Sound Deposit Insurance Pricing Using a Machine Learning Approach
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,...
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doaj-24bbb667d4f64cedbb1658408c606d4b2020-11-24T21:24:31ZengMDPI AGRisks2227-90912019-04-01724510.3390/risks7020045risks7020045Sound Deposit Insurance Pricing Using a Machine Learning ApproachHirbod Assa0Mostafa Pouralizadeh1Abdolrahim Badamchizadeh2Mathematical Sciences Building, University of Liverpool, Liverpool L69 7ZL, UKDepartment of Statistics, Faculty of Mathematical Science and Computer, Allameh Tabataba’i University, Tehran 1489684511, IranDepartment of Statistics, Faculty of Mathematical Science and Computer, Allameh Tabataba’i University, Tehran 1489684511, IranWhile 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.https://www.mdpi.com/2227-9091/7/2/45deposit insuranceimplied volatilitystatic arbitrageparameterizationmachine learningcalibration |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hirbod Assa Mostafa Pouralizadeh Abdolrahim Badamchizadeh |
spellingShingle |
Hirbod Assa Mostafa Pouralizadeh Abdolrahim Badamchizadeh Sound Deposit Insurance Pricing Using a Machine Learning Approach Risks deposit insurance implied volatility static arbitrage parameterization machine learning calibration |
author_facet |
Hirbod Assa Mostafa Pouralizadeh Abdolrahim Badamchizadeh |
author_sort |
Hirbod Assa |
title |
Sound Deposit Insurance Pricing Using a Machine Learning Approach |
title_short |
Sound Deposit Insurance Pricing Using a Machine Learning Approach |
title_full |
Sound Deposit Insurance Pricing Using a Machine Learning Approach |
title_fullStr |
Sound Deposit Insurance Pricing Using a Machine Learning Approach |
title_full_unstemmed |
Sound Deposit Insurance Pricing Using a Machine Learning Approach |
title_sort |
sound deposit insurance pricing using a machine learning approach |
publisher |
MDPI AG |
series |
Risks |
issn |
2227-9091 |
publishDate |
2019-04-01 |
description |
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. |
topic |
deposit insurance implied volatility static arbitrage parameterization machine learning calibration |
url |
https://www.mdpi.com/2227-9091/7/2/45 |
work_keys_str_mv |
AT hirbodassa sounddepositinsurancepricingusingamachinelearningapproach AT mostafapouralizadeh sounddepositinsurancepricingusingamachinelearningapproach AT abdolrahimbadamchizadeh sounddepositinsurancepricingusingamachinelearningapproach |
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1725987686674595840 |