Posted: Jun 18, 2023
The proliferation of financial derivatives over the past three decades has fundamentally transformed global financial markets, creating complex interdependencies that challenge traditional notions of market efficiency and risk diversification. While derivatives serve essential functions in price discovery, risk transfer, and market completion, their intricate network effects and non-linear payoff structures introduce computational complexities that exceed the analytical capabilities of conventional financial models. This research addresses the critical gap in understanding how derivatives simultaneously enhance market efficiency while potentially undermining diversification benefits through hidden correlation channels. Our research introduces a novel computational paradigm that combines quantum-inspired optimization with neuromorphic processing to analyze derivative market dynamics. This approach enables us to capture the non-linear, high-dimensional relationships between derivative instruments and market efficiency while accounting for behavioral factors that influence trading decisions.
Downloads: 5
Abstract Views: 892
Rank: 210238