MACHINE LEARNING METHODS FOR PRICING FINANCIAL DERIVATIVES
By Lei Fan, Justin Sirignano
Rating
1398
Battle Count: 130
Relevance
9/10
Highly relevant for options pricing and hedging in quantitative trading strategies
Implementation Complexity
7/10
Requires advanced knowledge of stochastic calculus and deep learning
Reproducibility
4/5
The paper provides detailed methodology and algorithms, but full code is not provided
About this paper
Methodology: Neural Network SDE Models. Problem types: Regression, Time Series Forecasting.
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