Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging and Fitting

15 Pages Posted: 20 Jun 2018

See all articles by Jan De Spiegeleer

Jan De Spiegeleer

RiskConcile

Dilip B. Madan

University of Maryland - Robert H. Smith School of Business

Sofie Reyners

KU Leuven - Department of Mathematics

Wim Schoutens

KU Leuven - Department of Mathematics

Date Written: June 5, 2018

Abstract

In this paper, we show how we can deploy machine learning techniques in the context of traditional quant problems. We illustrate that for many classical problems, we can arrive to speed-ups of several orders of magnitude by deploying machine learning techniques based on Gaussian process regression. The price we have to pay for this extra speed is some loss of accuracy. However, we show that this reduced accuracy is often well within reasonable limits and hence very acceptable from a practical point of view.

The concrete examples concern fitting and estimation. In the fitting context, we fit sophisticated Greek profiles and summarize implied volatility surfaces. In the estimation context, we reduce computation times for the calculation of vanilla option values under advanced models, the pricing of American options and the pricing of exotic options under models beyond the Black-Scholes setting.

Keywords: Machine learning, Derivatives, Hedging, Implied volatility

JEL Classification: C60, G10

Suggested Citation

De Spiegeleer, Jan and Madan, Dilip B. and Reyners, Sofie and Schoutens, Wim, Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging and Fitting (June 5, 2018). Available at SSRN: https://ssrn.com/abstract=3191050 or http://dx.doi.org/10.2139/ssrn.3191050

Jan De Spiegeleer

RiskConcile ( email )

Kapeldreef 60
Leuven, 3000
Belgium
492227143 (Phone)

HOME PAGE: http://www.riskconcile.com

Dilip B. Madan

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States
301-405-2127 (Phone)
301-314-9157 (Fax)

Sofie Reyners

KU Leuven - Department of Mathematics ( email )

Celestijnenlaan 200 B
Leuven, B-3001
Belgium

Wim Schoutens (Contact Author)

KU Leuven - Department of Mathematics ( email )

Celestijnenlaan 200 B
Leuven, B-3001
Belgium

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