Option Pricing by Fuzzy Logic Based Signal Processing
نویسندگان
چکیده
One of the best examples of mathematically rigorous signal processing in finance is the Black-Scholes model for price evolution of financial options. To address the same problem, this paper proposes a Takagi-SugenoKang (TSK) fuzzy rule-based option pricing model that requires only a small number of rules to describe highly complex financial signals such as option prices. The findings for this data-driven approach indicate that the TSK model presents a robust option pricing tool that is superior to an array of well-known parametric models from the literature, including the Black-Scholes model. In addition, its predictive performance is consistently no worse than that of a non-parametric feedforward neural network model.
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