Type
Text
Type
Dissertation
Advisor
Stoyanov, Stoyan | Glimm, James | Kim, Young Shin (Aaron) | Xiao, Keli
Date
2017-12-01
Keywords
Applied mathematics
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/11401/78217
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Exploring the possibility of market shocks forecasting is a significant topic for both academia and practice in finance. Measured by innovations generated from conventional time series models, market shocks are being assumed to follow specific distributions in the extensive literature. However, inconsistency occurs all the time in the real-world data. In this thesis, we propose and then apply a mutual information-based ARMA-GARCH-Artificial Neural Network framework to predict the direction of innovations under a high-frequency scenario. We leverage on the strength of neural networks in addressing complex pattern recognition problems. We study performances of two variable/feature selection techniques based on mutual information. Moreover, we conduct a series of comprehensive tests based on U.S. stock market high-frequency data to validate the effectiveness of our framework. | 103 pages
Recommended Citation
Sun, Jinwen, "Are Market Shocks Predictable? Evidence from High-Frequency Scenarios." (2017). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3711.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3711