A Latent Variable Model for Financial Range
Abstract
In this paper we introduce a latent variable based model for the dynamics of financial range, the stochastic conditional range (SCR). We propose to estimate its parameters by Kalman filter, indirect inference and simulated maximum likelihood depending on the hypotheses on the distributional form of the innovations. The model is applied to a large subset of the S&P 500 components. A comparison of its fitting and forecasting abilities with the conditional autoregressive range (CARR) model shows that the new approach can provide a competitive alternative.
Full Text: PDF DOI: 10.15640/jfbm.v6n2a2
Abstract
In this paper we introduce a latent variable based model for the dynamics of financial range, the stochastic conditional range (SCR). We propose to estimate its parameters by Kalman filter, indirect inference and simulated maximum likelihood depending on the hypotheses on the distributional form of the innovations. The model is applied to a large subset of the S&P 500 components. A comparison of its fitting and forecasting abilities with the conditional autoregressive range (CARR) model shows that the new approach can provide a competitive alternative.
Full Text: PDF DOI: 10.15640/jfbm.v6n2a2
Browse Journals
Journal Policies
Information
Useful Links
- Call for Papers
- Submit Your Paper
- Publish in Your Native Language
- Subscribe the Journal
- Frequently Asked Questions
- Contact the Executive Editor
- Recommend this Journal to Librarian
- View the Current Issue
- View the Previous Issues
- Recommend this Journal to Friends
- Recommend a Special Issue
- Comment on the Journal
- Publish the Conference Proceedings
Latest Activities
Resources
Visiting Status
 Today |
42 |
| Yesterday |
147 |
| This Month |
353 |
| Last Month |
4065 |
 All Days |
1112657 |
 Online |
11 |
