Dynamic Conditional Correlation Research Articles

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.

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.

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.

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.

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.

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.

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.

Time-varying Connectedness Between ESG Stocks and BRVM Traditional Stocks

Periods of crisis prompt investors to look out for means of making returns even in uncertain market conditions. Investors are using stocks selected on an environmental, social, and governance (ESG) basis to mitigate the unavoidable risks of investing in assets during these times of pandemic and war. In an integrated global financial system, we sought to explore the connectedness, if any, between the returns on the Bourse Régionale des Valeurs Mobilières (BRVM) and ESG-based stocks. Using a time-varying parameter vector autoregression (TVP-VAR) to analyze daily returns from 12 March 2013 to 4 April 2022, we find categorically that ESG-based stocks and BRVM stocks are connected, with ESG-based stocks dominating the network connectedness. Furthermore, using dynamic connectedness correlations from dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH), we show that ESG stocks could be used as a safe haven or weak hedge for BRVM stocks in times of crisis. We test for the robustness of our findings using quantile causality. The causality test further shows that ESG-based stocks cause movements in BRVM stocks mostly at the lower quantiles—enhancing the findings of dominancy from the TVP-VAR estimates and offer diversification and safe haven benefits from the DCC-GARCH in extreme conditions. The implication of these findings for investors is that they could benefit from using ESG-based stocks in their portfolios, particularly in times of crisis. JEL Codes: G01, G11

From wars to waves: geopolitical risks and environmental investment behaviour

ABSTRACT This study investigates the impact of geopolitical risk (GPR) on sustainable investments, focusing on five global environmental indices and two global GPR indices. Using Corrected Dynamic Conditional Correlation Generalised Autoregressive Conditional Heteroskedasticity (cDCC-GARCH) model and Diebold and Yilmaz’s spillover analysis, we use daily data from January 2009 to October 2022, covering various market phases, including the European sovereign debt crisis, the COVID-19 pandemic, and the war in Ukraine. Results from the cDCC-GARCH model reveal high dynamic conditional correlations. During periods of high volatility, environmental indices displayed simultaneous and more intense responses, limiting investment diversification alternatives when considering only the environmental side. Diebold and Yilmaz’s static analysis demonstrates that environmental segments are more influenced by systemic shocks than specific causes, with GPR’s influence proving relatively weak. In the dynamic analysis, the spillover effects of GPR in environmental segments intensified during the pandemic crisis and the invasion of Ukraine, affecting market conditions.

The domino effect of silicon valley Bank's bankruptcy and the role of FED's monetary policy

This paper examines the spillover effects of bankruptcy by important tech industry banks—Silicon Valley Bank (SVB), Silvergate Bank, and Signature Bank—on the top 10 institutions in the MSCI Bank Index and the role that monetary policy by the US Federal Reserve (the Fed) played in this contagion, using Dynamic Conditional Correlation-Exponentional Generalized Autoregressive (DCC-EGARCH) and time-varying Granger-causality models. Our findings show that the dynamic conditional correlations among the banks were higher during the period of the SVB crisis, implying the presence of financial contagion from the bank's bankruptcy due to uncertainty triggered by its collapse. Financial contagion emerges between SVB and the top 10 banks, and the degree of contagion rises during the crisis period. Moreover, the Fed's monetary policy plays a significant role in contagion due to bank failures. The deepening of financial contagion followed the Fed's increases in the federal funds rate to combat inflation.

Financial cycles synchronisation in South Africa. A dynamic conditional correlation (DCC) Approach

Financial cycles synchronisation in South Africa. A dynamic conditional correlation (DCC) Approach

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.

Are there other fish in the sea? Exploring the hedge, diversifier and safe-haven features of ESG investments

Purpose This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities). Design/methodology/approach The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions. Findings The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design. Originality/value To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.