Type
Text
Type
Dissertation
Advisor
Frey, Robert J. | Rachev, Svetlozar | Douady, Raphael | Djuric, Petar M.
Date
2017-12-01
Keywords
Bayesian Filtering Estimation | Applied mathematics | Dynamic Factor Model | Statistics | Electrical engineering | Online Parameter Learning | Particle Filter | Regime Switching | Statistical Arbitrage Strategy
Department
Department of Applied Mathematics and Statistics
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/78240
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Statistical factor analysis has been widely used in many areas of investment science such as risk management, portfolio selection, trading strategies, ect.. This dissertation mainly investigates the estimation of dynamic factor model in the Bayesian framework, using the techniques of particle filter with online parameter learning such as marginalized particle filter and particle learning. We also compare our results with the offline conventional method such as Kalman filter combined with EM algorithm in root mean squared error criterion. In the real data analysis, regime switching or structure break in the factor structure will make the estimation of static model difficult and lead to the problem of model misspecification. For solving this issue, we construct a regime switching dynamic factor model and compare its performance with conventional method using EM algorithm in the context of statistical arbitrage trading strategy. | 104 pages
Recommended Citation
Mu, Yu, "Bayesian Filtering Estimation of Statistical Dynamic Factor Model" (2017). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3734.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3734