Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169130
Title: Empirical tail risk management with model-based annealing random search
Authors: Fan, Qi
Tan, Ken Seng
Zhang, Jinggong
Keywords: Business::Finance
Issue Date: 2023
Source: Fan, Q., Tan, K. S. & Zhang, J. (2023). Empirical tail risk management with model-based annealing random search. Insurance: Mathematics and Economics, 110, 106-124. https://dx.doi.org/10.1016/j.insmatheco.2023.02.005
Project: 04INS000509C300
RG55/20
Journal: Insurance: Mathematics and Economics
Abstract: Tail risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) are popularly accepted criteria for financial risk management, but are usually difficult to optimize. Especially for VaR, it generally leads to a non-convex NP-hard problem which is computationally challenging. In this paper we propose the use of model-based annealing random search (MARS) method in tail risk optimization problems. The MARS, which is a gradient-free and flexible method, can widely be applied to solving many financial and insurance problems under mild mathematical conditions. We use a weather index insurance design problem with tail risk measures including VaR, CVaR and Entropic Value at Risk (EVaR) as the objective function to demonstrate the viability and effectiveness of MARS. We conduct an empirical analysis in which we use a set of weather variables to hedge against corn production losses in Illinois. Numerical results show that the proposed optimization scheme effectively helps corn producers to manage their tail risk.
URI: https://hdl.handle.net/10356/169130
ISSN: 0167-6687
DOI: 10.1016/j.insmatheco.2023.02.005
Schools: Nanyang Business School 
Rights: © 2023 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:NBS Journal Articles

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