Summary of Pricing American Options Using Machine Learning Algorithms, by Prudence Djagba and Callixte Ndizihiwe
Pricing American Options using Machine Learning Algorithms
by Prudence Djagba, Callixte Ndizihiwe
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning algorithms are applied to pricing American options using Monte Carlo simulations, addressing complexities like early exercise and non-linear payoff structures. Traditional models, such as Black-Scholes-Merton, often fail to accurately price these options. This study evaluates machine learning models, including neural networks and decision trees, showing their potential to outperform traditional approaches. By integrating machine learning with Monte Carlo simulations, this research aims to improve accuracy and efficiency of option pricing, offering insights into quantitative finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to make it easier to price American options in the stock market. Traditional methods are not very good at doing this because they don’t account for things like people being able to buy or sell early. The researchers used special computer programs called Monte Carlo simulations and machine learning algorithms to try to do better. They tested different models, including neural networks and decision trees, and found that one type of model worked best. This new method can give more accurate prices and is a step forward in understanding finance. |
Keywords
» Artificial intelligence » Machine learning
