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Since the 1970s, futures hedge ratios have traditionally been calculated ex-post via economically structure-less statistical analyses. This paper proposes an ex-ante, more efficient, computationally simpler, general “carry cost rate” hedge ratio. The proposed hedge ratio is biased, but its bias is readily mitigatable via a stationary Bias Adjustment Multiplier (BAM). The 2-part intuition for the BAM and its stationarity is as follows. First, the paper reasons that the “traditional” hedge ratio should uncover the carry cost rate and shows that it does, albeit inefficiently. Then, since both the “traditional” and “carry cost rate” hedge ratios are driven by the carry cost rate, it may be that their ratio (for implementation in the same prior periods) is stationary and useful as an ex-ante BAM for the “carry cost rate” hedge ratio; the paper tests these conjectures and finds support for both. Specifically, the paper shows that the “bias-adjusted carry cost rate” hedge ratio, defined as the average product of the ex-post BAMs from prior periods and the current ex-ante “carry cost rate” hedge ratio, has higher hedge-effectiveness than that for either the “traditional” or “naive” benchmark hedge ratios in diverse real and simulated markets.

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes.

Purpose The purpose of this paper is to measure the effectiveness of the hedging with futures currency contracts. Measuring the effectiveness of hedging has become mandatory for Indian companies as the new Indian accounting standards, Ind-AS, specify that the effectiveness of hedges taken by the companies should be evaluated using quantitative methods but leaves it to the company to choose a method of evaluation. Design/methodology/approach The paper compares three models for evaluating the effectiveness of hedge – ordinary least square (OLS), vector error correction model (VECM) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH) model. The OLS and VECM are the static models, whereas DCC-MGARCH is a dynamic model. Findings The overall results of the study show that dynamic model (DCC-MGARCH) is a better model for calculating the hedge effectiveness as it outperforms OLS and VECM models. Practical implications The new Indian accounting standards (Ind-AS) mandates the calculation of hedge effectiveness. The results of this study are useful for the treasurers in identifying appropriate method for evaluation of hedge effectiveness. Similarly, policymakers and auditors are benefitted as the study provides clarity on different methods of evaluation of hedging effectiveness. Originality/value Many previous studies have evaluated the efficiency of the Indian currency futures market, but with rising importance of hedging in the Indian companies, Reserve Bank of India’s initiatives and encouragement for the use of futures for hedging the currency risk and now the mandatory accounting requirement for measuring hedging effectiveness, it has become more relevant to evaluate the effectiveness of hedge. To the authors’ best knowledge, this is one of the first few papers which evaluate the effectiveness of the currency future hedging.

This paper investigates out-of-sample performance of the naïve hedging strategy relative to that of the minimum variance hedging strategy, in which the covariance parameters are estimated from 18 econometric models. Hedging performance is compared across 24 futures markets. Our main findings suggest that it is difficult to find a strategy under the minimum variance framework that outperforms the naïve hedging strategy both consistently and significantly. Our findings are robust to different sample periods, estimation windows, and hedging horizons and can be partly explained by the effects of estimation error and model misspecification. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2028 . This paper was accepted by Itay Goldstein, finance.

This paper intends to explore the volatility transmission amid crude oil price and the gold price in India employing BEKK-GARCH, CCC-GARCH, and DCC-GARCH model. Further, the model’s outcome is used to estimate the optimal weights and hedge ratios for oil -bullion portfolio holdings. The outcome of the study indicating a historic relationship among oil and the gold price in India. The results also specify that the DCC GARCH model is more preferred than CCC GARCH and BEKK GARCH model since the DCC GARCH model provides more evidence of volatility spillover between the oil and gold returns. The analysis postulating that the gold price is sensitive to oil price changes. Hence, gold price changes could be a predictor for oil returns in India. The estimated hedge ratio postulates that gold is a valuable hedge for the fluctuations in the oil price in India. The study results provide a valuable insight to the investors/ institutional investors to understand the spillover effect to make better investment decisions by diversifying their risk.

Through the application of the VAR-AGARCH model to intra-day data for three cryp-tocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.

PurposeIn a first place, the present paper is designed to examine the dynamic correlations persistent between five cryptocurrencies, WTI, Gold, VIX and four stock markets (SP500, FTSE, NIKKEI and MSCIEM). In a second place, it investigates the relevant optimal hedging strategy.Design/methodology/approachEmpirically, the authors examine how WTI, Gold, VIX and five cryptocurrencies can be applicable to hedge the four stock markets. Three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) are implemented to estimate dynamic optimal hedge ratios.FindingsThe reached findings prove that both of the Bitcoin and Gold turn out to display remarkable hedging commodity features, while the other assets appear to demonstrate a rather noticeable disposition to act as diversifiers. Moreover, the results show that the VIX turns out to stand as the most effectively appropriate instrument, fit for hedging the stock market indices various related refits. Furthermore, the results prove that the hedging strategy instrument was indifferent for FTSE and NIKKEI stock while for the American and emerging markets, the hedging strategy was reversed from the pre-cryptocurrency crash to the during cryptocurrency crash period.Originality/valueThe first paper's empirical contribution lies in analyzing emerging cross-hedge ratios with financial assets and compare hedging effectiveness within the period of crash and the period before Bitcoin crash as well as the sensitivity of results to refits choose to compare between short term hedging strategy and long-term one.

We investigate gold’s role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions. For the empirical analysis, we extend the intraday multifractal correlation measure developed by Madani et al. (Bankers, Markets & Investors, 163:2–13, 2020) to consider the dependence for calm and extreme movement periods across different time scales. Interestingly, we employ the rolling window method to examine the time-varying dependence between gold-oil and gold-currency in terms of calm and turmoil market conditions. Based on high frequency (5-min intervals) across the period 2017–2019, our analysis shows three interesting findings. First, gold acts as a weak (strong) hedge for oil (currency) market movements, across all agent types. Second, gold has strong safe-haven capability against extreme currency movements, and against only short time scales of oil price movements. Third, hedging strategies confirm the scale-dependent gold's role in reducing portfolio risk as a hedge or safe haven. Implications for investors, financial institutions, and policymakers are discussed.

Effective risk management is an important aspect of farming. Risk management involves choosing among alternatives that reduce the financial effects of the uncertainties of weather, yields, prices, government policies, and other factors that can cause wide swings in farm income. To deal with price uncertainty, this paper focuses on futures markets and calculates hedge ratio for dates. A bivariate BEKK GARCH model is used to determine time-varying hedge ratios. The results show that the average BEKK BGARCH hedge ratio for dates is .7. Also in this paper, the hedge ratio, which takes into consideration the producers' risk-averse parameter, is estimated [Extended mean Gini hedge ratio (EMGHR)]. Results of EMGHR recommended that risk-averse producers, who have risky parameter equal to 50, could reduce their price risk to 60% by attending futures markets.