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

Academic,
Seminar / Lecture
 
Open to the Public

Dilip Madan

You are invited to attend a lecture titled:
Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging and Fitting
Speaker: Dilip Madan, Professor of Mathematical Finance, Robert H. Smith School of Business

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 at 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.

Bio

Dilip Madan is Professor of Mathematical Finance at the Robert H. Smith School of Business. Currently he serves as a consultant to Morgan Stanley, Norges Bank Investment Management and MarketToppers. He has also consulted with Citigroup, Bloomberg, the FDIC, Wachovia Securities, Caspian Capital and Meru Capital. He is a founding member and Past President of the Bachelier Finance Society. He received the 2006 von Humboldt award in applied mathematics, was the 2007 Risk Magazine Quant of the year, received the 2008 Medal for Science from the University of Bologna and held the 2010 Eurandom Chair. He was inducted into the Circle of Discovery of the College of Computer, Mathematical and Natural Sciences in 2014. He has published over 150 papers and serves on the Advisory Board of Mathematical Finance, is Co-editor of the Review of Derivatives Research, and an Associate Editor for Quantitative Finance among a number of other Journals.

Light refreshments will be served.