The Hedging Effectiveness of ECU Futures Contracts: Forecasting Evidence from an Error Correction Model

In this paper, the traditional price change hedge ratio estimation method is extended by applying the theory of cointegration in case of hedging with European Currency Unit (ECU) futures contracts. Previous studies ignore the last period's equilibrium error and short-run deviations. The findings of this study indicate that the hedge ratio estimated by the error correction method is superior to the one obtained from the traditional method, as evidenced by the likelihood ratio test and out-of-sample forecasts. Hedgers can control the risk of their portfolios more effectively at a lower cost.     

Cross-hedging foreign currency risk: Empirical evidence from an error correction model

In this article, the traditional price change hedge ratio estimation method is extended by applying the theory of cointegration in the case of cross-hedging of spot exchange risk of the Belgian franc (BF), the Italian lira (IL), and the Dutch guilder (NG) with U.S. Dollar Index futures contracts. Previous studies ignore the last period's equilibrium error and short-run deviations. The findings of this study indicate that the hedge ratio estimated by the error correction method is superior to that obtained from the traditional method, as evidenced by the likelihood ratio test and out-of-sample forecasts. Hedgers will be able to control the risk of their portfolios more effectively at a lower cost.     

Spot asset carry cost rates and futures hedge ratios

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.

     

Comparing Trading Performance of the Constant and Dynamic Hedge Models: A Note

The constant and dynamic hedge models, with the presence of transaction costs are compared for the Share Price Index futures contract trading on the Sydney Futures Exchange. The optimal hedge ratio is estimated by using a dynamic, bivariate two-stage model for the return equation with a dynamic GARCH error structure for the conditional hedge ratios. When portfolio projections are compared based on their profit positions (net of transaction costs), the GARCH hedge model dominates the next best competitor in terms of trading profit.     

A Futures Duration-Convexity Hedging Method

A duration-based hedge ratio is the conventional method to hedge against price changes of a fixed-income instrument. However, the relationship between bond prices and interest rates is nonlinear, creating a convexity effect. Moreover, term structure changes often are nonparallel in nature, which causes imperfect hedges for the duration-based hedging model. One solution to these problems is to dynamically change the duration-based hedge ratio; however, this procedure is costly and is not effective when jumps in prices occur. A superior solution is to develop a two-instrument hedge ratio that simultaneously hedges both duration and convexity effects. This paper first presents such a two-instrument hedge ratio and then we examine its effectiveness. The simulation results show that this duration-convexity hedge ratio is vastly superior to alternative hedge ratio methods for both simple and complex changes in the term structure.     

Determination of Optimal Hedging Strategy for Index Futures: Evidence from Turkey

This paper aims to determine optimal hedge strategy for the Istanbul Stock Exchange (ISE)-30 stock index futures in Turkey by comparing hedging performance of constant and time-varying hedge ratios under mean-variance utility criteria. We employ standard regression and bivariate GARCH frameworks to estimate constant and time-varying hedge ratios respectively. The Turkish case is particularly challenging since Turkey has one of the most volatile stock markets among emerging economies and the turnover ratio as a measure of liquidity is very high for the market. These facts can be considered to highlight the great risk and, therefore, the extra need for hedging in the Istanbul Stock Exchange (ISE). The empirical results from the study reveal that the dynamic hedge strategy outperforms the static and the traditional strategies.     

Using the short-lived arbitrage model to compute minimum variance hedge ratios: application to indices,stocks and commodities

The short-lived arbitrage model has been shown to significantly improve in-sample option pricing fit relative to the Black–Scholes model. Motivated by this model, we imply both volatility and virtual interest rates to adjust minimum variance hedge ratios. Using several error metrics, we find that the hedging model significantly outperforms the traditional delta hedge and a current benchmark hedge based on the practitioner Black–Scholes model. Our applications include hedges of index options, individual stock options and commodity futures options. Hedges on gold and silver are especially sensitive to virtual interest rates.     

TREASURY BILL FUTURES AS A HEDGING TOOL: A RISK-RETURN APPROACH

The primary purpose of this study is to measure the hedging performance of Treasury Bill Futures on a risk-return basis. A theoretical model is presented and hedging effectiveness is tested using T-Bill cash and futures data. Successful hedging depends critically upon the ability to determine the optimal hedge ratio. The results also indicate that the traditional one-to-one hedge outperforms the more sophisticated hedge ratio models; however, even here the risk-return benefits of hedging are minimal.     

Zur Risikoanalyse von Hedgefonds: Ein datenanalytischer Ansatz

Hedge fund returns have a number of specific features compared to traditional investments which result in problems when applying traditional methods of risk analysis (Markowitz portfolio selection theory, Sharpe Ratio, value at risk calculation based on normal returns). These problems have to be considered adequately by insurance companies when constructing internal risk models and performing risk management for hedge funds in their investment.The present paper has its focus on the departure of hedge fund returns from the normality hypothesis, especially with respect to the statistical quantities skewness and kurtosis (fat tail problem). A statistical analysis of hedge fund index returns gives evidence that the majority of hedge fund returns show substantial departures from normality. In addition, the analysis shows that hedge fund returns are adequately represented by the family of GH-distributions developed in exploratory data analysis. Following this result a risk analysis of hedge fund strategies is performed on the basis of the GH-value at risk.     

Option pricing and hedging under a stochastic volatility Lévy process model

In this paper, we discuss a stochastic volatility model with a Lévy driving process and then apply the model to option pricing and hedging. The stochastic volatility in our model is defined by the continuous Markov chain. The risk-neutral measure is obtained by applying the Esscher transform. The option price using this model is computed by the Fourier transform method. We obtain the closed-form solution for the hedge ratio by applying locally risk-minimizing hedging.     

Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets

We use the All Ordinaries Index and the corresponding Share Price Index futures contract written against the All Ordinaries Index to estimate optimal hedge ratios, adopting several specifications: an ordinary least squares‐based model, a vector autoregression, a vector error‐correction model and a diagonal‐vec multivariate generalized autoregressive conditional heteroscedasticity model. Hedging effectiveness is measured using a risk‐return comparison and a utility maximization method. We find that time‐varying generalized autoregressive conditional heteroscedasticity hedge ratios perform better than constant hedge ratios in terms of minimizing risks, but when return effects are also considered, the utility‐based measure prefers the ordinary least squares method in the in‐sample hedge, whilst both approaches favour the conditional time‐varying multivariate generalized autoregressive conditional heteroscedasticity hedge ratio estimates in out‐of‐sample analyses.     

Performance hypothesis testing with the Sharpe ratio: The case of hedge funds

As recent research highlights that the Sharpe ratio has a decision theoretic foundation even in the case of asymmetric or fat-tailed excess returns and thus is adequate even for the evaluation of hedge funds, this note provides the first Sharpe ratio based performance analysis of the hedge fund market. Furthermore, it addresses the important practical question whether the choice of hypothesis test used to statistically compare Sharpe ratios can influence an investor’s hedge fund selection process. Our key findings are as follows: (i) Only a small fraction of hedge funds in our large dataset can significantly outperform passive investments in corresponding hedge fund indices. (ii) Especially in the presence of autocorrelated or skewed excess returns, the traditional test of Jobson and Korkie, 1981, Memmel, 2003 tends to overstate the number of significant outperformers and thus provides potentially misleading information for investors. Decision makers are advised to use the bootstrap test of Ledoit and Wolf (2008) allowing robust and more reliable inference.     

STOCK INDEX FUTURES HEDGING: HEDGE RATIO ESTIMATION, DURATION EFFECTS, EXPIRATION EFFECTS AND HEDGE RATIO STABILITY

This paper examines hedging effectiveness for the FTSE-100 Stock Index futures contract from 1984 to 1992. It investigates the appropriate econometric technique to use in estimating minimum variance hedge ratios by undertaking estimations using OLS, an ECM and GARCH. Simple OLS outperforms more complex econometric techniques. Additionally, the paper examines the impact ofhedge duration and time to expiration on estimated hedge ratios and hedge ratio stability over time. It is shown that hedge ratios and hedging effectiveness increase with hedge duration, hedge ratios approach unity as expiration approaches and while hedge ratios vary over time they are stationary.     

Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.     

现代投资组合新视角:对冲基金配置的理论与实证研究

本文在分析传统的投资对冲基金组合架构存在不足的基础上,提出了新的资产组合构架模型,并从理论上论证了对冲基金为何不是一个纯粹超额收益的制造者而更多是一个风险溢价的提供者以及将对冲基金与传统资产有机整合到一起的好处。然后用Cornish—Fisher伸展式对新的资产组合进行分析,证明了新架构的合理性,并用欧美市场的数据进行实证检验。本研究为对冲基金投资者提供了新的操作范式。     

Alpha or beta in the eye of the beholder: What drives hedge fund flows?

Capital Asset Pricing Model (CAPM) alpha explains hedge fund flows better than alphas from more sophisticated models. This suggests that investors pool together sophisticated model alpha with returns from exposures to traditional (except for the market) and exotic risks. We decompose performance into traditional and exotic risk components and find that while investors chase both components, they place greater relative emphasis on returns associated with exotic risk exposures that can only be obtained through hedge funds. However, we find little evidence of persistence in performance from traditional or exotic risks, which cautions against investors’ practice of seeking out risk exposures following periods of recent success.     

Measuring minimum variance hedging effectiveness: Traditional vs. sophisticated models

This study investigates whether the more sophisticated GARCH based models are better minimum variance hedging strategies than the less sophisticated regression based traditional models. The findings of the study suggest that the traditional models that directly estimate the optimal hedge ratio significantly outperform the more sophisticated models that indirectly estimate the optimal hedge ratio based on timevarying variance-covariance parameters. Although, the sophisticated models seem to have more theoretical appeal, the higher estimation and misspecification errors of these models reduce their hedging effectiveness, making them inferior to the traditional models.     

Comparing the Effectiveness of Traditional and Time Varying Hedge Ratios Using New Zealand and Australian Debt Futures Contracts

We apply cointegration methodology to the New Zealand and Australian 90-day, three-year and 10-year debt and futures markets. We compare traditional methods of calculating hedge ratios with those computed by using univariate and multivariate error correction models. We use out-of-sample forecasting to determine which approach is the most effective. Contrary to recent research, our results show that univariate and multivariate error correction models do not outperform more traditional methods of constructing hedges.     

The use and abuse of the hedging effectiveness measure

Ederington (1979) proposed an effectiveness measure for futures hedging. Since then, this measure has been widely adopted in the literature to compare different hedge ratios against the OLS (ordinary least squares) hedge ratio. This note attempts to demonstrate this application is inappropriate. Ederington hedging effectiveness is only useful for measuring the risk reduction effect of the OLS hedge ratio. It does not apply to other hedge ratios and therefore should not serve as a criterion to compare different hedge strategies against the OLS strategy. A strict application of this measure almost always leads to an incorrect conclusion stating that the OLS hedge ratio is the best hedging strategy.     

A Markov regime switching approach for hedging energy commodities

This paper estimates constant and dynamic hedge ratios in the New York Mercantile Exchange oil futures markets and examines their hedging performance. We also introduce a Markov regime switching vector error correction model with GARCH error structure. This specification links the concept of disequilibrium with that of uncertainty (as measured by the conditional second moments) across high and low volatility regimes. Overall, in and out-of-sample tests indicate that state dependent hedge ratios are able to provide significant reduction in portfolio risk.