managers may consider diversifying their portfolios to include sectors least susceptible to negative impacts from the CPU and EU shocks such as Health Care and Oil & Gas. Our findings also show that CPU shocks moderate the dynamic conditional correlations between the EU and the U.S. sectoral returns of Consumer Services, Materials, Health Care, Telecommunication, Oil and Gas and Utility. Fund managers should contemplate augmenting the allocations to the Financials, Industrials, and Technology sectors owing to their diminished interconnectivity with the EU during periods of heightened CPU.

Can geopolitical risks impact the long-run correlation between crude oil and clean energy markets? Evidence from a regime-switching analysis

The increasingly volatile and complex geopolitical landscape has impacted the global energy market more frequently, posing serious challenges to the global energy low-carbon transition strategy. However, the heterogeneity of the impact of geopolitical risks (GPR) on the dynamic correlation between oil and clean energy across various market conditions and sub-industries of clean energy remains distinct. To address this gap, this paper combines a dynamic conditional correlation with the mixed data sampling (DCC-MIDAS) model and the Markov regime-switching model to investigate the influence of GPR on the correlation between oil and clean energy, particularly in diverse market conditions. Empirical findings reveal that the impacts of GPR on the long-run correlations of oil-clean energy vary across different market regimes, both in terms of the overall market and industry sectors. Specifically, GPR negatively affects the oil-clean energy correlation with stronger effects in the low-correlation regime. In particular, GPR plays a positive effect on the oil-biofuel nexus, however, the impact of GPR on the oil-fuel cells nexus exhibits opposite directions in high- and low-correlation regimes. Moreover, the transmission of geopolitical risks to the energy markets is predominantly driven by threats (GPT) during periods of low volatility and correlation, while acts (GPA) play a more significant role during periods of high volatility and correlation. These results emphasize the significance for both investors and policymakers in considering the time-varying impact of geopolitical risks across various market conditions and individual clean energy sub-sectors.

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.

Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of Bitcoin and global crude oil price movement

PurposeThis study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.Design/methodology/approachThis study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.FindingsThe outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.Research limitations/implicationsThe analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.Originality/valueMost of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

Volatility transmission in the property market during two inflationary periods: The 2008–2009 global financial crisis and the COVID-19 crisis

This research paper investigates the changes in correlation patterns of volatility between the UK and US property markets and other global markets during periods of inflation. We use the global financial crisis (GFC) and the COVID-19 crisis as proxies for two inflationary periods. Our data set consists of the period between April 11, 2007, to April 4, 2022. We apply a bivariate Dynamic Conditional Correlation (DCC) and a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to explain the time-varying correlation between UK and US property markets and other global property markets. According to our analysis, there is a sizable time-varying link between the real estate markets in our sample. Additionally, we discover that the GFC and COVID-19 exhibit different relationships with UK and US real estate market indices.

Managing risk and reaping rewards: Climate‐change futures as a game‐changer for energy futures markets

AbstractClimate‐change futures provide a platform for low‐carbon portfolios and energy market risk hedging. Climate changes induce uncertainty in energy‐commodity markets. We investigate the potential of diversifying and hedging energy‐commodity market risk with climate‐change futures, using dynamic conditional correlation (DCC)‐ordinary least squares (OLS) incorporating quantile‐dummies and cross‐quantilogram (CQ) approaches. DCC‐OLS models reveal that the World and USA climate‐change futures exhibit that they can be diversifiers for oil, ethanol, gasoil, and gasoline. These futures also exhibit hedging features for natural gas, coal, and heating oil. Euro climate‐change futures demonstrate hedging capabilities for all energy commodities except oil and gasoil. World, USA, and Euro climate‐change futures have the potential to serve as safe‐haven financial instruments in the face of the high volatility of Brent crude oil, gasoil, and heating oil. The CQ reveals that World, USA, and Euro climate‐change futures exhibit hedging and safe‐haven capacity against oil, natural gas, coal, gasoil, gasoline, and heating futures. Climate‐change futures may protect financial investments during extreme volatility in energy commodities.

US macroeconomic determinants of Bitcoin

This study aims to determine the impact of macroeconomic variables on bitcoin prices in the United States. Bitcoin is one of the cryptocurrencies that has the highest price and the most users in the United States in recent years. This study uses monthly data on inflation, interest rates, USD/EUR rates, gold prices, and bitcoin prices. To achieve the objectives of this study, Dynamic Conditional Correlation (DCC) and Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) were used. The results showed that there is a negative and significant relationship between the variables of inflation, interest rates, and USD/EUR rates affecting the price of Bitcoin in that period. Conversely, there is a positive and significant relationship between the price of gold and the price of Bitcoin in the United States during that period. An in-depth understanding of how macroeconomic factors such as inflation, interest rates and the USD/EUR rates affect Bitcoin price is key to making smart investment decisions in an increasingly complex crypto market. The findings of this analysis confirm that the significant relationship between macroeconomic variables and Bitcoin price provides deeper insights for investors to anticipate market movements and design adaptive investment strategies.

Package CovRegpy: Regularized covariance regression and forecasting in Python

Abstract This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.11. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression, along with a powerful portfolio allocation framework for dynamic multi-period asset investment management. The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: CovRegpy.py, CovRegpy_DCC.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These six scripts contain implementations of software features including multivariate covariance time series models based on the regularized covariance regression (RCR) framework, dynamic conditional correlation (DCC) framework, risk premia parity (RPP) weighting functions, singular spectrum analysis (SSA), singular spectrum decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting, and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. We examine, herein, two real-world case studies for actuarial practitioners. The first of these is a modification (demonstrating the regularization of covariance regression) of the original example from Hoff & Niu ((2012). Statistica Sinica, 22(2), 729–753) which modeled the covariance and correlative relationship that exists between forced expiratory volume (FEV) and age and FEV and height. We examine this within the context of making probabilistic predictions about mortality rates in patients with chronic obstructive pulmonary disease. The second case study is a more complete example using this package wherein we present a funded and unfunded UK pension example. The decomposition algorithm isolates high-, mid-, and low-frequency structures from FTSE 100 constituents over 20 years. These are used to forecast the forthcoming quarter’s covariance structure to weight the portfolio based on the RPP strategy. These fully funded pensions are compared against the performance of a fully unfunded pension using the FTSE 100 index performance as a proxy.

Correlation meets causality: A holistic measure of financial contagion

This study introduces a new measure of financial contagion. We argue that a rapid increase in correlations between two series is necessary but not sufficient for contagion to occur, and develop a contagion test that combines dynamic conditional correlations with time-varying Granger causality. We empirically illustrate our new approach using systemic risk data covering the period 1996–2023. We show that there are periods when correlations increase rapidly without causality, as well as periods when causality is present but correlations do not increase. The proposed test enables data-driven detection of contagion episodes and provides a clear distinction between interconnectedness and contagion.

Investigating the feasibility of the double-play strategy: evidence from Hong Kong’s speculative attacks

ABSTRACT This paper examines the ‘double-play’ strategy adopted by speculators during currency attacks in Hong Kong. We modify the speculators’ objective function to interpret how they earned potential returns from the automatic mechanism of the linked exchange rate system. Furthermore, we investigate the dynamic conditional correlations among currency, stock and derivatives markets, and identify the key determinants for these relationships during speculative attacks. Our findings offer a comprehensive insight into the behaviour of speculators during currency attacks, and provide compelling empirical support for the impact of central bank interventions on market dynamics.

Monetary Policy Spillovers and Inter-Market Dynamics Perspective of Preferred Habitat Model

This study advances the understanding of the Preferred Habitat Model’s capacity to shed light on the inter-market transfer of mean returns and the diffusion of price volatility in Pakistani investment markets. It examines the extent to which returns in one market exert a systematic influence on returns across others under the potential sway of interest rate policy shifts, USD exchange rate volatility, and domestic inflation trends. Employing a methodological arsenal that includes the GARCH process, enhanced by Dynamic Conditional Correlations (DCC), as well as the Markov Switching Model, this research assesses the propagation of mean returns and volatility across markets. The analysis uncovers significant linkages between monetary policy and stock market indices, underscoring the profound impact of monetary policy on cross-market performance transmission. These insights are pivotal for regulators overseeing the nuanced interaction between monetary policy and market performance. They are crucial for local and international investors interested in developing economies, especially in Pakistan’s markets.

Examining the dynamic conditional correlation between oil prices and stock prices: Implications for global financial markets and the impact of COVID-19

The dynamic structure of the global economy is one of many factors that affect the fluctuations in the global financial markets. COVID-19, like all other epidemics, has been an important period in human history. Exporting and importing oil are also important distinctions in economies. This study aims to investigate the dynamic relationship between stock market closing prices and Brent oil prices of six major oil-importing and oil-exporting economies during the COVID-19 period. These stock markets have a significant impact on the world economy and serve as reference points for evaluating industry performance. The Dynamic Conditional Correlation (DCC) method are used to model the complex interactions of the energy market and markets. It aims to reveal the relationship between fluctuations in energy prices and other macroeconomic indicators. According to the findings, it is concluded that fluctuations in oil prices have a non-negligible impact on stock markets due to the persistence of ARCH effects and volatility in each financial market. Assuming that volatility continues, it is seen that volatility continues to have an impact on the markets examined.

Contagion and linkages across international currencies

This paper examines dynamic linkages among international currencies in the face of several turmoil episodes including the global financial crisis (GFC), eurozone sovereign debt crisis (ESDC) and the recent COVID-19 pandemic. We utilize weekly data from August 2005 till December 2022. Our empirical strategy consists of identification of crisis periods using a Markov-switching VAR, followed by estimation of the dynamic conditional correlations using an ADCC-GARCH model and testing for the impact of the crises using a regression model with robust standard errors. We compare impact of the episodes on the dynamic correlations across the currency pairs. Our findings suggest that correlation between developed economy exchange rates increased significantly during these episodes implying existence of a contagion effect. While significant negative correlation coefficients were observed across all pairs with Japanese Yen during GFC and ESDC, indicating flight to quality. We analyse factors that may be responsible for changes in co-movement of the currencies during the study period and find that financial stress, emergence of the pandemic and geo-political risk have a significant role to play.

Can gold-backed cryptocurrencies have dynamic hedging and safe-haven abilities against DeFi and NFT assets?

Given that the interconnections of NFT and DeFi digital assets with other stablecoins still not sufficiently studied, this paper is two-fold. We first examine the dynamic conditional correlation between gold-backed cryptocurrencies and NFTs and DeFi assets during the period 02/11/2021-05/01/2023. We thereafter assess the diversification potential of gold-backed cryptocurrencies against NFTand DeFi. To this end, we use the time-varying Student's copula to investigate the cross-markets linkages among different assets in dynamic fashion. We afterwards compute the optimal hedging ratios and effectiveness index to better explore the effectiveness of portfolio risk management. Our findings clearly show that the degree of dependence between gold-backed cryptocurrencies and NFT and DeFi tokens tends to vary over time. Our results also display that gold-backed cryptocurrencies act as suitable hedging (or diversifying) assets during normal times. Nevertheless, such assets can be considered as robust safe havens during the 2022 bear market for the most of NFT and DeFi assets. More specifically, PAXG and PMGT are found to be the best safe haven instruments for both NFT and DeFi tokens. DGX also serves as safe-haven assets for some NFT and DeFi assets, but with a lower risk-mitigation capacity compared to PAXG and PMGT. In most cases, DGX tends to act as a strong diversifier. Its hedging feature is only recorded for the NFT Protocol (xNFT) and the DeFi token Chainlink (LINK). Our results are of particular interest to investors and portfolio managers who search for safe havens to mitigate the risk of their NFT and DeFi portfolios.

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.

Dynamic partial correlation models

We introduce a new scalable model for dynamic conditional correlation matrices based on a recursion of dynamic bivariate partial correlation models. By exploiting the model’s recursive structure and the theory of perturbed stochastic recurrence equations, we establish stationarity, ergodicity, and filter invertibility in the multivariate setting using conditions for bivariate slices of the data only. From this, we establish consistency and asymptotic normality of the maximum likelihood estimator for the model’s static parameters. The new model outperforms benchmarks like the t-cDCC and the multivariate t-GAS, both in simulations and in an in-sample and out-of-sample asset pricing application to US stock returns.

Gold-backed cryptocurrencies: A hedging tool against categorical and regional financial stress

This study evaluates the potential of gold-backed cryptocurrencies, such as Tether Gold and PAX Gold, as a hedge and safe haven against global, regional, and categorical financial stresses. Hedge and safe haven properties of gold-backed cryptocurrencies are also compared with those of gold and Bitcoin. For the analyses, dynamic conditional correlation (DCC) and quantile coherency techniques are applied to daily data from February 2020 to March 2023. The results show that Tether Gold and PAX Gold are strong safe havens against the US and equity-valuation-related financial stress but weak safe havens against global financial stress. Tether Gold is a weak safe haven against credit-related financial stress as well. Tether Gold is a strong hedge against US financial stress but a weak hedge against aggregate financial stress of developed economies and that of emerging economies. In our sample, gold-backed cryptocurrencies usually outperform gold and Bitcoin as a hedge and safe haven against financial stresses. The Quantile coherency analysis shows that Tether Gold is a hedge against low to moderate financial stress and a safe haven against extreme financial stresses. These findings have important implications for investors, risk-managers and policy makers.

Downside risk in Dow Jones equity markets: hedging and portfolio management during COVID-19 pandemic and the Russia–Ukraine war

PurposeThis paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.Design/methodology/approachDCC-GARCH and ADCC-GARCH models.FindingsThe most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.Originality/valueOur paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

Untangling the entanglement of US monetary policy uncertainty and European natural gas and carbon prices

Owing to the Russia–Ukraine conflict, the price of natural gas in Europe fluctuated significantly in recent years. As natural gas in Europe is highly dependent on imports, import prices are inevitably affected by monetary policy uncertainties. Additionally, monetary policy uncertainty has a significant impact on financial markets. As a unique financial market, the carbon market is affected by monetary policy uncertainties, but the mechanism of the impact is not yet clear. Therefore, we conduct a systematic study of the relationships between monetary policy uncertainty, European imported natural gas prices, and carbon prices in the last three phases of European Union emission trading scheme. This study uses the dynamic conditional correlation generalized autoregressive conditional heteroskedastic (DCC-GARCH) model and nonlinear auto-regressive distributed lag (NARDL) model to comprehensively analyse the correlation between these three variables, including volatility correlations and trend correlations. The results show that the dynamic conditional correlations between carbon and gas prices are mostly positive, as is the dynamic conditional correlations between natural gas prices and monetary policy uncertainty. However, the dynamic conditional correlations between carbon prices and monetary policy uncertainty gradually decreases and changes from positive to negative in 2015. In addition, the findings indicate the asymmetry of the short-term impact and long-term impact of monetary policy uncertainties and imported natural gas prices on the carbon prices trends. Monetary policy uncertainty has asymmetric effects on carbon prices in each phase of the research. However, the natural gas prices have asymmetric effects only in Phases 2 and 4. Based on the findings, some policy proposals are provided for the development of the carbon markets.