Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features

Nystrup, Peter, Kolm, Petter N. and Lindstrom, Erik, "Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features." The Journal of Financial Data Science 2.3 (2020).

Posted: 5 Jun 2020 Last revised: 17 Jun 2020

See all articles by Peter Nystrup

Peter Nystrup

Lund University; Technical University of Denmark - Department of Applied Mathematics and Computer Science

Petter N. Kolm

New York University (NYU) - Courant Institute of Mathematical Sciences

Erik Lindstrom

Lund University

Date Written: May 6, 2020

Abstract

In many financial applications it is important to classify time series data without any latency while maintaining persistence in the identified states. We propose a greedy online classifier that contemporaneously determines which hidden state a new observation belongs to without the need to parse historical observations and without compromising persistence. Our classifier is based on the idea of clustering temporal features while explicitly penalizing jumps between states by a fixed-cost regularization term that can be calibrated to achieve a desired level of persistence. Through a series of return simulations, we show that in most settings our new classifier remarkably obtains a higher accuracy than the correctly specified maximum likelihood estimator. We illustrate that the new classifier is more robust to misspecification and yields state sequences that are significantly more persistent both in and out of sample. We demonstrate how classification accuracy can be further improved by including features that are based on intraday data. Finally, we apply the new classifier to estimate persistent states of the S&P 500 index.

Keywords: Clustering; Finance; Jump models; Markov-Switching; State sequence estimation; Unsupervised learning

JEL Classification: C22, C38, C51, C58, C61, G11

Suggested Citation

Nystrup, Peter and Kolm, Petter N. and Lindstrom, Erik, Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features (May 6, 2020). Nystrup, Peter, Kolm, Petter N. and Lindstrom, Erik, "Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features." The Journal of Financial Data Science 2.3 (2020). , Available at SSRN: https://ssrn.com/abstract=3594875 or http://dx.doi.org/10.2139/ssrn.3594875

Peter Nystrup

Lund University ( email )

Box 117
Lund, SC Skane S221 00
Sweden

Technical University of Denmark - Department of Applied Mathematics and Computer Science ( email )

Denmark

Petter N. Kolm (Contact Author)

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY 10012
United States

Erik Lindstrom

Lund University ( email )

Box 117
Lund, SC Skane S221 00
Sweden

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