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Sound deposit insurance pricing using a machine learning approach

Assa, H., Pouralizadeh, M., Badamchizadeh, A. (2019) Sound deposit insurance pricing using a machine learning approach. Risks, 7 (2). ISSN 2227-9091. (doi:10.3390/risks7020045) (KAR id:87560)


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. © 2019 by the author. Licensee MDPI, Basel, Switzerland.

Item Type: Article
DOI/Identification number: 10.3390/risks7020045
Uncontrolled keywords: deposit insurance; implied volatility; static arbitrage; parameterization; machine learning; calibration
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Hirbod Assa
Date Deposited: 28 Apr 2021 15:03 UTC
Last Modified: 04 Mar 2024 17:31 UTC
Resource URI: (The current URI for this page, for reference purposes)

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