Risk Magazine Cutting Edge Article | Machine Learning: Deep Asymptotics
Machine Learning: Deep Asymptotics
Artificial neural networks have recently been proposed as accurate and fast approximators in various derivatives pricing applications. Their extrapolation behavior cannot be controlled due to the complex functional forms typically involved. In this new research, Drs. Alexandre Antonov, Michael Konikov and Vladimir Piterbarg overcome this significant limitation and develop a new type of neural network that incorporates large-value asymptotics, allowing explicit control over extrapolation. Complete the form to download this Risk.net research paper, “Deep Asymptotics”.
Authors: Dr. Alexandre Antonov, Ph.D., Dr. Michael Konikov, Dr. Vladimir Piterbarg
Artificial neural networks have recently been proposed as accurate and fast approximators in various derivatives pricing applications. Their extrapolation behavior cannot be controlled due to the complex functional forms typically involved. In this new research, Drs. Alexandre Antonov, Michael Konikov and Vladimir Piterbarg overcome this significant limitation and develop a new type of neural network that incorporates large-value asymptotics, allowing explicit control over extrapolation. Complete the form to download this Risk.net research paper, “Deep Asymptotics”.
Authors: Dr. Alexandre Antonov, Ph.D., Dr. Michael Konikov, Dr. Vladimir Piterbarg
Authors
Dr. Vladimir Piterbarg
Dr. Piterbarg is a Managing Director and the Head of Quantitative Analytics at Barclays Capital. Before joining Barclays Capital in March 2005, he was a co-head of quantitative research for Bank of America, where he had worked for 8 years. Vladimir Piterbarg’s main areas of expertise are the modelling of exotic interest rate and hybrid derivatives.
Among his many published papers, Dr. Piterbarg authored "Stochastic volatility model with time-dependent skew," (Applied Mathematical Finance, 12(2): 147-185, June 2005), which introduced stochastic volatility to the LIBOR market model. He was named Quant of the Year 2006 by Risk Magazine, was the Associate Editor of the Journal of Computational Finance, and was Co-Editor (along with Leif B.G. Andersen) of the Interest Rate Modeling section for the Encyclopedia of Quantitative Finance.
Dr. Piterbarg holds a Ph.D. in Mathematics (Stochastic Calculus) from University of Southern California.
Dr. Alexandre Antonov
Dr. Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997 and joined Numerix in 1998, where he currently works as a Senior Vice President of Quantitative Research. His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA. Dr. Antonov is a published author for multiple publications in mathematical finance, including RISK magazine and a frequent speaker at financial conferences.
Dr. Michael Konikov
Dr. Michael Konikov is a Senior Vice President and Head of Quantitative Development at Numerix, where he manages a team responsible for the development and delivery of models in Numerix software. Previously, he worked at Citigroup, Barclays, and Bloomberg in quantitative research and desk quant roles. He completed his PhD in mathematical finance at the University of Maryland College Park, concentrating in particular on the application of pure jump processes to option pricing. Dr. Konikov's publications cover diverse asset classes ranging from equity to interest rates and credit.
