Hedge Ratios Research Articles

OPTIMAL HEDGE RATIO OF SUKUK AND ISLAMIC EQUITY: A NOVEL APPROACH

This research applies a novel model to compute a hedge ratio. Specifically, the model modifies volatility forecasts of an exponentially weighted moving average method to account for the fat-tailed distribution of returns. This simpler model aims to overcome the widely-known drawback of the complex GARCH models that a long daily return period is required to ensure the model’s convergence. The data are Islamic exchange-traded funds: SP Funds Dow Jones Global Sukuk ETF, Wahed FTSE USA Shariah ETF, and iShares MSCI EM Islamic UCITS ETF. Sukuk act as a diversifier over the turmoil period since they are positively correlated with Islamic equity and their volatility is less than that of Islamic equity. This work also implements widely-used methods such as Dynamic Equicorrelation-GARCH, GO-GARCH, asymmetric DCC-GARCH, naïve approach, and linear regression. Two forms of data splitting and a rolling-window analysis are carried out to reduce data mining bias. All models generate one-step ahead forecasts of hedge ratios. Applying wavelet-transformed returns and utility analysis incorporating third and fourth moments, the proposed models produce better performance than the competing models. The results remain the same irrespective of different hedging instruments (precious metals) and asset classes.

Hedging With Futures

This research explores the utilization of wavelet transform decomposition as an effective tool for hedging in the Indian stock market, particularly focusing on hedging with index futures contracts. Utilizing daily data obtained from the National Stock Exchange (NSE) of India spanning from 2010 to 2022, the study investigates the lead-lag relationships, correlations, and hedge ratios across different time scales through the wavelet transform method. The findings indicate a clear relationship between the Nifty 50, Nifty Bank, and Index futures in both short and longer time frames. However, in intermediate time scales, the Nifty Bank contract exhibits a leading position in the market. The correlation analysis underscores that time plays a crucial role in determining the variations, resulting in a wide range of correlations. The effectiveness of hedging, measured by the hedge ratio, displays an increasing trend across different time zones.

On the Time-varying Correlations and Hedging Effectiveness: An Analysis of Crude Oil, Gold, and Stock Market

In this paper, we examine the time-varying conditional correlation between international crude oil and gold prices, along with their volatilities, and Indian stock prices, using the DCC-GARCH model. The results suggest that the relationships between stock/oil, stock/gold, stock/oil volatility, and stock/gold volatility are time-varying. We then use the conditional variance and covariance derived from the DCC-GARCH model to measure the optimal portfolio weight and hedge ratios for stock/oil and stock/gold portfolios. Our findings indicate that, to minimize risk without reducing expected returns, investors should hold 37% and 49% of oil and gold, respectively, in their portfolios, relative to stocks. However, there is wide variation in the hedge ratios over time, suggesting that portfolio managers will need to rebalance portfolios frequently. Finally, we analyze correlation coefficients under various deciles of stock market returns, showing that gold can be used as a hedge against stock market declines. Given India's significant improvement in financial indicators, the results of this study will be useful for portfolio managers, risk managers, policymakers, and researchers.

Hedging Efficiency of Energy Commodities between Indian and American Commodity Exchanges: Constant and Time-Varying Approaches

This research paper investigates the hedging efficiency between spot and future prices of crude oil and natural gas commodities in the energy category of the commodity market. The study focuses on two major commodity exchanges, the Multi Commodity Exchange of India (MCX) and the New York Mercantile Exchange (NYMEX), which serve as the global benchmarking commodity market. Various econometric models have been incorporated to measure constant and time-varying hedging efficiency, we analyze the potential of futures contracts in mitigating price risks in these markets. The results indicate significant and consistent hedge ratios for both crude oil and natural gas in both the MCX and NYMEX markets. Moreover, the MGARCH model, which assesses hedging that varies over time, demonstrates the ability to adjust hedging positions based on market changes. Johansen's co-integration test endorses the presence of enduring connections between spot and future prices in both exchanges. Traders and investors can effectively use futures contracts to mitigate price risks in energy commodity markets, ensuring more dependable financial outcomes amid price fluctuations. Additionally, policymakers can utilize these research findings to promote the adoption of futures contracts, make well-informed choices, manage risks effectively, and enhance the overall efficiency and stability of energy commodity markets.

The influence of the spillover between futures and spot markets on hedging policy: evidence from Chinese stock markets

This paper examines the impact of risk spillovers between Chinese stock and futures markets on stock hedging policies. This paper calculates the correlation between the overall risk spillover and the hedging ratio, effectiveness, and hedging cost based on the hedging portfolio of stock futures of the CSI 300 index, the SSE 50 index, and the CSI 500 index. The results show that, on the one hand, the CSI 300 and CSI 500 futures markets are the propagators of risk spillovers, which shows that the price fluctuations of these two types of futures markets have a stronger impact on the stock spot price. The CSI 50 Futures market is the recipient of risk, and the CSI 50 Futures price is more vulnerable to the impact of the stock market. The results of the three types of samples show that when the stock market experiences extreme decline, the Risk Spillover Effect in the futures and spot markets will be significantly reduced, which indicates that the price of stocks and their derivatives is more deviated from the equilibrium relationship of pricing, and at this time, the function of derivatives in risk control is unreliable. On the other hand, the total spillover index of stock futures and spot markets has a positive correlation with hedging rate, hedging effectiveness and hedging cost. When the total spillover real number increases, it will get a more effective hedging effect. In addition, the hedging index is more sensitive to the overall spillover of high asset liquidity hedging portfolios. These conclusions provide a basis for adjusting asset positions according to the changes of Risk Spillover between the futures market and the spot market to obtain higher hedging effectiveness. The contribution of this paper is to reveal the possibility of finding the risk factors of hedging from the perspective of Risk Spillover in a very simple way, so it has reliable practicability in practice.

Behavioural explanations of Expectile VaR forecasting and dynamic hedging strategies for downside risk during the COVID‐19 pandemic: Insights from financial markets

AbstractIn this paper, we investigate the influence of confirmation bias on Expectile Value at Risk (EVaR) forecasting among fundamentalist, optimistic, and pessimistic investors in cryptocurrency, commodity, and stock markets before and during the COVID‐19 pandemic. Utilizing the DCC‐range GARCH model, we also explore the conditional minimum downside risk hedge ratios. Our findings demonstrate that confirmation bias leads to excessive EVaR for financial market returns, regardless of the period being before or during COVID‐19. Moreover, fundamentalists' expectations in all markets remain constant, while without confirmation bias, optimists' and pessimists' expectations tend to converge to zero over time but diverge significantly during turbulent periods. When confirmation bias is present, the average distance between these expectations widens. Analysing the hedge ratio results, with or without confirmation bias, also unveils the conditional minimum downside risk hedge ratios. These ratios indicate the optimal proportions for hedging downside risk in each financial market during different periods. We find that the conditional minimum downside risk hedge ratios are generally lower (higher) during the pre‐COVID‐19 (COVID‐19) period, implying that hedging costs are higher during the COVID‐19 period. These insightful findings offer valuable insights for traders and regulators in identifying and understanding the risk conditions of cryptocurrency, commodity, and stock markets. Additionally, the analysis of conditional minimum downside risk hedge ratios provides investors with essential information on how to strategically position their portfolios to mitigate and manage risk during both tranquil and turbulent market conditions, with and without confirmation bias.

Foreign exchange hedging using regime‐switching models: The case of pound sterling

AbstractWe develop a four‐state regime‐switching model for optimal foreign exchange (FX) hedging using forward contracts. The states correspond to four distinct market conditions, each defined by the direction and magnitude of deviation of the prevailing FX spot rate from its long‐term trends. The model's performance is evaluated for five currencies against the pound sterling for various horizons. Our examination compares the hedging outcomes of the proposed model to those of other commonly employed hedging methods. The empirical results suggest that our model demonstrates the highest level of risk reduction for the US dollar, euro, Japanese yen and Turkish lira and the second‐best performance for the Indian rupee. The risk reduction is significantly higher for lira compared with the other approaches, implying that the proposed model might be able to provide much more effective hedging for highly volatile currencies. The improved performance of the model can be attributed to the adjustability of the estimation horizon for the optimal hedge ratio based on the prevailing market conditions. This, in turn, allows it to better capture fat‐tail properties that are frequently observed in FX returns. Our findings suggest that FX investors tend to use short‐term memory during low market conditions (relative to trend) and long‐term memory in high ones. The results would be also useful to build a better understanding of how investor behaviour depends on market conditions and mitigate the adverse behavioural implications of short‐term memory, such as panic.

Optimizing portfolios for the BREXIT: An equity-commodity analysis of US, European and BRICS markets

The objective of this study is to create optimal two-asset portfolios consisting of stocks from Western Europe, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa), as well as sixteen commodity types during the BREXIT period. We utilized dynamic variances and covariances from the GARCH model to derive weights for the two-asset portfolios, with each portfolio consisting of one equity factor and one commodity factor. Subsequently, hedge ratios were calculated for these various assets. Our findings indicate that portfolios consisting of European stocks do not require the inclusion of commodities, whereas the other equities do.

Hedging with futures during nonconvergence in commodity markets

Hedging in grain futures markets offers market participants the opportunity to mitigate the price risk in spot markets by taking offsetting positions in futures. The performance of a traditional minimum variance hedge ratio (MVHR) relies on the correlation between the spot and futures price changes. During 2005–2010, delivery-location cash prices for several crops decoupled from the prices for their related expiring futures contracts—raising concerns over the hedging effectiveness of these contracts. We investigate how short hedgers, like farmers, performed during periods with and without convergence in corn, soybean, and wheat markets. We show that, ex post, MVHR, often does not minimize the variance of wheat producers’ profits during nonconvergence when compared to a range of other hedging choices. We also find that the performance of MVHR weakens during years with low carryover. We further assess hedging performance of MVHR and other hedge ratios in achieving higher net selling prices, and find that nonconvergence particularly impairs their performance in the wheat market where the nonconvergence anomaly was the most prominent. Taken together, our results raise questions on the role of futures markets as risk management tools during nonconvergence episodes regardless of how the hedge ratio is chosen.

Examining the hedge performance of US dollar, VIX, and gold during the coronavirus pandemic: Is US dollar a better hedge asset?

This study utilizes the hedging potential of the U.S. Dollar Index (USDX) during the COVID-19 period, specifically comparing its positive effects on optimal portfolio weights and hedging ratios with those of traditional hedging assets, such as the VIX and gold. The scalar BEKK GARCH model is employed to forecast volatility and calculate hedging indicators. The results show that USDX exhibits strong hedging abilities against S&P 500 index volatility. These findings highlight the advantageous role of the USDX as a hedging instrument, particularly during periods of heightened market uncertainty, such as during the COVID-19 crisis. Despite the increased market volatility during the COVID-19 pandemic, the value of the optimal portfolio weights is stable and the volatility of the weights is significantly reduced, demonstrating the strength of the USDX's low risk and volatility in hedging against market fluctuations. Moreover, the increase in the hedge ratio indicates that more capital is allocated to hedging, reflecting the increased correlation between the USDX and S&P 500 index. These results emphasize the beneficial role of the USDX as a hedging instrument during times of elevated market uncertainty, such as during the COVID-19 crisis. Ultimately, USDX can provide valuable insights for market participants seeking effective hedging strategies.

Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China

In the structure of energy consumption of China, coal and oil rank the top and the second place. It is obviously important to better understand the dynamics of the price volatility between crude oil and coal in China, and then to build a reasonable energy portfolio. As the listing of China's crude oil futures offers a new sample to study the volatility contagion of oil and coal prices, by using daily data from 2nd May 2018 to January 23, 2020, four different multivariate GARCH models are compared and contrasted in this paper. Then, based on the results of the four MGARCHs, we investigate the oil-coal portfolios' optimal weights and hedge ratios. The results indicate that, first, a long-run co-integration relationship exists in China's crude oil and coal. Second, in short run, futures price of crude oil leads that of coal, the volatility spillover effect of crude oil on coal is stronger than that of coal on crude oil. Third, the DCC-GARCH model fits best for data. Last, weights of optimal portfolio and ratios of hedge show that the crude oil is a tool to make the risk of portfolio minimal in energy markets. These results are expected to have implications for energy producers, users and trading enterprises to inform their decision-making.

Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures

Considering the significance of the frequency domain information in price series, we propose a decomposition-integration hedging model from the perspective of time–frequency domain to improve the hedging effectiveness and robustness. First, the returns series of spot and futures are decomposed and reconstructed into high frequency, medium frequency, and low frequency sequences to incorporate the frequency information into the hedging model. Then, we construct a model pool to fit the various characteristics of the data in each frequency domain and further extract the corresponding information. Finally, by taking four moments of investors risk appetite into consideration, the multi-objective optimization is employed to integrate the final hedging ratio. The empirical results indicate that the decomposition-integration hedging model is superior to the single time domain models in comprehensive evaluation indices. In addition, within a certain range of market fluctuations, the structure of our model has some characteristics that are compatible with reality. To recapitulate briefly, the hedging ratio of our model is mainly determined by the high frequency and medium frequency information under drastic market fluctuations. While when the market volatility is small, the hedging ratio largely depends on low frequency information.

Dynamic connectedness between energy markets and the Brazilian cash market: An empirical analysis pre‐ and post‐COVID‐19

AbstractBrazil's significant commodity production is internationally recognized, yet the absence of a mature futures market exposes it to price risks and international shocks. This study explores the dynamic connectedness between commodity futures and the Brazilian cash markets, using a time‐varying parameter vector autoregressive model. We also assess COVID‐19's impact on this connectedness. We find a significant influence of oil prices on Brazilian ethanol prices, and particularly emphasize the Heating Oil spillover effect on ethanol in the post‐COVID‐19 era. We also note the ascension of Brazilian soybean spot markets' international significance since 2017, amplifying their role in global grain price discovery. Finally, by computing hedge ratios and effectiveness between commodity futures contracts and Brazilian spot prices, our study reveals soybean cash price as the most effective hedge. These insights deepen comprehension of connectedness within Brazilian commodity markets, thereby guiding investors and policymakers in strategic energy policy decisions.

Can diversification be improved by using cryptocurrencies? Evidence from Indian equity market

PurposeThis study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war.Design/methodology/approachResearchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model.FindingsThe findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis.Originality/valueThis study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.

Network connectedness and portfolio hedging of green bonds, stock markets and commodities

PurposeThis study aims to investigate the impact of the COVID-19 pandemic on the time-frequency connectedness between green bonds, stock markets and commodities (Brent and Gold), with a particular focus on China and its implication for portfolio diversification across different frequencies.Design/methodology/approachTo this end, the authors implement the frequency connectedness approach of Barunik and Krehlik (2018), followed by the network connectedness before and during the COVID-19 outbreak. In particular, the authors implement more involvement in portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness for green bonds and other financial assets.FindingsThe time-frequency domain spillover results show that gold is the net transmitter of shocks to green bonds in the long run, whereas green Bonds are the net recipients of shocks, irrespective of time horizons. The subsample analysis for the pandemic crisis period shows that green bonds dominate the network connectedness dynamic, mainly because it is strongly connected with the SP500 index and China (SSE). Thus, green bonds may serve as a potential diversifier asset at different time horizons. Likewise, the authors empirically confirm that green bonds have sizeable diversification benefits and hedges for investors towards stock markets and commodity stock pairs before and during the COVID-19 outbreak for both the short and long term. Gold only offers diversification gains in the long run, while Brent does not provide the desired diversification gains. Thus, the study highlights that green bonds are only an effective diversified.Originality/valueThis study contributes to the existing literature by improving the understanding of the interconnectedness and hedging opportunities in short- and long-term horizons between green bonds, commodities and equity markets during the COVID-19 pandemic shock, with a particular focus on China. This study's findings provide more implications regarding portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness.

The Dynamic Return and Volatility Spillovers among Size-Based Stock Portfolios in the Saudi Market and Their Portfolio Management Implications during Different Crises

This study contributes to the ongoing debate on the size effect and size-based investment styles by investigating the return and volatility spillovers and time-varying conditional correlations among Saudi large-, mid-, and small-cap indices. To this end, we utilize the weekly returns on the MSCI Saudi large-, mid-, and small-cap indices over a long sample period, spanning several crises. The econometric approach that we use is a VAR-asymmetric BEKK-GARCH model which accounts for structural breaks. On the basis of the VAR-asymmetric BEKK-GARCH model estimation results, we calculate portfolio weights and hedge ratios, and discuss their risk management implications. The empirical results confirm the presence of unilateral return spillovers running from mid- to small-cap stocks, while multilateral volatility spillovers are documented, albeit substantially weakened when accounting for structural breaks. The time-varying conditional correlations display clear spikes around crises, which translate to higher hedge ratios, increasing the cost of hedging during turbulent times. The optimal portfolio weights suggest that investors generally overweight large caps in their portfolios during uncertain times to minimize risk without lowering expected returns. The main takeaway from our results is that passively confining fund managers to a particular size category regardless of the prevailing market conditions may lead to suboptimal performance.

Dynamic correlations and volatility spillovers between subsectoral clean‐energy stocks and commodity futures markets: A hedging perspective

AbstractThis study investigates the time‐varying connectedness between subsectoral clean‐energy stocks and fossil fuel energy commodities (crude oil, natural gas, and coal) over the period of December 2013–January 2023 employing the Diebold and Yilmaz approach and the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model. According to the findings, oil transmits the highest volatility spillover shocks to biofuels, and the least to the fuel cell industry. Both natural gas and coal transmit the highest volatility spillover shocks to energy storage, and the least to geothermal and green information technology, respectively. The study also finds strong and time‐varying volatility connectedness among clean‐energy assets and fossil fuels, significantly affected by global extreme events, such as the COVID‐19 pandemic and the Russia–Ukraine conflict. Additionally, the study provides time‐varying and mean optimal hedge ratios with optimal portfolio weights for investors. The empirical results are robust, and important portfolio and policy implications based on empirical findings are provided.

Hedging Strategies in Carbon Emission Price Dynamics: Implications for Shipping Markets

The European Union (EU) has agreed to gradually include shipping in the EU emissions trading scheme (EU ETS), which makes shipping companies vulnerable to carbon price fluctuations. The aim of this paper is to investigate the effectiveness of carbon and petroleum futures contracts in managing carbon and bunker risks. We examine the effectiveness of alternative hedging methods, including both static and dynamic approaches, to estimate optimal hedge ratios under single and composite cross-hedge settings. Our results show that carbon future contracts are important for hedging the carbon emission allowances price risk, and Brent oil futures are the most effective instrument for out-of-sample hedging of bunker prices. In addition, the hedging effectiveness indicates that conventional methods outperform the sophisticated models in terms of variance reduction. Our study offers new insights into how the carbon and bunker markets relate to a combination hedging in reducing the joint price risk, which can be used to promote risk management in the market.

Connectedness of non-fungible tokens and conventional cryptocurrencies with metals

Employing the vector auto-regression based on generalized forecast error variance decomposition, this paper investigates the connectedness of non-fungible tokens (NFTs) with precious and industrial metals and compares the results with those for conventional cryptocurrencies (CCCs). Our study scrutinizes separately the total static and the net dynamic spillovers of returns and volatilities from March 2018 to August 2021. We evidence that both, the total return and total volatility connectedness indices for the NFTs-metals framework are below the respective indices for the CCCs-metals framework, indicating new avenues for hedging and harvesting diversification benefits of NFT exposures. We provide empirical evidence that the NFTs are distinct from the CCCs not only in terms of the volatility spillovers, but in terms of the return spillovers too. In addition, we observe the decoupling in the net volatility spillovers between the precious and non-precious metals due to the COVID-19 meltdown. COVID-19 makes precious metals transmit volatility while industrial metals continue acting as net receivers of volatility shocks. Optimal weight and hedge ratios are presented for NFT-metal and crypto-metal pairs. These findings provide potential implications for investors and policy makers.

Which Commodity Sectors Effectively Hedge Emerging Eastern European Stock Markets? Evidence from MGARCH Models

This study aims at examining whether hedging emerging Eastern Europe stock markets with commodities sectors can help in reducing market risks and whether it has the same effectiveness among different sectors. As an attempt to achieve this goal, we opt for three types of MGARCH model. These are DCC, ADCC and GO-GARCH, which are used with each bivariate series to model dynamic conditional correlations, optimal hedge ratios and hedging effectiveness. Rolling window analysis is used for out-of-sample one-step-ahead forecasts from December 1994 to June 2022. The results have shown that the commodities sectors of industrial metals and energy represent the optimal hedging instruments for emerging Eastern Europe stock markets as they have the highest hedging effectiveness. Additionally, our empirical results have proved that hedge ratios estimated by the DCC and ADCC models are very similar, which is not the case for GO-GARCH, and that hedging effectiveness is preferably estimated by the ADCC model.