Graduation Semester and Year
2020
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Mathematics
Department
Mathematics
First Advisor
Andrzej Korzeniowski
Abstract
Risk measures emerge in fields such as economics, insurance, finance and are concerned with a stochastic representation of uncertainties stemming from the unpredictability of the real world events. In essence, risk analysis amounts to quantifying the chances of undesirable events and developing a model that limits the impact of potential losses. Assets and liabilities in the Insurance industry, as well as financial goals of Investment companies rely on calculating the probability that their respective portfolios satisfy the preset constraints. On the flip side, risk measures serve both industries by providing optimal strategies for minimizing losses. Our research is concerned with Distorted Risk Measures (DRMs) in stochastic optimization regarding decisions about the size of the risk exposure. We extend the classical Lundberg Risk Model to the case of periodic reinsurance with investment.
Keywords
Lundberg model, Periodic reinsurance with investment, Value-at-Risk, Conditional tail expectation, Distortion risk measures with constraints
Disciplines
Mathematics | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
N'Gozan, Benie Justine, "STOCHASTIC RISK MEASURES FOR THE LUNDBERG MODEL WITH REINSURANCE AND INVESTMENT" (2020). Mathematics Dissertations. 210.
https://mavmatrix.uta.edu/math_dissertations/210
Comments
Degree granted by The University of Texas at Arlington