Projects per year
Personal profile
Research profile
Peter researches machine learning methods for Finance. More specifically, he develops fast and accurate methods for option valuation and calibration. In his research, he combines classical option theory methods with modern methods such as neural networks, tree regression, and differential machine learning to produce powerful tools for trading and risk management. He hopes these tools can improve solutions to complex, real-world problems, changing the world.
Collaborations from the last five years
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Projects
- 1 Active
Research output
- 2 Preprint
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NN de-Americanization: A Fast and Efficient Calibration Method for American-Style Options
Lind, P. P. & Gatheral, J., 14 Nov 2023, SSRN: Social Science Research Network, 32 p.Research output: Working paper/Preprint › Preprint
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Overcoming the Feature Selection Issue in the Pricing of American Options
Lind, P. P., 28 Jan 2022, SSRN: Social Science Research Network, 24 p.Research output: Working paper/Preprint › Preprint