Probabilistic Sequence Modeling

Matthew F. Dixon, Igor Halperin and Paul Bilokon
Additional contact information
Matthew F. Dixon: Illinois Institute of Technology, Department of Applied Mathematics
Igor Halperin: New York University, Tandon School of Engineering
Paul Bilokon: Imperial College London, Department of Mathematics

Chapter Chapter 7 in Machine Learning in Finance, 2020, pp 221-238 from Springer

Abstract: Abstract This chapter presents a powerful class of probabilistic models for financial data. Many of these models overcome some of the severe stationarity limitations of the frequentist models in the previous chapters. The fitting procedure demonstrated is also different—the use of Kalman filtering algorithms for state-space models rather than maximum likelihood estimation or Bayesian inference. Simple examples of hidden Markov models and particle filters in finance and various algorithms are presented.

Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41068-1_7

Ordering information: This item can be ordered from
http://www.springer.com/9783030410681

DOI: 10.1007/978-3-030-41068-1_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().