HEDGING WITH INTERNATIONAL STOCK INDEX FUTURES: AN INTERTEMPORAL ERROR CORRECTION MODEL

In this paper we extend the traditional price change hedge ratio estimation method by applying the theory of cointegration to hedging with stock index futures contracts for France (CAC 40), the United Kingdom (FTSE 100), Germany (DAX), and Japan (NIKKEI). Previous studies ignore the last period's equilibrium error and short-run deviations. The findings of this study indicate that the hedge ratios obtained from the error correction method are superior to those obtained from the traditional method as evidenced by the likelihood ratio test and out-of-sample forecasts. Using the procedures developed in this paper, hedgers can control the risk of their portfolios more effectively at a lower cost.     

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.     

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.     

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.

     

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.     

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.     

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.     

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.     

Combining the Effects of OLS and Spread on Futures Hedging: Evidence from the Taiwan Stock Index

Abstract:

This study proposes a dynamic hedge ratio, the combined ordinary least squares spread (COLSS), which combines the hedge ratio of ordinary least squares and the value of spread. Using this dynamic ratio for hedging with futures contracts, one can replace spot risk with spread risk. The COLSS captures not only the long-run equilibrium between spot and futures returns, but also the short-run deviation from equilibrium. The spread is forecast by one-period lagged stock market factors and high-order moments that are estimated by an options model. In the in-sample and out-of-sample tests, the COLSS strategy achieves significant risk reduction and outperforms the alternative models by a large utility improvement.     

Minimum variance hedging when spot price changes are partially predictable

In many markets, changes in the spot price are partially predictable. We show that when this is the case: (1) although unbiased, traditional regression estimates of the minimum variance hedge ratio are inefficient, (2) estimates of the riskiness of both hedged and unhedged positions are biased upward, and (3) estimates of the percentage risk reduction achievable through hedging are biased downward. For natural gas cross hedges, we find that both the inefficiency and bias are substantial. We further find that incorporating the expected change in the spot price, as measured by the futures-spot price spread at the beginning of the hedge, into the regression results in a substantial increase in efficiency and reduction in the bias.     

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.     

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.     

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.     

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance.     

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.     

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.     

Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19

This study combined time-varying parameter vector autoregression (TVP-VAR) and a spillover index model to analyze the static, total, and net spillover effects of energy and stock markets before and after the COVID-19 outbreak. A network method was also used to depict structural changes more intuitively. Furthermore, we calculated and compared changes in the hedge ratio, optimal portfolio weights, and hedge effectiveness to guide investors to adjust portfolio strategies during COVID-19. The main findings were as follows: First, COVID-19 had a significant impact on spillover effects, and the average value of total spillover index increased by 19.94% compared with that before the epidemic. Second, the energy market was an important risk recipient of the stock market before COVID-19, and the extent of risk acceptance increased after the COVID-19 outbreak. Third, the hedging ratio, optimal portfolio weights, and hedge effectiveness showed huge changes after the COVID-19 outbreak, requiring investors to adjust their portfolio strategies.     

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.     

Currency Hedging Using the Mean-Gini Framework

The mean-Gini framework has been suggested as a robust alternative to the portfolio approach to futures hedging given its optimality under general distributional conditions. However, calculation of the Gini hedge ratio requires estimation of the underlying price distribution. We estimate minimum-Gini hedge ratios using two widely-used estimation procedures, the empirical distribution function method and the kernel method, for three emerging market and three developed market currencies. We find that these methods yield different Gini hedge ratios. These differences increase with risk aversion and are statistically significant for all developed market currencies but only one emerging market currency. In-sample analyses show that the empirical distribution function method is more effective at risk reduction than the kernel method for developed market currencies, whereas the kernel method is superior for emerging market currencies. Post-sample analyses strengthen the superiority of the empirical distribution function method for developed market and, in several cases, for emerging market currencies.JEL Classification: F31, G15     

The minimum variance hedge and the bankruptcy risk of the firm

In this paper, we analyze the influence of hedging with forward contracts on the firm's probability of bankruptcy (POB). The minimization of this probability can serve as a substitute for the maximization of shareholders' wealth. It is shown that the popular minimum variance hedge is generally neither necessary nor sufficient for the minimization of the firm's POB. Moreover, our model suggests a correction of the widespread view that a reduction in the variance of the future value of the firm is inevitably accompanied by a reduction in its default risk. We derive an analytical solution for the variance-minimizing hedge ratio of a firm exposed to both input and output price uncertainty that takes into account the issue of correlation. Based on this solution, we provide a graphical analysis to prove our claim that there is a fundamental difference between hedging policies focused on bankruptcy risk and those following conventional wisdom even if positive correlation constitutes a “natural” hedge.