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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Option pricing with stochastic volatility model versus machine learning algorithms
Organization Unit
Authors
  • Wenxuan Zhang
Supervisors
  • Erich Walter Farkas
  • Alexander Smirnow
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Number of Pages 44
Date 2021
Abstract Text This thesis is about the pricing performance and strategy development based on pricing deviation of the machine learning algorithms of European options and convertible bonds. The classical models such as the Black-Scholes model and the Heston model usually make some unrealistic economical and statistical assumptions, and suffer from huge computational power required for parametric calibration. Regarding the inevitable flaws of traditional models, this article attempts to break out of the constraints of formula models and explore the issue of derivative pricing from the perspective of non-parametric models. The empirical analysis is based on data sets of China's 50ETF options and convertible bonds. A least squared error fitness function is used to calibrate the parameters for the Heston model. It shows that machine learning algorithms, especially the XGBoost method, not only has higher pricing accuracy and less calibration time, but also has some pricing power for abnormal prices in the market. In order to prove that the results obtained are not just products of over-fitting, this thesis back-tests the corresponding arbitrage strategy based on pricing deviation. The result shows that the XGBoost method has better annualized returns and risk control.
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