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Option Pricing with Machine Learning: An Application of Shanghai Crude Oil

Pan, Shuiqing LU (2024) DABN01 20241
Department of Economics
Department of Statistics
Abstract
Options, as complex derivative assets, play a crucial role in real life for hedging risks and speculating profits. Their high volatility and the challenging nature of price predictions make them not only essential tools for financial markets but also a compelling research topic for scholars. Building on previous studies, this thesis focuses on Shanghai Crude Oil options, offering insights into their pricing dynamics. In addition to traditional pricing features, such as strike price, time to maturity, and volatility, this thesis explores the impact of macroeconomic factors on crude oil prices to determine whether these factors can also influence option pricing. By employing machine learning algorithms, the performances of these models are... (More)
Options, as complex derivative assets, play a crucial role in real life for hedging risks and speculating profits. Their high volatility and the challenging nature of price predictions make them not only essential tools for financial markets but also a compelling research topic for scholars. Building on previous studies, this thesis focuses on Shanghai Crude Oil options, offering insights into their pricing dynamics. In addition to traditional pricing features, such as strike price, time to maturity, and volatility, this thesis explores the impact of macroeconomic factors on crude oil prices to determine whether these factors can also influence option pricing. By employing machine learning algorithms, the performances of these models are compared with the Binomial Tree model. The findings show that XGBoost model outperforms the benchmark in both cases of Call and Put options, while other models’ performance shows less robustness. (Less)
Please use this url to cite or link to this publication:
author
Pan, Shuiqing LU
supervisor
organization
course
DABN01 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
Options Pricing, Shanghai Crude Oil, Machine Learning, Macroeconomic Factors
language
English
id
9154497
date added to LUP
2024-09-24 08:36:00
date last changed
2024-09-24 08:36:00
@misc{9154497,
  abstract     = {{Options, as complex derivative assets, play a crucial role in real life for hedging risks and speculating profits. Their high volatility and the challenging nature of price predictions make them not only essential tools for financial markets but also a compelling research topic for scholars. Building on previous studies, this thesis focuses on Shanghai Crude Oil options, offering insights into their pricing dynamics. In addition to traditional pricing features, such as strike price, time to maturity, and volatility, this thesis explores the impact of macroeconomic factors on crude oil prices to determine whether these factors can also influence option pricing. By employing machine learning algorithms, the performances of these models are compared with the Binomial Tree model. The findings show that XGBoost model outperforms the benchmark in both cases of Call and Put options, while other models’ performance shows less robustness.}},
  author       = {{Pan, Shuiqing}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Option Pricing with Machine Learning: An Application of Shanghai Crude Oil}},
  year         = {{2024}},
}