Hedge Ratios Research Articles

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.

The dynamic connectedness between collateralized loan obligations and major asset classes: a TVP-VAR approach and portfolio hedging strategies for investors

Motivated by the increasing demand for alternative assets that can contribute to reducing portfolio risk, this paper examines the volatility spillovers between collateralized loan obligations (CLOs) and various in-demand investment instruments, including equities, bonds, crude oil, commodities, gold, bitcoin, shipping and real estate. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) modification of the classical spillover approach, for the period from January 1, 2012, to August 31, 2023. The empirical findings show moderate levels of dynamic connectedness; albeit several external shocks strengthened the interconnection among the assets. Moreover, we compare the ability of CLOs for hedging, during the overall sample period and multiple subperiods, by estimating hedge ratios and optimal portfolio weights, in order to inform investors about feasible portfolio adjustments. Our results indicate that CLOs constitute an effective hedging tool, irrespective of the period covered, as the short position in their volatility provides high hedging effectiveness for investors holding long positions in the volatility of all the remaining assets.

Interplay of crises: Unpacking intraday spillovers in oil and European equities in the shadow of the COVID-19 and the Ukraine-Russia war

This study investigates the time-varying frequency of spillovers between European stock markets and oil during the COVID-19 pandemic and the Russia-Ukraine war. Using the spillover index by Diebold & Yilmaz, 2012 and Baruník & Křehlík, 2018, we analyze high-frequency data at a 5-min interval to analyze the interplay between crude oil market returns and the Stoxx 600 index returns, including sectors such as auto, basic material, banks, chemicals, food and beverage, health, industrials, insurance, oil and gas, retail, real estate, technology (tech), telecommunication (telecom), and utilities. The sample period is January 3, 2022, to March 25, 2022. Our findings reveal a substantial degree of connectedness within this financial network, with specific sectors—auto, chemicals, food, industrials, insurance, real estate, retail, tech, and telecom—acting as net transmitters of shocks, while other sectors assume the role of net receivers. The Russia-Ukraine war is a significant driver of these interconnected dynamics. Spillovers are most prevalent in the medium-term horizon, with the time dimension affecting the role of sectors and oil. Furthermore, our research highlights the potential benefits of adding oil assets to portfolios, enhancing risk-adjusted performance. However, ongoing risk management remains crucial due to the dynamic hedge ratios. This study offers practical insights for investors and policy makers, aiding risk management and investment strategies in turbulent markets.

Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?

Our study analyzes the dynamic spillover effects between the NFT market, Bitcoin market, oil market, gold futures market, S&P 500 stock index, bond market, and US dollar index over three time periods: the full sample period (2017/6/23–2022/9/2), before the COVID-19 outbreak (2017/6/23–2020/1/12), and after the COVID-19 outbreak (2020/1/13–2022/9/2) by employing the DCC-GARCH-based connectedness model. Furthermore, we apply the DCC-GARCH-t-Copula model, Risk Parity Portfolio (RPP) model, and Minimum Connectedness Portfolio (MCoP) model to evaluate the bilateral dynamic hedge ratios, portfolio weights, and the multivariate portfolio performance of these financial assets, respectively. Our findings suggest that NFTs are a source of volatility spillovers during both periods and that the COVID-19 outbreak accelerates the speed and intensity of volatility spillovers from NFTs to traditional financial markets. Moreover, most of the volatility spillovers from NFTs are caused by own shocks, which implies that NFTs are not susceptible to financial risk contagion. The results of bilateral portfolio analysis show that NFTs can effectively hedge other traditional financial assets when used as long positions. The results of multivariate portfolio analysis demonstrate that NFTs can also reduce investment risk despite their smaller weighting. In the two months after the COVID-19 outbreak, MCoP slightly outperforms RPP, although RPP outperforms MCoP in general.

Research on dynamic behavior hedging of corn price risk based on the LA-DCC-GARCH model under COVID-19

The ravages of COVID-19 have had a huge impact on the global economy, and in the grain market, grain prices have fluctuated greatly due to the impact of factors such as rising circulation costs and sudden panicky demand, which has increased the business risks of sellers and deep-processing enterprises. To effectively reduce and avoid the risks brought by COVID-19, this paper takes utility maximization as the goal and helps grain enterprises manage grain price risks by constructing optimal dynamic behavioral hedging strategies. Therefore, this paper first measures the corn price risk during COVID-19, and then, fits the corn spot and futures yield fluctuations based on the DCC-GARCH model, which highlights the model’s advantages for portraying the corn spot and futures price fluctuations during COVID-19. Subsequently, the dynamically optimal hedging ratio is solved from the perspective of behavioral hedging by combining the loss aversion (LA) utility function. Finally, the LA-DCC-GARCH model is compared with the Minimum variance (MV)-DCC-GARCH model in terms of hedging performance, which highlights the conclusion that the behavioral hedging strategy significantly outperforms the traditional hedging strategy.

Optimal hedging efficiency in global freight markets: Comparing FFAs and time charter strategies

The paper aims to mitigate financial risk in highly volatile shipping freight markets by employing a dynamic hedging model. The primary criterion for evaluating the effectiveness of various methods for estimating optimal hedge ratios through Forward Freight Agreements (FFAs) is the minimum variance hedging rule. Four different methods are utilized to estimate two types of hedge ratios. The first type, a static hedge ratio, is calculated using the OLS and ECM methods. The second type, a time-varying hedge ratio, is determined through a bivariate GARCH model and a Rolling Window OLS method. Additionally, the hedging effectiveness of the traditional method of hedging, time chartering, is compared to the more modern and sophisticated shipping derivatives methods using the coefficient of variation. Highlights Estimating optimal hedge ratios through Forward Freight Agreements (FFAs) with the minimum variance hedging rule Four different methods are utilized to estimate two types of hedge ratios for static and dynamic hedging Assess the hedging effectiveness of the different methods of hedge ration estimation Compare the hedging effectiveness of the traditional method of hedging, time chartering, to the more modern and sophisticated shipping derivatives methods using the coefficient of variation

Can fiat currencies really hedge Pax gold? Evidence from range-based DCC models

PurposeThis paper assesses the co-movement between Pax gold and six fiat currencies. It also investigates the optimal time-varying hedge ratios in order to examine the properties of Pax gold as a diversifier and hedge asset.Design/methodology/approachThis paper examines the volatility spillover between Pax gold and fiat currencies using the framework of wavelet analysis, BEKK-GARCH models and Range DCC-GARCH. Moreover, this paper proposes to use the covariance and variance structure obtained from the new range DCC-GARCH framework to estimate the time-varying optimal hedge ratios, the optimal weighs and the hedging effectiveness.FindingsWavelet coherence method reveals that, at low frequency, large zone of co-movements appears for the pairs Pax gold/EUR, Pax gold/JPY and Pax gold/RUB. Further, the BEKK results show unidirectional (bidirectional) transmission effects between Pax gold and EUR, GBP, JPY and CNY (INR, RUB) fiat currencies. Moreover, the Range DCC results show that the Pax gold and the fiat currency returns are weakly correlated with low coefficients close to zero. Thus, Pax gold seems to serve as a safe haven asset against the systematic risk of fiat currency markets. In addition, the results of optimal weights show that rational investor should invest more in Pax gold and less in fiat currencies. Concerning the hedge ratios results, the findings reveal that the INR (JPY) fiat currency appears to be the most expensive (cheapest) hedge for the Pax-gold market. However, the JPY’s fiat currency appears to be the cheapest one. As for hedging effectiveness results, the authors found that hedging strategies including fiat currencies–Pax gold pairs are most likely to sharply decrease the portfolio’s risk.Practical implicationsA comprehensive understanding of the relationship between Pax Gold and fiat currencies is crucial for refining portfolio strategies involving cryptocurrencies. This research underscores the significance of grasping volatility transmissions between these currencies, providing valuable insights to guide investors in their decision-making processes. Moreover, it encourages further exploration into the interdependencies of digital currencies. Additionally, this study sheds light on effective contagion risk management, particularly during crises such as Covid-19 and the Russia–Ukraine conflict. It underscores the role of Pax Gold as a safe-haven asset and offers practical guidance for adjusting portfolios across various economic conditions. Ultimately, this research advances our comprehension of Pax Gold’s risk-return profile, positioning it as a potential hedge during periods of uncertainty, thereby contributing to the evolving literature on cryptocurrencies.Originality/valueThis study’s primary value lies in its pioneering empirical examination of the time-varying correlations and scale dependence between Pax Gold and fiat currencies. It goes beyond by determining optimal time-varying hedge ratios through the innovative Range-DCC-GARCH model, originally introduced by Molnár (2016) and distinguished by its incorporation of both low and high prices. Significantly, this analysis unfolds within the unique context of the Covid-19 pandemic and the Russian–Ukrainian conflict, marking a novel contribution to the field.

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.

Contingent Claims and Hedging of Credit Risk with Equity Options

Abstract Using contingent-claims valuation, we introduce novel hedge ratios for credit exposures using put options. Option hedge ratios are generally in line with the empirical sensitivities of credit spread changes to put option returns and, relative to stock hedge ratios, produce further reductions in volatility for a portfolio of North American firms. We show that option hedge ratios capture option-specific credit exposure related to the VIX index and the default spread, which is unaccounted for by Merton’s (1974) equity hedge ratios alone. Combining stocks and put options for credit risk hedging can be done effectively using the volatility smirk. (JEL E43, E44, G10)

Extreme Connectedness Across Chinese Stock and Commodity Futures Markets

This study examines the price spillovers and connectedness among stock markets, the Shanghai Stock Exchange Composite (SSEC) index, Hang Seng index, Shenzhen Stock Exchange (SZSE) index, commodity futures markets, Aluminum (AL), Gold (AU), Copper (CU), Zinc, Steel Rebar, and Natural Rubber in bearish and bullish market situations. We use the quantile connectedness approach of Ando et al. (2022) to calculate hedge coverage ratios, optimal portfolio weights, and hedge coverage effectiveness. Our empirical analysis yields several important results. First, there is no difference in the results of the TVP-VAR-DY and TVP-VAR models in relation to the magnitude of return transmission during the sample period. Second, CU is the main transmitter and AU is the main receiver of shocks from the network. Third, under the QVAR method estimations, the CU (AU) is the major (minor) contributors to the network during the normal, bearish, and bullish market statuses. Fourth, the results of the hedge ratio strategy confirm that CU and AU are respectively the most and the least expensive assets for a long-term investment in the Chinese stock market in different market conditions.

Impact of night trading sessions on volatility of USD futures market in Thailand

Prior to September 2021, USD Futures could only be traded during the regular hours in Thailand Futures Exchange (TFEX). An additional trading session at night, while trading in both London and New York exchanges is active, enables investors to better handle their foreign exchange exposure or speculative needs of the moment. However, understanding volatility behavior is crucial for achieving successful trading. Therefore, this study investigates the impact of night trading sessions on the USD Futures volatility using GARCH family models. The USD Futures market volatility is examined through comparative analysis before and after the introduction of the night trading session. Both TARCH and EGARCH models have revealed no existence of leverage effect over the sample period - from January 2, 2020 to December 30, 2022. The GARCH model has proved to be the most accurate model for describing USD Futures volatility. Following the launch of nighttime trading, USD Futures market has experienced higher and more persistent volatility. In response to an increase in the volatility of USD Futures, TFEX should increase its margin requirement and monitor the speculative movements in futures market for their possible destabilizing effect. Investors should also adjust their hedge ratio to manage risk more appropriately and incorporate an extended period of increased uncertainty into their trading strategies.

Double Asymmetric Impacts, Dynamic Correlations, and Risk Management Amidst Market Risks: A Comparative Study between the US and China

Extreme shocks, including climate change, economic sanctions, geopolitical conflicts, etc., are significant and complex issues currently confronting the global world. From the US–China perspective, this paper employs the DCC-DAGM model to investigate how diverse market risks asymmetrically affect return volatility, and extract correlations between stock indices and hedging assets. Then, diversified and hedging portfolios, constructed by optimal weight and hedge ratio, are investigated using multiple risk reduction measures. The empirical results highlight that, first, diverse risks exhibit an asymmetric effect on the return volatility in the long term, while in the short term, the US stock market is more sensitive to negative return shocks than the Chinese market. Second, risks impact correlations differently across time horizons and countries. Short-term correlations are stronger than long-term ones for the US market, with the Chinese stock market displaying more stable correlations. Third, the hedging strategy is more effective in reducing volatility and risk for US stocks, while the diversification strategy proves more effective for Chinese stocks. These findings have implications for market participants striving to make their portfolios robust during turbulent times.

Bridging the gap: Uncovering static and dynamic relationships between digital assets and BRICS equity markets

This study uniquely explores the link between nonfungible tokens (NFTs) and the stock markets, providing vital insights for investors to optimize portfolios during global uncertainties such as the health crisis and geopolitical conflicts. We employ the quantile vector autoregression (QVAR) model on daily data from March 14, 2018 to December 23, 2022. Subsequently, the statistics for portfolio analysis are computed using the DCC-GARCH model. Our results highlight that total connectedness at both extremes is significantly higher than at the mean and median quantiles suggesting strong impact of extreme events. The findings reveal that the equity markets of the BRICS countries receive shocks from the system, and NFTs act as transmitters of these shocks. Finally, the pre-COVID-19 pandemic optimal weights remained lower than the COVID-19 pandemic weights, proposing that to reduce risk investors should increase investment in BRICS markets. Similarly, the higher hedge ratio during the turmoil period implies a higher hedging cost. Our findings imply that investors should consider adjusting their investment strategies during periods of heightened global uncertainty to minimize risk and maximize returns.

Quantile Hedging in the complete financial market under the mixed fractional Brownian motion model and the liquidity constraint

This article proposes a pricing framework for European option that utilizes a Quantile hedging strategy in a complete financial market. The methodology involves applying the long memory Geometric Brownian motion model, utilizing the generalized mixed fractional Girsanov theorem, and incorporating relevant findings related to quasi-conditional expectation. The first step in this framework is to derive the option price formula using the equivalent martingale measure. This approach enables investors to use the entire option price without any restrictions to hedge their contingent claim. Then, we add the initial wealth condition to hedge and determine the option price with the Quantile hedging strategy. To do this, we find the maximum probability of success in complete risk hedging for different amounts of model parameters and then use the probability values to determine the option price. Moreover, we apply the minimum variance algorithms and the error correction method to find the hedge ratio. Finally, we illustrate the hedging strategy and provide numerical results that assist investors in determining the required capital for covering the option risk under market data. This calculation takes into account both the level of risk tolerance and the anticipated return on investment.

Exploring the potential of the carbon credit program for hedging energy prices in Brazil

The transition to a low-carbon economy is imperative to reduce reliance on fossil fuels and mitigate pollution emissions. This preposition also aligns with the United Nations Sustainable Development Goals (SDGs-13), which highlight the climate change action. In this vein, Brazil has implemented the Decarbonization Credit (CBIOS) program to incentivize biofuel production and promote environmental sustainability through carbon credit emissions. To this end, the present study evaluates the effectiveness of the CBIO contract as a hedging tool for investors in the face of energy price fluctuations and decarbonization efforts. Specifically, we employ conditional dynamic correlation (DCC-GARCH) and optimal hedge ratio (HR) techniques to assess the relationship between CBIO and the futures and spot prices of sugar, oil, and ethanol. Our findings suggest that the current CBIO contract is not an effective hedge against energy spot and future prices. However, our analysis identifies a strengthening correlation between ethanol traded in Chicago and CBIO over time, highlighting the potential for an underlying contract to serve as an effective hedging tool in the future. Our study adds to the existing literature on carbon pricing mechanisms and their impact on financial markets, emphasizing the importance of sustainable energy policies and their potential to mitigate the risks associated with energy price volatility and decarbonization efforts.

Is digital gold a hedge, safe haven, or diversifier? An analysis of cryptocurrencies, DeFi tokens, and NFTs

ABSTRACT The crypto assets market is growing rapidly, exposing investors to new risks. As a result, finding viable candidates to hedge and diversify crypto portfolios is a critical and timely topic. In this paper, we explore the potential of gold-backed cryptocurrencies as safe haven assets in the context of building a diversified digital assets portfolio. Empirically, we investigate the financial properties (diversification, safe haven, and hedging capabilities) of two gold-backed cryptocurrencies against the three main digital assets categories, i.e. traditional cryptocurrencies, Non-Fungible Tokens (NFTs), and Decentralized Finance (DeFi) tokens, considering major external and internal crises. We also estimate the hedge ratios and the hedging effectiveness of the considered pairs. Overall, our findings indicate that the examined gold-backed cryptocurrencies are good diversifiers, with varying hedging, and safe haven properties depending on the nature of the crises, such as the COVID-19 pandemic and the Russia–Ukraine War, as well as the digital asset category considered. Several financial implications for investors and policymakers are proposed and discussed.

Testing the Hedging Effectiveness of Gold Derivative Market in India

The purpose of this article is to estimate the hedging effectiveness of the Indian Gold Derivative Market from both the statistics of Static hedge methodology of Vector Error Correction Method and Time-Varying hedge methodology of DVEC-GARCH, covering the time span of 1st April 2005 till 31st March 2019, to account for any difference existence in both. The study is conducted to judge the performance of gold futures as a tool of risk management. The result reveals the fact that hedging effectiveness from both the methodologies are found to be 35%. The reason behind low hedging efficiency might be due to the fact that gold itself is used as a hedge instrument and always in demand by the Indian investors for varied reasons as well as its accessibility in various forms (Gold ETFs and Gold Bonds issued by Government). The hedgers’ participation may be less as compared to speculators with respect gold future hedging. The results of phase-wise analysis, that is, before crisis (2005–2007), the hedging efficiency comes out to be 26%, during the crisis (2008–2012) 38.7% and after the crisis (2013–2018) 40% from both the static and dynamic hedge ratio models. The results showed that with the outbreak of Global Financial crisis in 2007–2009 and European debt crisis in 2010–2012 and thereafter, gold futures are increasing use as a risk management tool, but still at a minimal level.

Dependence structure between NFT, DeFi and cryptocurrencies in turbulent times: An Archimax copula approach

This paper investigates the connectedness among eighteen cryptocurrency assets including NFT, DeFi, gold-backed cryptocurrencies, and traditional cryptocurrencies. We also compute the Optimal hedge ratio for each pair of (gold-backed) cryptocurrency-NFT/DeFi and assess their hedge effectiveness. To this end, we use a combination of econometric methods. Our sample period goes from 01/11/2021 to 21/02/2023, making the empirical analysis insightful and interesting as it includes the Covid-19 health crisis and the Russia–Ukraine war. Our empirical findings highlight the dissimilarities between different cryptocurrencies in terms of connectedness with NFT/DeFi assets. They also reflect the diversification benefits generated by the inclusion of gold-backed cryptocurrencies into NFT/DeFi portfolios, in particular in times of unprecedented events. These findings could be useful for crypto-investors who search to diversify their portfolios.

Hedging performance analysis of energy markets: Evidence from copula quantile regression

AbstractThis study investigates hedging performance with respect to different market structures for energy‐related commodities, including West Texas Intermediate crude oil, Brent crude oil, Chinese crude oil, and Heating oil. Copula quantile regression functions and the generalized autoregressive conditionally heteroscedasticity model are combined to analyze the nonlinear impact of dependence and the heterogeneous impact of market structure changes on hedging performance. Results show that hedging performance presents nonlinearity and market structure changes have surprisingly strong heterogeneous effects on the quantile hedge ratio, where bearish and bullish have lower hedge ratios than normal markets, which is captured better by Clayton copula quantile regression. Additionally, the trend of hedging effectiveness over different market structures also shows an inverted U shape. After changing data frequency or the types of futures contracts, the conclusions remain the same. Our empirical findings imply that hedgers are supposed to adjust the hedging number of futures according to market structure changes to hedge price risk effectively.

IMPACT OF DERIVATIVE WARRANTS INTRODUCTION ON THAILAND STOCK MARKET VOLATILITY

The Stock Exchange of Thailand (SET) first launched derivative warrants on SET50 index (SET50 DWs) on April 17, 2014. They are currently the most active DWs on the SET. This research uses the GARCH family models augmented with dummy variable to analyze the effect of SET50 DWs on stock market volatility. The sample data consist of daily returns of SET50 index from the period October 30, 2012 to December 30, 2019. The empirical results indicate that the coming into market of SET50 DWs reduces stock market volatility. The GARCH (1,1) TARCH (1,1), and EGARCH (1,1) models are not radically different from each other in their output. However, the asymmetric TARCH (1,1) model is found to provide the best fit in modelling volatility. The SET50 index shows the existence of leverage effect, where negative shocks have a greater impact on the volatility than positive shocks. Introducing SET50 DWs lowers the price volatility of SET50 index so investor having a portfolio investment with a correlation to the performance of SET50 index should adjust hedge ratio appropriately to manage investment risk. There is also a suggestion for policy makers to support the launch of DWs to lower the volatility in underlying spot market resulting in improved efficiency.