Option Pricing with Machine Learning: An Application of Shanghai Crude Oil
(2024) DABN01 20241Department 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:
http://lup.lub.lu.se/student-papers/record/9154497
- author
- Pan, Shuiqing LU
- supervisor
- organization
- course
- DABN01 20241
- year
- 2024
- 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}}, }