A hybrid deep learning approach using Price Approximator and Calibration Correction networks improves the efficiency and accuracy of Heston model calibration on S&P 500 option data.
Deeply Learning Derivatives
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abstract
This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy.
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math.AP 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
A hybrid deep learning approach using Price Approximator and Calibration Correction networks improves the efficiency and accuracy of Heston model calibration on S&P 500 option data.