Abstract
The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods.
| Original language | English |
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| Title of host publication | Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering |
| Place of Publication | Port Jefferson, NY |
| Publisher | IEEE |
| Pages | 240-249 |
| Number of pages | 10 |
| ISBN (Print) | 0780356632 |
| DOIs | |
| Publication status | Published - 1999 |
| Event | IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering - Duration: 1 Jan 1999 → 1 Jan 1999 |
Conference
| Conference | IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering |
|---|---|
| Period | 1/01/99 → 1/01/99 |
Bibliographical note
©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Keywords
- deficiencies
- stationary models
- financial time series
- non-stationarity
- stationary regimes
- generator switches
- financial markets
- hidden Markov model
- financial data sets
- benchmark methods