Hedge Ratios Research Articles

Sustainable Synergy: Static and Dynamic nexus between ESG and BRICS Equity Markets

Sustainable Synergy: Static and Dynamic nexus between ESG and BRICS Equity Markets

Unlocking the diversification benefits of DeFi for ASEAN stock market portfolios: a quantile study

This study examines the return connectedness between decentralized finance (DeFi)’s and the Association of Southeast Asian Nations (ASEAN) stock markets using the quantile vector autoregressive framework, which allows us to investigate the connectedness at conditional quantiles. Our sample includes four major DeFi’s and six ASEAN stock markets, spanning from March 2018 to December 2022. The static results indicate a moderate level of return transmission between the system at mean and median quantile. This propagation increases substantially under extreme market conditions, establishing an asymmetric transmission across quantiles. Despite being a relatively new asset class, DeFi dominates the equity market and acts as the primary shock transmitter to the system in most instances. The dynamic analysis reveals that total system connectedness fluctuates over time and quantiles. The total system connectedness peaked during the COVID-19 and the Russia–Ukraine conflict period, indicating the impact of global events on system transmission. The optimal weight and hedge ratio estimated using the DCC-GARCH model indicate that DeFi is beneficial for portfolio construction and risk management. The rising trend in dynamic optimal weight and hedge ratio during the COVID-19 pandemic demonstrates that investors should decrease their investments in DeFi and increase hedging costs. Therefore, portfolio managers and investors should readjust their portfolio allocation in a timely manner according to different market states to build additional effective hedging and diversification strategies to avoid large losses and to reduce portfolio risk exposure.

Exploring environmental and managerial impacts of crude oil volatility on QUAD financial markets: Insights into dynamic connectedness.

The QUAD nations' (United States, India, Japan, and Australia) financial markets are deeply integrated with global markets. The burgeoning surge of risk in global markets requires caution, which can be possible by determining the connectedness. This study elucidates the dynamic connectedness between crude oil and the financial markets of QUAD nations, aiming to enhance understanding of its environmental and managerial implications. This study uses Diebold and Yilmaz (2012) model for the empirical investigation. After investigation the connectedness, portfolio weight, and hedge ratio are determined to know the hedge ratio. It is found that the Japanese market and crude oil emerge as the least transmitter and least receiver of the shock, respectively. Additionally, the result reveals that hedging is most expensive in the US market while cheapest in the Australian market. This paper contributes to understanding the influence of energy market fluctuations on financial markets amidst recent global crises, such as the COVID-19 pandemic and the Russia-Ukraine conflict, with implications for both environmental and economic resilience.

Adaptive Modeling and Risk Strategies for Cross-Border Real Estate Investments

In this study, we examine predictive modeling and risk management approaches tailored for cross-border real estate investments, with a focus on adapting models to stable and volatile market conditions. Drawing on an extensive dataset spanning diverse economic environments, we evaluate the performance of linear, polynomial, and logarithmic models in capturing real estate price dynamics. Findings indicate that the linear model provides reliable accuracy in stable markets (R2=0.98), aligning well with predictable, incremental trends typical of such environments. However, in markets marked by high volatility, the polynomial model outperforms, effectively capturing non-linear fluctuations with R² values of 0.89, thus providing a more robust framework for regions subject to economic and political shifts. To address currency risk and extreme loss potential, we integrate Conditional Value-at-Risk (CVaR) and Dynamic Optimal Hedge Ratio (DOHR) methodologies. These approaches collectively reduce return volatility by approximately 15% in volatile markets, enhancing stability in high-risk environments. Furthermore, the analysis underscores the strategic value of Environmental, Social, and Governance (ESG) alignment, particularly in fostering regulatory support and community acceptance, which are vital for long-term investment sustainability. Our findings suggest a tailored strategy: linear models with simplified risk management are well-suited for stable markets, while volatile markets benefit from polynomial models paired with advanced risk measures. Prioritizing ESG-compliant projects further mitigates regulatory and reputational risks. These insights provide a foundation for optimizing investment strategies across varied economic landscapes, with future work recommended to explore adaptive machine learning techniques for real-time model adjustments.

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.

Hedging downside risk in agricultural commodities: A novel nonparametric kernel method

Hedging downside risk in agricultural commodities: A novel nonparametric kernel method

Connectedness between energy cryptocurrencies and US equity markets: A quantile-based analysis

Connectedness between energy cryptocurrencies and US equity markets: A quantile-based analysis

Dynamic DeFi-G7 stock markets interactions and their potential role in diversifying and hedging strategies

AbstractThis study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.

Comparative analysis of futures contract cross-hedging effectiveness for soybean: models and insights

ABSTRACT The purpose of this research is to investigate the use of cross-hedging in the South African soybean market, by using the JSE BEAN contract as a cross-hedge instrument for the JSE SOYA contract. The research study involves a detailed assessment of the data, which includes the use of stationarity tests such as the Phillips-Perron and Augmented Dickey-Fuller, as well as the Johansen co-integration approach to analyse long-term relationships between the variables. To evaluate the efficiency of cross-hedging strategies, three distinct hedging models are used: the ordinary least squares (OLS) model, error correction model (ECM) and EC-GARCH. The results demonstrate that these models produce significant hedge ratios. Through back-testing and the application of hedging strategies, it is determined that the JSE BEAN contract holds the potential to serve as a cross-hedge for the JSE SOYA contract in the South African soybean market. This study provides useful information for market participants and model selection in the context of cross-hedging.

Harmony in diversity: Exploring connectedness and portfolio strategies among crude oil, gold, traditional and sustainable index

Harmony in diversity: Exploring connectedness and portfolio strategies among crude oil, gold, traditional and sustainable index

Contagion effect between fuel fossil energies and agricultural commodity markets and portfolio management implications

Contagion effect between fuel fossil energies and agricultural commodity markets and portfolio management implications

Exploring the Effectiveness of Hedging in the Indian Commodity Market: A Comparative Analysis of Constant and Dynamic Hedge Ratios Across Agricultural and Non-Agricultural Commodities

Exploring the Effectiveness of Hedging in the Indian Commodity Market: A Comparative Analysis of Constant and Dynamic Hedge Ratios Across Agricultural and Non-Agricultural Commodities

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.

Research on the effectiveness of hedging on SSE 50 stock index futures

Stock index futures is an important investment product, which gives financial market participants a new way to manage risks, especially in the case of investors holding more spot positions, the risk avoidance function of stock index futures is more prominent. Therefore, this paper to the optimal hedging ratio of stock index futures as the research focus, the above card 50 stock index futures main contract (code: IHL 8) as the research object, on this basis, using OLS and VAR model, for the SSE 50 index (code: 000016), and use the effectiveness of hedging index He to evaluate the effectiveness of the hedge, so as to get the most effective hedging model. Through the empirical research in this paper, it is found that the hedge effect obtained by using the VAR model is better than that of the traditional OLS model. By using the stock index futures, the market participants can effectively avoid the risk of the spot market.

Research on Hedging Ratio of Stock Index Futures to ETF Fund

Exchange Traded Funds (ETF) can largely avoid non-systematic risk, but investors often cannot avoid systemic risk, and the hedging function of stock index futures can do this. Therefore, hedging ETF with stock index futures has become a good investment strategy. Based on the trading data of the China Securities Index (CSI) 300 stock index futures and CSI 300 Exchange-Traded Fund (ETF), this paper analyzes the optimal hedging ratio of CSI 300 stock index futures by Ordinary Least Squares (OLS) and dynamic Error Correction Model - Generalized Autoregressive Conditional Heteroskedasticity (ECM-GARCH) model and compares the hedging performance predicted by the model to avoid the systematic risk of ETF. The results show that the hedging effect of the dynamic hedging model is better than that of the static model, and the ability to avoid systemic risk is also better. The predictions of both models show that stock index futures hedge ETFs very well. The dynamic model is more able to reduce the heteroscedasticity of the transaction data.

Non-fungible tokens and metal markets: time-varying spillovers and portfolio implications

ABSTRACT The linkage between the metal market and crypto assets is a topic of concern. Utilizing a novel TVP-VAR framework, this study examines the volatility transmission between NFTs and precious/industrial metals. The results show strong connectivity, with most NFTs being net transmitters, and industrial metals being mostly net spillover recipients. Moreover, the connectivity was affected by the COVID-19 epidemic and the Russia-Ukraine War. Considering the potential reference value that this empirical fact may bring to market participants, this paper divides the time samples and uses the DCC-GARCH t-copula method to analyse the portfolio construction of every sub-sample. The research investigates and compares the hedge ratio, optimal weights, and hedging effectiveness of NFT-industrial metals and NFT-precious metals. These findings can bring potential inspiration to investors, market regulators, and policymakers.

Comparative Analysis of Gold, Bitcoin and Gold-backed Cryptocurrencies as Safe Havens During Global Crises: A Focus on G7 Stock Market and Banking Sector Indices

In this article, we attempt to analyze and compare the safe-haven features of gold, Bitcoin and gold-backed cryptocurrency against the stock and banking indices of G7 countries during the outbreak of adverse events. To do so, we examine dynamic relationships between different assets and we compute optimal hedge ratios for different couples using the corrected Asymmetric Dynamic Conditional Correlation-Exponential Generalized Autoregressive Conditional Heteroscedasticity and corrected Asymmetric Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity models. We clearly show that gold and gold-backed cryptocurrency maintain higher weights in optimal portfolios compared to Bitcoin. We also report that shocks due to unexpected events increasingly affect dynamic correlations, asset weights and hedge ratios. This underscores the need for regular demand for rebalancing the hedge positions and effective risk management. We thereafter show that the relationship between Bitcoin (gold-backed cryptocurrency) and G7 indices is highly affected by the outbreak of COVID-19 pandemic. Such findings highlight the hedging and safe-haven features of different asset classes against stock markets. They could have insightful implications for investors who want to minimize investment risks and policymakers who are worried about the financial consequences of different unexpected events.

Carbon Emissions Pricing: Linkages Between EU ETS Spot and Future Prices and Completeness of EU ETS Market

The study examines the relationship and linkages between spot and future prices of European Union Emission Trading Systems (EU-ETS) during 2019–2021. Dynamic conditional correlation specification of multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model was employed to examine the time-varying correlation between spot and future prices. Also, vector autoregression mean model and MGARCH-Baba-Engle-Kroner-Kraft volatility model was jointly estimated to model the spillover between EU ETS spot and future prices in the first and second moments. Lastly, we utilize the variance-covariance matrix of joint mean-variance model estimation to derive the optimal conditional hedge ratio as well as the hedge effectiveness of EU ETS future contracts. Our findings reveal a high conditional correlation and significant spillover between carbon spot and future markets in EU. Further, our study uncovers a high degree of hedge effectiveness for EU ETS future contracts. This is possibly the first study that examines the linkages between EU ETS spot and future prices pertaining to the recent transition stage of phase III and the initial stage of ongoing phase IV of the ETS market. Our findings pinpoint to ETS markets becoming more complete and in turn offering optimal hedging avenues. JEL Codes: G15, C58, Q38

Assessing Bitcoin, gold and gold-backed cryptocurrencies as safe havens for energy and agricultural commodities: insights from COVID-19, Russia–Ukraine conflict and SVB collapse

PurposeThis paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the Russia–Ukraine conflict and the Silicon Valley Bank (SVB) collapse.Design/methodology/approachThe authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the Russian–Ukrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our results’ robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities.FindingsThe results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run.Originality/valueThis paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the Russia–Ukraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets.

Employment of the DCC-GARCH Copula Model to explore a link between robotics and artificial intelligence and green crypto investments.

The pursuit of the Sustainable Development Goals (SDGs) requires considerable new green crypto investments. To attract the flow of this investment, it is necessary to develop and apply robotic artificial intelligence (AI) as it has the potential to encourage the adoption of environmental innovation and increase individuals' environmental awareness. Our research employs the DCC-GARCH Copula Model to examine time-varying spillovers and prove interlinkages between the development of AI and green cryptocurrencies in the period from January 1, 2018, to September 8, 2023. Comparing the optimum hedge ratios with the optimal portfolio weights, we demonstrate that the optimal hedge strategy for BOTZ is the most successful one. However, the success of hedging depends on the portfolio's risk profile. Based on our analysis of the cumulative profit profile of different approaches, we continue to believe that the best portfolio weighting strategy is the one that produces positive returns in the middle of 2020 and the first part of 2022 and 2023. This demonstrates that the most profitable diversification approach is not always the most successful one. Our results have important policy implications for investors and governments.