Empirical tail risk management with model-based annealing random search
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 th...
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sg-ntu-dr.10356-1691302023-07-03T02:58:16Z Empirical tail risk management with model-based annealing random search Fan, Qi Tan, Ken Seng Zhang, Jinggong Nanyang Business School Business::Finance Tail Risk Random Search 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. Ministry of Education (MOE) Nanyang Technological University Tan acknowledges research funding from Nanyang Technological University, Singapore University Grant and President’s Chair in Actuarial Risk Management. Zhang acknowledges the research funding support from the Nanyang Technological University Startup Grant (04INS000509C300) and the Singapore Ministry of Education Academic Research Fund Tier 1 Grant (RG55/20). 2023-07-03T02:58:16Z 2023-07-03T02:58:16Z 2023 Journal Article 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 0167-6687 https://hdl.handle.net/10356/169130 10.1016/j.insmatheco.2023.02.005 2-s2.0-85149459721 110 106 124 en 04INS000509C300 RG55/20 Insurance: Mathematics and Economics © 2023 Elsevier B.V. All rights reserved. |
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Business::Finance Tail Risk Random Search Fan, Qi Tan, Ken Seng Zhang, Jinggong Empirical tail risk management with model-based annealing random search |
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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. |
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Nanyang Business School |
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Nanyang Business School Fan, Qi Tan, Ken Seng Zhang, Jinggong |
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Article |
author |
Fan, Qi Tan, Ken Seng Zhang, Jinggong |
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Fan, Qi |
title |
Empirical tail risk management with model-based annealing random search |
title_short |
Empirical tail risk management with model-based annealing random search |
title_full |
Empirical tail risk management with model-based annealing random search |
title_fullStr |
Empirical tail risk management with model-based annealing random search |
title_full_unstemmed |
Empirical tail risk management with model-based annealing random search |
title_sort |
empirical tail risk management with model-based annealing random search |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/169130 |
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1772828176110583808 |