| dc.identifier.uri | http://hdl.handle.net/11401/78217 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.type | Dissertation | |
| dcterms.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. | |
| dcterms.available | 2018-06-20T18:03:27Z | |
| dcterms.contributor | Stoyanov, Stoyan | en_US |
| dcterms.contributor | Glimm, James | en_US |
| dcterms.contributor | Kim, Young Shin (Aaron) | en_US |
| dcterms.contributor | Xiao, Keli | en_US |
| dcterms.creator | Sun, Jinwen | |
| dcterms.dateAccepted | 2018-06-20T18:03:27Z | |
| dcterms.dateSubmitted | 2018-06-20T18:03:27Z | |
| dcterms.description | Department of Applied Mathematics and Statistics | en_US |
| dcterms.extent | 103 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/78217 | |
| dcterms.issued | 2017-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2018-06-20T18:03:27Z (GMT). No. of bitstreams: 1
Sun_grad.sunysb_0771E_13506.pdf: 5981848 bytes, checksum: 61d6fbde82fe73163b0417bc61d79da6 (MD5)
Previous issue date: 12 | en |
| dcterms.subject | Applied mathematics | |
| dcterms.title | Are Market Shocks Predictable? Evidence from High-Frequency Scenarios. | |
| dcterms.type | Dissertation | |
