Mathematical Finance & Financial Data Science Seminar
Feature Selection in Jump Models
Speaker: Peter Nystrup, InCommodities A/S
Date: Tuesday, March 23, 2021, 5:30 p.m.
Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account. In this talk, we propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. The resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.
This is joint work with Petter Kolm and Erik Lindstrom.
Peter Nystrup is a Quantitative Analyst at InCommodities, an energy-trading company in Denmark. He has previously been a Postdoctoral Fellow in the Division of Mathematical Statistics at Lund University in Sweden and in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark, and a Visiting Researcher at NYU Courant and Stanford University. He has worked in equity sales at Nordea Markets, in the investment department at Danish pension fund Sampension, and as an external consultant on advanced analytics at the energy company Ørsted.
Dr. Nystrup earned his B.Sc. in Engineering degree in Mathematics and Technology from the Technical University of Denmark (DTU) in 2012, followed by a M.Sc. (Hons.) in Engineering degree in Mathematical Modeling and Computation in 2014. In 2018, he was awarded the Ph.D. degree in Engineering from DTU upon completion of a research project on dynamic asset allocation and identification of regime shifts in financial time series. His research has been published in leading journals covering topics from quantitative finance and portfolio management to forecasting, optimization, and operations research.
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