Posted: Jul 29, 2023
The landscape of institutional investment management has undergone profound transformation in recent decades, with derivatives emerging as increasingly sophisticated tools for risk management and performance enhancement. Traditional approaches to derivative utilization have predominantly focused on hedging specific exposures or implementing directional views, often treating these instruments as ancillary components rather than integral elements of portfolio construction. This research challenges conventional paradigms by proposing a comprehensive framework that repositions derivatives as central instruments in achieving dual objectives of risk mitigation and performance optimization simultaneously. Institutional investors, including pension funds, insurance companies, and endowment funds, face complex challenges in navigating volatile financial markets while meeting their fiduciary obligations. The conventional separation between risk management and return generation has created artificial boundaries that limit the potential benefits derivable from sophisticated financial instruments. Our research addresses this gap by developing an integrated approach that leverages the unique characteristics of derivatives to create more resilient and efficient portfolios. This study makes several distinctive contributions to the field of financial technology and institutional investment management. First, we introduce the Dynamic Derivative Allocation Matrix (DDAM), a novel framework that employs quantum-inspired optimization algorithms to determine optimal derivative positions across multiple time horizons and market regimes. Second, we challenge the traditional view of derivatives as mere hedging instruments by demonstrating their capacity to serve as dynamic performance enhancers that can generate alpha while reducing overall portfolio risk. Third, we provide empirical evidence across multiple market cycles showing that our approach significantly outperforms conventional derivative strategies in terms of risk-adjusted returns.
Downloads: 3
Abstract Views: 1305
Rank: 159769