ScholarWorks@성균관대학교

초록

The accurate and efficient estimation of option prices and Greeks is crucial for effectively managing and hedging risks in financial derivatives. Traditional approaches often struggle with the analytical and numerical complexities involved, especially in sophisticated option pricing models. For example, pricing exotic options under non-Markovian models may not even be formulated as partial differential equations, and thus relies on simulation schemes. This paper introduces a deep learning-based framework utilizing generative neural networks, specifically conditional normalizing flows, to estimate option prices and Greeks. Using these generative networks, two approaches are proposed: simulation-based and integration-based approaches. These methods are universally applicable across a variety of models-from the simple Black-Scholes model to complicated non-Markovian models with rough volatility- and a wide range of option types-including vanilla options, cliquet-style options, and multi-asset options. Experimental results demonstrate that our proposed methods provide more accurate and reliable results compared to a feed-forward neural network method. Furthermore, our approach can compute option prices and Greeks thousands to hundreds of thousands of times faster than traditional Monte-Carlo methods with similar accuracy. This significant acceleration, while maintaining high accuracy, highlights the considerable potential of our methods to enhance both the efficiency and scalability of financial derivatives computation.

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