Pairs Of Assets Research Articles

ESG Meets DeFi: Exploring Time‐Varying Linkages and Portfolio Implications

ABSTRACTThis study delves into the connection between Environmental, Social and Governance (ESG) indices and emerging Decentralised Finance (DeFi) assets. Specifically, it explores the ever‐changing, frequency‐based linkage between these ESG indices and innovative DeFi assets. Employing the wavelet coherence technique, we analyse a broad range of ESG indices, spanning emerging, developed and US indices, alongside various DeFi assets. This examination covers a period encompassing the COVID‐19 pandemic and the Russian‐Ukrainian conflict to assess market linkages during extreme events. We then calculate optimal weights and hedge ratios for ESG‐DeFi asset pairs to evaluate portfolio implications. Wavelet analysis results frequently show no connection between ESG indices and DeFi assets, underscoring the diversification benefits of adding DeFi to an ESG portfolio. However, this relationship notably strengthens at the pandemic crisis's onset, indicating DeFi assets lose diversification potential during turbulent times. Portfolio allocation should mainly favour ESG indices during normal periods, with minimal variations in optimal weights pre‐ and post‐COVID. Meanwhile, hedge ratio analysis suggests DeFi assets make sound investments during crises, although hedging costs increase during turbulent periods. These findings hold relevance for investors, portfolio managers and policymakers.

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.

Chinese Crude Oil Futures and Sectoral Stocks: Copula-Based Dependence Structure and Connectedness

Abstract China launched its first crude oil futures on 18th March 2018, with the primary objective of introducing its own hedging instrument in China. Our study responds to this objective by investigating the dependence structure based on time-variant and time-invariant copula, connectedness in various market conditions, and hedging effectiveness of oil futures with eleven GICS sectors in China using daily returns data from 19th June 2019 to 24th February 2024. Findings suggest that time-varying copula is a best fit for all the GICS sectors in China except for the Energy. Similarly, the results of time-varying (TVP-VAR) connectedness indicate that the oil futures are the net receiver of shocks in total, short (1–5 days), and long-time (5 to infinity) spillover. The oil futures can better offset portfolio losses during the down market than normal and up markets. Lastly, based on TVP-VAR, the hedging ratio, optimum portfolio weights, and hedge effectiveness are calculated. We find a positive hedge ratio across various pairs of assets, which reduces the assets’ volatility. Moreover, hedge ratios and optimum portfolio weights are non-constant over time. Our results significantly contribute to the scant literature on Chinese crude oil futures and have implications for investors and policymakers.

Exploring the Relationship and Predictive Accuracy for the Tadawul All Share Index, Oil Prices, and Bitcoin Using Copulas and Machine Learning

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.

Volatility Transmission and Market Connectivity of Metals and Energy Commodities: Insights from the Spillover Index

This paper explores the interconnections between metal and energy commodities by assessing the transmission of volatility within their futures markets. Achieved through the spillover index, the analysis reveals the impact of shocks on asset pairs—highlighting which assets absorb and transmit volatility, thereby explaining market connectivity. The dataset covers a period of twenty years of daily closing prices from the London Metals Exchange for a range of commodities. The Diebold and Yilmaz (2012) index, enriched by Baruník and Krehlík (2018) was used to reflect the changes in trends. The results obtained provide insights into market connectivity and the propagation of volatility during periods of economic distress. In this context, following the 2008 crisis, precious metals exhibited significant interconnectivity, with the emergence of Silver's vulnerability along with Gold's volatility tied to pre-crisis returns. Furthermore, copper's volatility reflects conditional correlations with market stock prices and other commodities particularly during economic downturns.

Is investing in green assets costlier? Green vs. non-green financial assets

The urgent shift towards a zero- or low-carbon economy to combat climate change has heightened interest in green financial instruments among investors. Although green financial assets have grown significantly in a decade, investing in green assets is speculated to be costlier than traditional assets. Against this backdrop, we analyse whether investing in green financial assets is costlier than investing in traditional assets. We employ a comprehensive methodology, encompassing risk-adjusted returns, tail risks, time-varying correlations, novel quantile volatility connectedness, and portfolio implication techniques to show that most green assets outperform non-green assets in terms of risk-adjusted returns. Likewise, the potential loss likelihood of investing in green assets is lower. Our time-varying correlation outcomes reveal a strong positive association between green assets and their traditional counterparts, with limited hedging opportunities. Further, quantile vector autoregression results suggest that total connectedness between the green assets and their non-green pairs is lower in the normal market than in the extreme markets. Green and non-green asset pairs are also reciprocal in spreading and absorbing volatility shocks across most market states. Finally, the portfolio implications reveal that most asset pairs have a higher hedging cost; however, allocating more than 90% of funds to green assets can provide significant hedging effectiveness. Overall, investing in green assets is not costlier than investing in non-green assets, but they may be marginally rewarded. Our empirical findings have crucial implications for investors, policymakers, and regulatory bodies.

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring.

The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.

An application of copulas to OPEC’s changing influence on fossil fuel prices

This work examines how the dependence structures between energy futures asset prices differ in two periods identified before and after the 2008 global financial crisis. These two periods were characterized by a difference in the number of extraordinary meetings of OPEC countries organized to announce a change of oil production. In the period immediately following the global financial crisis, the decrease in oil prices and oil and gas demand forced OPEC countries to make frequent adjustments to the production of oil, while, since the first quarter of 2010, the recovery led to more regular meetings, with only three organized extraordinary meetings. We propose to use a copula model to study how the dependence structure among energy prices changed among the two periods. The use of copula models allows to introduce flexible and realistic models for the marginal time series; once marginal parameters are estimated, the estimates are used to fit several copula models for all asset combinations. Model selection techniques based on information criteria are implemented to choose the best models both for the univariate asset prices series and for the distribution of co-movements. The changes in the dependence structure of couple of assets are investigated through copula functionals and their uncertainty estimated through a bootstrapping method. We find the strength of dependence between asset combinations considerably differ between the two periods, showing a significant decrease for all the pairs of assets.

Do cryptocurrencies integrate with the indices of equity, sustainability, clean energy, and crude oil? A wavelet coherency approach

AbstractThe paper examines market co‐movement between pairs of financial assets in the time‐frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short‐term scales and grow stronger in the medium‐term scales, especially during the COVID‐19 period, and further exhibit weaker heterogeneous associations in the long‐term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID‐19 periods (Medium‐term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short‐term. The lead–lag heterogeneous effects of the asset‐pairs may pave arbitrage opportunities for investors.

Geoacoustic Inversion Using Simple Hand-Deployable Acoustic Systems

This article proposes the use of a simple, low-cost, hand-deployable pair of experimental assets to conduct geoacoustic inversion at sea. The system consists of an expendable, fully mechanical acoustic source called a rupture induced underwater sound source (RIUSS) and a new ropeless passive acoustic mooring called a TOSSIT (not an acronym). Used together, RIUSS and TOSSIT enable the collection of acoustic data suitable to perform single-hydrophone geoacoustic inversion. The method is illustrated using data collected on the New England Mud Patch in May 2021 from a relatively small (22 m) and inexpensive chartered fishing vessel. Modal time-frequency dispersion from 15 to 385 Hz is extracted from the TOSSIT/RIUSS data using warping, and used as input for Bayesian transdimensional geoacoustic inversion. The inversion results compare favorably to results obtained with data collected on the same track with traditional assets (e.g., a vertical line array) during the 2017 Seabed Characterization Experiment, even when jointly inverting for the water-column sound speed profile and seabed geoacoustic parameters. This further demonstrates inversion repeatability in a given location using data sets collected years apart, and under different (and potentially unknown) oceanographic conditions.

Robust optimal investment strategy for a DC pension plan in the market with mispricing and constant elasticity of variance

This paper considers an optimal asset allocation problem for a defined contribution pension fund in a continuous time setting, and the manager is concerned about potential model misspecification. We suppose that the financial market consists of one risk-free asset, one market index fund whose price satisfies the constant elasticity of variance model, and a pair of risky assets with mispricing. Under the objective of maximizing the expected utility of the pension fund wealth at the retirement, the closed form expressions of the robust optimal investment strategy and the corresponding value function are obtained by using the stochastic control theory. Finally, some numerical examples are given to compare the difference between the ambiguity averse and the ambiguity neutral strategies, and to investigate the effect of relevant parameters on the strategy. We find that the optimal investment strategy is not a long-short symmetric strategy when the mispricing assets have different mispricing correct capability, and it will lead to significant utility loss for the manager if the model uncertainty risk is ignored.

An Algorithm for Exchanging Target Asset Pairs using the Kidney Exchange Model

Since chemical, biological, radiological, nuclear, and high yield explosive (CBRNE) attacks can cause catastrophic damage, it is important to detect and eliminate the means of attack at the origin. In surveillance operations, efficient allocation of friendly intelligence assets and enemy targets is critical for continuous and reliablemonitoring. In this research, we investigate a mathematical model for exchanging target–asset pairs when there are sudden changes in various operational environments. For this task, we refer to the kidney exchange model as a benchmark. In particular, the methods for constructing and solving the target–asset exchange problem in near realtime are presented. Additionally, we introduce the methodology and results for obtaining a feasible solution of the weapon target assignment problem using the exchange model. Our method can facilitate decisions in reconnaissance operations, especially when countless targets and assets are intricately intertwined in future battlefield scenarios.

How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?

C52; C53; G15.

Modeling Covid-19 contagious effect between asset markets and commodity futures in India

For the purpose of optimizing an investment portfolio in the Indian market, this research provides an understanding of the connectedness, volatility transmission and investment scenario between different pairs of commodity futures and assets, observing the impact of Covid-19. To do that, we derived a daily dataset spanning from 2016 to 2021 bifurcated into two panels comprising the pre & post-Covid-19 onset periods. First, we employed wavelet coherence plots to check the directional connectedness between asset and commodity futures. The DCC-GARCH model was then applied on both the data panels to investigate and compare volatility transmission. Hedge ratios and optimum portfolio weights have been estimated to demonstrate a comparative investment scenario. We observe a significant long-term volatility transmission from the asset markets to commodity futures for both the onset panel. Commodity futures provide effective hedging and diversification efficiency against the asset markets. DCC, hedge ratio and optimum portfolio weights show the heterogeneous patterns of investment for pre & post-Covid-19 period. Gold, bond, bullion futures and exchange rates are recognized as a gauge of the economy in crisis. This work will provide significant insight to readers about portfolio construction and required changes in portfolio weights in normal and crisis market conditions.

One track, two experiments four years apart: On the repeatability of geoacoustic inversion on the New England Mud Patch

Our current knowledge of the geoacoustic properties of the New England Mud Patch (NEMP) is mostly driven by data collected in 2017 as part of the Seabed Characterization Experiment (SBCEX17). In 2021, a modest geoacoustic inversion experiment was performed on the NEMP using a simple and low-cost pair of experimental assets: a “TOSSIT” passive acoustic mooring and an impulsive “RIUSS” (Rupture Induced, Underwater Sound Source). The TOSSIT/RIUSS data were collected on a track that was studied intensively during SBCEX17, but with fundamental differences in oceanographic conditions: a frontal intrusion was present at the experimental site in 2021, creating a strongly stratified sound speed profile (SSP) in the water column, while the water column was essentially iso-speed in 2017. The 2021 TOSSIT/RIUSS data are used to perform geoacoustic inversion using warping and Bayesian trans-dimensional methods. The geoacoustic properties estimated for the 2021 data compare favorably to results obtained with SBCEX17 data, even when the 2021 data are inverted jointly for water-column SSP and seabed parameters. This study demonstrates inversion repeatability on the NEMP using data sets collected years apart and under different (and potentially unknown) oceanographic conditions. [Work supported by the Office of Naval Research.]

On the time-varying correlations between oil-, gold-, and stock markets: The heterogeneous roles of policy uncertainty in the US and China

This paper investigates the effects of economic policy uncertainty (EPU) and monetary policy uncertainty (MPU) in the US and China on oil-stock and gold-stock correlations. A quantile regression approach is employed to analyze the heterogeneous impacts under different market correlation regimes. Our findings suggest that the “US impact” prevails across all market correlations in the sample, while “China impact” is found for oil-stock correlations. Furthermore, the impacts of EPU and MPU on correlations of different asset pairs exhibit heterogeneity in direction and in different correlation regimes. EPU and MPU have homogenously negative effects on gold-stock correlations across various correlation regimes. Differently, in terms of oil-stock correlations, they exhibit more significant and stronger positive impacts in the medium and high correlation regime than in the low correlation regime. Gold can provide a better diversification for stock market risks than crude oil during the period of high level of economic uncertainty.

A new copula for modeling portfolios with skewed, leptokurtic and high-order dependent risk factors

The paper proposes a new copula for modeling higher-order dependencies between pairs of portfolio assets, employing orthogonal polynomials to model symmetric co-kurtoses. Skewness and leptokurtosis of portfolio margins are modeled either with the Gram–Charlier expansion of the Normal distribution or Gram–Charlier-like expansions of leptokurtic laws. Details on the estimation method of this copula are provided, and a simulation study is carried out to assess its potential range of applicability with respect to widely employed alternatives in the copula literature. Empirical evidence of the suitability of this approach to model financial data and compute risk measures is provided.

The Myth of Diversification Reconsidered

That investors should diversify their portfolios is a core principle of modern finance. Yet there are some periods in which diversification is undesirable. When the portfolio’s main growth engine performs well, investors prefer the opposite of diversification. An ideal complement to the growth engine would provide diversification when it performs poorly and unification when it performs well. Numerous studies have presented evidence of asymmetric correlations between assets. Unfortunately, this asymmetry is often of the undesirable variety: It is characterized by downside unification and upside diversification. In other words, diversification often disappears when it is most needed. In this article, the authors highlight a fundamental flaw in the way some prior studies have measured correlation asymmetry. Because they estimate downside correlations from subsamples in which both assets perform poorly, they ignore instances of successful diversification (i.e., periods in which one asset’s gains offset the other’s losses). The authors propose instead that investors measure what matters: the degree to which a given asset diversifies the main growth engine when it underperforms. This approach yields starkly different conclusions, particularly for asset pairs with low full-sample correlation. The authors review correlation mathematics, highlight the flaw in prior studies, motivate the correct approach, and present an empirical analysis of correlation asymmetry across major asset classes. <b>TOPICS:</b>Portfolio theory, portfolio construction, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ There is strong empirical evidence that asset class correlations are asymmetric, which poses complications in portfolio construction. ▪ Investors prefer diversification when a portfolio’s main growth engine performs poorly and unification when it performs well. ▪ To measure correlation asymmetry caused by nonnormality, investors must adjust for changes in correlation that arise mathematically when part of a sample is excluded. ▪ Unlike prior research, investors should condition correlations on the performance of a single asset, not two assets.

Multivariate bilateral gamma, copulas, CoSkews and CoKurtosis

Correlation graphs are introduced to delineate the levels observed in data and models for return and squared return correlations. A sample of 2048 representative pairs of equity assets is selected from a possible collection of [Formula: see text] pairs by quantization. Five copulas are estimated and simulated on these pairs of returns, the Gaussian, t-copula, Clayton, Gumbel and Frank. Additionally, the multivariate bilateral gamma (MBG) model that introduces dependence via common time changes is also fit and simulated. Results of fit statistics on returns, CoSkew and CoKurtosis pairs are reported. The general ordering of the models is MBG, t-copula, followed by the Gaussian, Frank, Gumbel and Clayton copulas.

The Myth of Diversification Reconsidered

That investors should diversify their portfolios is a core principle of modern finance. Yet there are some periods where diversification is undesirable. When the portfolio’s main growth engine performs well, investors prefer the opposite of diversification. An ideal complement to the growth engine would provide diversification when it performs poorly and unification when it performs well. Numerous studies have presented evidence of asymmetric correlations between assets. Unfortunately, this asymmetry is often of the undesirable variety: it is characterized by downside unification and upside diversification. In other words, diversification often disappears when it is most needed. In this article we highlight a fundamental flaw in the way that some prior studies have measured correlation asymmetry. Because they estimate downside correlations from subsamples where both assets perform poorly, they ignore instances of “successful” diversification; that is, periods where one asset’s gains offset the other’s losses. We propose instead that investors measure what matters: the degree to which a given asset diversifies the main growth engine when it underperforms. This approach yields starkly different conclusions, particularly for asset pairs with low full sample correlation. In this paper we review correlation mathematics, highlight the flaw in prior studies, motivate the correct approach, and present an empirical analysis of correlation asymmetry across major asset classes.