Conditional Correlation Research Articles

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.

Sounding out horse mussel sediment thickness: an integrated data approach

Horse mussel beds are listed as a Priority Marine Feature (PMF) in Scotland for their influence in the creation of diverse benthic communities and provision of ecosystem services. In Scotland, horse mussel beds are also recognised for their importance in marine carbon sequestration. Unfortunately, there is a paucity of data on horse mussel bed carbon stocks and sediment thickness. There are also significant financial and logistical barriers with which to address these data gaps. This study investigates the robustness of Sub-Bottom Profiling (SBP) as a cost-effective method to quantify horse mussel sediment thickness across a landscape. Integrating SBP and Drop-Down Video (DDV) data, this study also details an integrated approach to investigate the links between horse mussel habitat condition and sediment thickness. With the addition of abiotic data, this study uses Structural Equation Modelling (SEM) to elucidate key relationships between abiotic factors, biotic variables, and sediment thickness. There is a significant positive correlation of horse mussel habitat condition and sediment thickness. Average horse mussel total sediment thickness, across all measures of habitat condition, was 1.37m. These findings substantially increase previous estimates of horse mussel sediment thickness, and potential value to climate change mitigation through blue carbon frameworks. This study highlights the importance of both abiotic and biotic factors on marine carbon sediment quantification.

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.

Abstract 2331: Uncovering gene co-regulation networks in spatial transcriptomics data

Abstract Single-cell spatial transcriptomics measures thousands of genes in situ over potentially millions of cells. These rich datasets pose the best problem in genomics: how can an analyst possibly uncover all the biological insights contained within them? Careful analysis can uncover intricate biological relationships, but only if the analyst knows where to look. One useful set of “places to look” is genes sharing spatial correlations, i.e. that tend to be expressed in the same regions as each other. When genes are expressed together in space, it suggests causality, either by one gene acting on another, or by some latent variable impacting all the genes. These causal relationships have only become detectable en masse with the advent of spatial transcriptomics data, making them rich in potential for previously unknown findings. Unfortunately, spatial correlation has largely fallen short of its promise for one reason: genes expressed by the same cell type are usually highly spatially correlated. These trivial correlations overwhelm results driven by interesting biology. Here we present an algorithm designed to discover spatial correlations arising from causal relationships and not from the cell type landscape. We compute genes’ conditional correlations over space, looking at the residuals of spatial expression after accounting for the cell type landscape. In a 6,000-plex Spatial Molecular Imaging (SMI) analysis of colon cancer, we find that approximately 80% of the strongest spatial correlations can be attributed to cell type; the remaining 20% suggest underlying causal relationships. 96 distinct modules comprising up to 46 spatially correlated genes were discovered. The expression within these modules is driven by between 1 and 19 different cell types. Notable modules include those related to IFNG signaling, antigen presentation, B-cell chemotaxis and activation, and microenvironmental remodeling. Further, our study delves into the spatial correlations among ligand-receptor pairs, revealing spatial co-regulation in a limited subset of the 555 ligand-receptor pairs examined in the 6,000-plex panel. This approach enables us to discern spatial correlations that hint at biological causality, thereby beginning to delineate the "spatial interactome" of cancer. Leveraging a growing collection of 6,000-plex CosMxTM SMI cancer datasets that span various cancer types including breast, liver, kidney, lung, color, skin, and pancreatic cancers, we are systematically documenting the spatial dependencies of genes. This includes identifying correlations that are consistent across different tumors, as well as those unique to specific tumors or tumor types. Early observations have highlighted modules of chemokines (CCL3/CCL4/CCL3L, CXCL1/CXCL2/CXCL3), genes indicative of CAF phenotype (FN1 and collagens), and those involved in cell adhesion and collagen formation. For research use only. Not for use in diagnostic procedures. Citation Format: Patrick Danaher, Dan McGuire, Michael Patrick, David Kroeppler, Joachim Schmid, Joseph M. Beechem. Uncovering gene co-regulation networks in spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2331.

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.

Abstract P108: Stroke Incidence and High Sensitivity C-reactive Protein Among African Americans: The Jackson Heart Study

In the United States, strokes are a leading cause of death and disabilities, especially for older adults; however, effective biological markers to predict stroke remain elusive. The objective of this study was to investigate the association between inflammation, as measured by high-sensitivity c-reactive protein (hs-CRP) and stroke incidence among African Americans (AA) enrolled in a community-based longitudinal observational study, the Jackson Heart Study.Baseline hs-CRP levels were categorized in quintiles: Quintile 1 (0.0084 mg/L); Quintile 2 (0.0085 - 0.0189 mg/L); Quintile 3 (0.0190 - 0.036 mg/L); Quintile 4 (0.037 - 0.0675 mg/L); Quintile 5 (≥ 0.0676 mg/L). Stroke risk factors/covariates were compared across hs-CRP tertiles. Continuous variables were compared using a one-way ANOVA, and a Chi-Square test was used for categorical variables. To determine whether baseline hs-CRP levels were associated with stroke incidence, Cox regression analyses were conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CI).Results from the unadjusted model of the Cox regression indicates hs-CRP levels in quintile 2 (HR 1.48; 95% CI 0.96-2.29), quintile 3 (HR 1.44; 95% CI 0.93-2.24), and quintile 4 (HR 1.09; 95% CI 0.68-1.74) were not associated with stroke incidence when compared to quintile 1 (reference). Participants with the highest hs-CRP levels, quintile 5, were at an increased risk for stroke (HR 1.78; 95% CI 1.17-2.72) compared to the reference quintile. In a fully adjusted model including risk factors and mediators for stroke/hs-CRP (demographics, anthropometrics, health condition covariates, health behavioral risk factors, and cardiovascular disease history), the increased stroke risk incidence remained present for quintile 5 (HR 1.87; 95% CI 1.17-2.98) compared to reference quintile 1.Overall, these results corroborate previous studies’ findings in Black Americans that hs-CRP is associated with stroke incidence only at most severe levels. Although several studies have demonstrated higher hs-CRP in AAs, there have been a lesser association of hs-CRP and cardiovascular disease; hence, these findings provide new insights that hs-CRP l may not be a reliable predictor for stroke incidence among AAs.

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.

Hedging Bitcoin with commodity futures: An analysis with copper, gas, gold, and crude oil futures

There is increased interest in the dynamic relationships between cryptocurrency and commodity futures. This study examines the hedging performance of four well-known commodity futures against fluctuations in Bitcoin prices. Furthermore, this study used the DCC- and ADCC-MGARCH models to estimate conditional correlations and time-varying optimal hedge ratios between the returns of copper, gas, gold, and crude oil futures, and Bitcoin. We use a rolling window method to calculate one-step-ahead time-varying optimal hedge ratios and evaluate hedging performance. The empirical results show that gas and gold have hedge benefits to Bitcoin. However, crude oil shows poor hedge performance. From the results of one-step-ahead hedge ratios, for copper and oil, we find that hedge ratios increased and hedge effectiveness improved since the COVID-19 outbreak.

Real-time forecast of DSGE models with time-varying volatility in GARCH form

Recent research shows that time-varying volatility plays a crucial role in non-linear modeling. Contributing to this literature, we suggest an approach that allows for straightforward computation of DSGE models with time-varying volatility, where the volatility component is formulated as a GARCH process. As an application of our approach, we examine the forecasting performance of this DSGE-GARCH model using euro area real-time data. Our findings suggest that the DSGE-GARCH approach is superior in out-of-sample forecasting performance in comparison to various other benchmarks for the forecast of inflation rates, output growth and interest rates, especially in the short term. Comparing our approach to the widely used stochastic volatility specification using in-sample forecasts, we also show that the DSGE-GARCH is superior in in-sample forecast quality and computational efficiency. In addition to these results, our approach reveals interesting properties and dynamics of time-varying correlations (conditional correlations).

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

Hedging investment-grade and high-yield bonds with credit VIX

The existing literature lacks evidence of the ability of newly introduced credit VIXs to hedge investment-grade and high-yield corporate bond indices. A dynamic hedging analysis shows that credit VIX is an effective hedge for its corresponding bond index, but the hedging ability of high-yield credit VIX for high-yield bonds is more stable and effective than that of investment-grade credit VIX for investment-grade bonds. A regression analysis indicates that conditional correlations decreased during the Fed tightening cycle, resulting in more hedging effectiveness for both credit VIXs; whereas, the hedging effectiveness of high-yield bonds improved around the peak of the COVID-19 outbreak.

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.

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.

Hidden truncation model with heteroskedasticity: S&P 500 index returns reexamined

PurposeThis paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.Design/methodology/approachThe model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.FindingsThe model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.Originality/valueThe paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

Preventing Stunting in South African Children Under 5: Evaluating the Combined Impacts of Maternal Characteristics and Low Socioeconomic Conditions

More than 140 million children under five suffered from stunting in 2020. This highlights the ongoing challenge of addressing childhood malnutrition globally. We utilized data from a nationally representative sample of children under five years of age (n = 14,151) who participated in five cycles of the South African National Income Dynamics Study (SA-NIDS) (2008–2017). We estimated the proportion of stunted children attributed to the mothers’ anthropometric characteristics and socioeconomic conditions. We also quantified the population-level burden of low-socioeconomic conditions on hunger/food insecurity among pregnant women (n = 22,814) who participated in the nine rounds of the South African General Household Surveys (GHS) (2008–2021). Results from weighted-multivariate logistic regression were incorporated into the population-level impacts of correlates of stunting and low-socioeconomic conditions. The prevalence of stunting declined from 25% in 2008 to 23% in 2017. Mothers’ anthropometric measures (underweight/height < 160 cm), marital status, low education, absence of medical insurance and low-socioeconomic conditions were all identified as the most influential risk factors for stunting. Their population-level impacts on stunting increased substantially from 34% (in 2008) to 65% (in 2017). Comprehensive strategies emphasizing enhanced food security, extended breastfeeding, appropriate nutrition, and access to adequate healthcare and education are urgently needed to reduce the burden of food insecurity low-socioeconomic, malnutrition, and its long-term consequences.

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.&#x0D;

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

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

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.