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Open AccessArticle

Sound Deposit Insurance Pricing Using a Machine Learning Approach

1
Mathematical Sciences Building, University of Liverpool, Liverpool L69 7ZL, UK
2
Department of Statistics, Faculty of Mathematical Science and Computer, Allameh Tabataba’i University, Tehran 1489684511, Iran
*
Author to whom correspondence should be addressed.
Risks 2019, 7(2), 45; https://doi.org/10.3390/risks7020045
Received: 30 December 2018 / Revised: 7 April 2019 / Accepted: 13 April 2019 / Published: 19 April 2019
(This article belongs to the Special Issue Machine Learning in Insurance)
While the main conceptual issue related to deposit insurances is the moral hazard risk, the main technical issue is inaccurate calibration of the implied volatility. This issue can raise the risk of generating an arbitrage. In this paper, first, we discuss that by imposing the no-moral-hazard risk, the removal of arbitrage is equivalent to removing the static arbitrage. Then, we propose a simple quadratic model to parameterize implied volatility and remove the static arbitrage. The process of removing the static risk is as follows: Using a machine learning approach with a regularized cost function, we update the parameters in such a way that butterfly arbitrage is ruled out and also implementing a calibration method, we make some conditions on the parameters of each time slice to rule out calendar spread arbitrage. Therefore, eliminating the effects of both butterfly and calendar spread arbitrage make the implied volatility surface free of static arbitrage. View Full-Text
Keywords: deposit insurance; implied volatility; static arbitrage; parameterization; machine learning; calibration deposit insurance; implied volatility; static arbitrage; parameterization; machine learning; calibration
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Assa, H.; Pouralizadeh, M.; Badamchizadeh, A. Sound Deposit Insurance Pricing Using a Machine Learning Approach. Risks 2019, 7, 45.

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