Risk management by Markov decision processes
| dc.contributor.author | Liang, You | |
| dc.contributor.examiningcommittee | Johnson, Brad (Statistics) Thavaneswaran, Aerambamoorthy (Statistics) Hao, Xuemiao (Actuarial Studies) Zhao, Yiqiang (Carleton University) | en_US |
| dc.contributor.supervisor | Wang, Xikui (Statistics) Yi, Yanqing (Statistics) | en_US |
| dc.date.accessioned | 2015-09-18T14:33:13Z | |
| dc.date.available | 2015-09-18T14:33:13Z | |
| dc.date.issued | 2015 | |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | A very important and powerful tool in the study of mathematical finance including risk management is the model of Markov decision processes. My PhD research in the area of risk management is focused on three major problems: (1) risk sensitive partially observable Markov decision processes, (2) dynamic risk measures, (3) dynamic deviation measures. Our first part is to extend the classic model of Markov decision processes simultaneously in two directions: partially observable states and risk sensitivity. Another direction of extending the classic model of Markov decision processes is to incorporate risk measures with the optimality criteria of the model. Our second part of the thesis is to use the model of Markov decision processes to characterize and derive new forms of dynamic risk measures. The third part of the thesis is to apply the model of Markov decision processes to characterize and derive a sequence of dynamic deviation measures. | en_US |
| dc.description.note | October 2015 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/30829 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Markov decision processes, Risk measures, Deviation measures | en_US |
| dc.title | Risk management by Markov decision processes | en_US |
| dc.type | doctoral thesis | en_US |
| local.subject.manitoba | yes | en_US |