Role of Evaluation Period Characteristics in the Efficiency Analysis of Futures Contracts for Equity Index Risk Management

Objective: Previous research has examined the effectiveness of futures contracts in risk management for various indices using different strategies and estimation methods across different years, countries, and underlying assets, yielding diverse results. One of the key dimensions influencing the divergence in outcomes of past studies is the characteristics of the assessment period for variance changes and the estimation period for optimal risk hedging. Understanding the role of these factors is crucial for enhancing the quality of future studies on risk hedging via futures contracts. Due to more implication of variance and more coverage in previeouse studies, this measure has been used for risk in contrast ot utility or Var. To validate the results we need to test the robustness of test results fo all three hypotheses.

Method: This meta-analysis encompasses all empirical tests conducted on the efficacy of stock index risk hedging using futures contracts, published in articles from 2015 to 2022. It includes 30 papers and 1,373 effect sizes, which consist of 1,846,373 and 1,052,225 observations in the estimation and assessment periods, respectively. To achieve the specified objectives, data were computed and analyzed using CMA software. The excell software has been used to arrange the data. The basis of the evaluation was the weighted average effect size of risk coverage efficacy, and ANOVA was utilized for comparing effect size groups. Due to convergence of effect sizes, the random effect method has been used to calculate the cumulative effect sizes. Seven-sptep meta-analysis has been used in this study.

Findings: Results indicated that the efficacy of risk hedging in the category with an assessment period of one year or less was greater than that in categories with an assessment period exceeding one year. Furthermore, the efficiency of stock index risk hedging utilizing out-of-sample strategies was found to be superior to that employing in-sample strategies. Moreover, using a larger dataset, or in other words, extending the estimation period, led to a reduction in risk hedging efficiency. Out of 16 robustness test categories for assessment period length and estimation strategy, results were confirmed in 15 categories, and for estimation period length, results were validated in ten categories.

Conclusion: During extended evaluation periods, the frequency of interest rate changes or other environmental factors is likely higher, which may reduce the effectiveness of risk hedging. Regarding the estimation window length, if investors estimate their risk hedging model parameters using datasets spanning less than three years, they can more effectively control price volatility in their cash assets. Indeed, under such conditions, the variance of a portfolio comprising both cash and futures assets experiences a more pronounced reduction. Finally, when investors utilize historical data to estimate parameters and implement risk hedging for future periods—adopting an out-of-sample approach—they can achieve more efficient risk coverage.