Hedging Strategy Analysis of Investment Risk Using LQ45 Index Futures Contracts with OLS and DCC-GARCH Models

Abstract

Volatility in the LQ45 Index remains a key concern for investors in Indonesia's capital market, underscoring the need for effective risk management strategies. This study investigates the hedging effectiveness of LQ45 index futures by comparing two estimation methods for hedge ratios: Ordinary Least Squares (OLS) and Dynamic Conditional Correlation-GARCH (DCC-GARCH). Using daily return data from 2021 to 2024, the analysis begins with stationarity testing and model diagnostics to ensure validity. Both models are estimated and evaluated based on their fit and statistical robustness. Hedging effectiveness is assessed by comparing portfolio variances before and after hedging, alongside statistical validation using F-tests and t-tests. Results show that both models offer risk reduction, but the DCC-GARCH model outperforms OLS by providing dynamic hedge ratios that better capture market volatility. The DCC-GARCH approach also satisfies all diagnostic criteria, indicating its statistical reliability and robustness. These findings highlight the relevance of dynamic hedging models in volatile markets and provide empirical support for integrating advanced econometric tools in risk management practices. The study contributes to the underexplored literature on index futures in emerging markets and offers valuable insights for policymakers and practitioners aiming to enhance derivative utilization in Indonesia.