A Black-Scholes-integrated Gaussian Process Model for American Option Pricing
Abstract
Acknowledging the lack of option pricing models that simultaneously have high
prediction power, high computational efficiency, and interpretations that abide by
financial principles, we suggest a Black-Scholes-integrated Gaussian process (BSGP)
learning model that is capable of making accurate predictions backed with fundamental
financial principles. Most data-driven models boast strong computational power at
the expense of inferential results that can be explained with financial principles.
Vice
versa, most closed-form stochastic models (principle-driven) exhibit inferential results
at the cost of computational efficiency. By integrating the Black-Scholes computed
price for an equivalent European option into the mean function of the Gaussian
process, we can design a learning model that emphasizes the strengths of both data-
driven and principle-driven approaches. Using American (SPY) call and put option
price data from 2019 May to June, we condition the Black-Scholes mean Gaussian
Process prior with observed data to derive the posterior distribution that is used
to
predict American option prices. Not only does the proposed BSGP model provide
accurate predictions, high computational efficiency, and interpretable results, but
it also captures the discrepancy between a theoretical option price approximation
derived by the Black-Scholes and predicted price from the BSGP model.
Type
Honors thesisDepartment
Statistical SciencePermalink
https://hdl.handle.net/10161/21549Citation
Kim, Chiwan (2020). A Black-Scholes-integrated Gaussian Process Model for American Option Pricing. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/21549.Collections
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