71,333 research outputs found

### HEDGING RISK FOR FEEDER CATTLE WITH A TRADITIONAL HEDGE COMPARED TO A RATIO HEDGE

This paper compares hedging risk for various weights of feeder cattle hedged with a traditional cross hedge and a ratio cross hedge. A traditional hedge calls for the purchase/sale of one pound of futures for each pound of cash feeder cattle. By contrast, a ratio hedge requires estimation of a hedge ratio to determine the number of pounds of futures needed to hedge one pound of cash feeder cattle. Hedge ratios were found to be larger than 1.0 for light-weight feeder cattle. By using the estimated hedge ratios, it was shown that hedging risk could be reduced 20-50 percent compared to that achieved by using a hedge ratio of 1.0.Livestock Production/Industries,

### WEIGHTED EXPECTED UTILITY HEDGE RATIOS

We derive a new hedge ratio based on weighted expected utility. Weighted expected utility is a generalization of expected utility that permits non-linear probability weights. Generally speaking weighted expected utility hedge ratios are less than minimum variance hedge ratios and larger than expected utility hedge ratios.Hedging, hedge ratio, weighted expected utility, Allais Paradox, Marketing,

### GENERALIZED HEDGE RATIO ESTIMATION WITH AN UNKNOWN MODEL

Myers and Thompson (1989) pioneered the concept of a generalized approach to estimating hedge ratios, pointing out that the model specification could have a large impact on the hedge ratio estimated. While a huge empirical literature exists on estimating hedge ratios, the literature is lacking a formal treatment of model specification uncertainty. This research accomplishes that task by taking a Bayesian approach to hedge ratio estimation, where specification uncertainty is explicitly modeled. Specifically, we present a Bayesian approach to hedge ratio estimation that integrates over model specification uncertainty, yielding an optimal hedge ratio estimator that is robust to possible model specification because it is an average across a set of hedge ratios conditional on di erent models. Model specifications vary by exogenous variables (such as exports, stocks, and interest rates) and lag lengths included. The methodology is applied to data on corn and soybeans and results show the potential benefits and insights gained from such an approach.Marketing,

### Hedging Effectiveness of Constant and Time Varying Hedge Ratio in Indian Stock and Commodity Futures Markets

This paper examines hedging effectiveness of futures contract on a financial asset and commodities in Indian markets. In an emerging market context like India, the growth of capital and commodity futures market would depend on effectiveness of derivatives in managing risk. For managing risk, understanding optimal hedge ratio is critical for devising effective hedging strategy. We estimate dynamic and constant hedge ratio for S&P CNX Nifty index futures, Gold futures and Soybean futures. Various models (OLS, VAR, and VECM) are used to estimate constant hedge ratio. To estimate dynamic hedge ratios, we use VAR-MGARCH. We compare in-sample and out-of-sample performance of these models in reducing portfolio risk. It is found that in most of the cases, VAR-MGARCH model estimates of time varying hedge ratio provide highest variance reduction as compared to hedges based on constant hedge ratio. Our results are consistent with findings of Myers (1991), Baillie and Myers (1991), Park and Switzer (1995a,b), Lypny and Powella (1998), Kavussanos and Nomikos (2000), Yang (2001), and Floros and Vougas (2006).

### Effective Basemetal Hedging: The Optimal Hedge Ratio and Hedging Horizon

This study investigates optimal hedge ratios in all base metal markets. Using recent hedging computation techniques, we find that 1) the short-run optimal hedging ratio is increasing in hedging horizon, 2) that the long-term horizon limit to the optimal hedging ratio is not converging to one but is slightly higher for most of these markets, and 3) that hedging effectiveness is also increasing in hedging horizon. When hedging with futures in these markets, one should hedge long-term at about 6 to 8 weeks with a slightly greater than one hedge ratio. These results are of interest to many purchasing departments and other commodity hedgers

### Testing for Constant Hedge Ratios in Commodity Markets: A Multivariate Garch Approach

The authors develop a new multivariate GARCH parameterization that is suitable for testing the hypothesis that the optimal futures hedge ratio is constant over time, given that the joint distribution of cash and futures prices is characterized by autoregressive conditional heteroskedasticity. The advantage of the new parameterization is that it allows for a flexible form of time-varying volatility, even under the null of a constant hedge ratio. The model is estimated using weekly corn prices. Statistical tests reject the null hypothesis of a constant hedge ratio and also reject the null that time variation in optimal hedge ratios can be explained solely by deterministic seasonality and time-to-maturity effects.

### Hedge ratio estimation and hedging effectiveness: the case of the S&P 500 stock index futures contract

This paper investigates the hedging effectiveness of the Standard & Poor’s (S&P) 500 stock index futures contract using weekly settlement prices for the period July 3rd, 1992 to June 30th, 2002. Particularly, it focuses on three areas of interest: the determination of the appropriate model for estimating a hedge ratio that minimizes the variance of returns; the hedging effectiveness and the stability of optimal hedge ratios through time; an in-sample forecasting analysis in order to examine the hedging performance of different econometric methods. The hedging performance of this contract is examined considering alternative methods, both constant and time-varying, for computing more effective hedge ratios. The results suggest the optimal hedge ratio that incorporates nonstationarity, long run equilibrium relationship and short run dynamics is reliable and useful for hedgers. Comparisons of the hedging effectiveness and in-sample hedging performance of each model imply that the error correction model (ECM) is superior to the other models employed in terms of risk reduction. Finally, the results for testing the stability of the optimal hedge ratio obtained from the ECM suggest that it remains stable over time.Hedging effectiveness; minimum variance hedge ratio (MVHR); hedging models; Standard & Poor’s 500 stock index futures

### The Dynamic International Optimal Hedge Ratio

Instead of modeling asset price and currency risks separately, this paper derives the international hedge portfolio, hedging asset price and currency risk simultaneously for estimating the dynamic international optimal hedge ratio. The model estimation is specified in a multivariate GARCH setting with vector error correction terms and estimated for the commodity and stock markets of the U.S., the U.K., and Japan.Optimal Hedge Ratio, International Hedging, Multivariate GARCH, Currency

### Hedge Effectiveness Forecasting

This study focuses on hedging effectiveness defined as the proportionate price risk reduction created by hedging. By mathematical and simulation analysis we determine the following: (a) the regression R2 in the hedge ratio regression will generally overstate the amount of price risk reduction that can be achieved by hedging, (b) the properly computed hedging effectiveness in the hedge ratio regression will also generally overstate the amount of risk reduction that can be achieved by hedging, (c) the overstatement in (b) declines as the sample size increases, (d) application of estimated hedge ratios to non sample data results in an unbiased estimate of hedging effectiveness, (e) application of hedge ratios computed from small samples presents a significant chance of actually increasing price risk by hedging, and (f) comparison of in sample and out of sample hedging effectiveness is not the best method for testing for structural change in the hedge ratio regression.out of sample, post sample, hedging, effectiveness, forecasts, simulation, Agricultural Finance,

### The Relative Merits of Investable Hedge Fund Indices and of Funds of Hedge Funds in Optimal Passive Portfolios

Can the new investable hedge fund indices (IHF) enhance the performance of optimal passive portfolios made of equities and bonds? How do they compare to funds of hedge funds (FoHF) as well as to other alternative investments such as commodities and volatility? The conclusions depend crucially on forecasts of future expected excess returns for all assets as well as a careful conditioning of the data to reflect trading costs and remove unrealistic serial correlations. A naïve forecast based on recent historical performance leads to no allocations to either IHF or FoHF, a result explained by the performance of equities and commodities and limited diversification effects from hedge funds. Yet a forecast based on market equilibrium returns for all main asset classes but hedge funds, which are kept at their historical level, leads to the opposite result with optimal portfolios almost exclusively invested in hedge funds. Both conclusions are unrealistic and unstable. More reasonable allocations are obtained with the Black-Litterman (BL) approach to combining subjective views with equilibrium returns. Then both hedge funds instruments play a significant role in optimal passive portfolios if their expected excess returns are at least 1%. Long volatility positions are also likely to be attractive. However the BL approach can also be criticised.hedge funds, investable hedge funds indices, funds of hedge funds, commodities, VIX, mean-variance analysis, Sharpe Ratio, Adjusted Sharpe Ratio, Omega Ratio, Black Litterman model

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