Bertrand Hassani

Université Paris I Panthéon-Sorbonne

17, rue de la Sorbonne

Paris, IL 75005

France

University College London - Department of Computer Science

United Kingdom

SCHOLARLY PAPERS

10

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SSRN CITATIONS
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Top 47,937

in Total Papers Citations

7

CROSSREF CITATIONS

5

Scholarly Papers (10)

1.

Standardized Measurement Approach for Operational Risk: Pros and Cons

Number of pages: 18 Posted: 04 Jun 2016
Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Macquarie University, Université Paris I Panthéon-Sorbonne and University College London - Department of Computer Science
Downloads 585 (50,183)
Citation 2

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2.

Credit Risk Analysis Using Machine and Deep Learning Models

University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 08/WP/2018
Number of pages: 32 Posted: 17 Apr 2018
Peter Martey Addo, Dominique Guegan and Bertrand Hassani
Labex ReFi, Université Paris I Panthéon-Sorbonne and Université Paris I Panthéon-Sorbonne
Downloads 514 (59,091)
Citation 4

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Credit risk, Financial regulation, Data Science, Bigdata, Deep learning

3.

A Mathematical Resurgence of Risk Management: An Extreme Modeling of Expert Opinions

Frontiers in Finance and Economics, Vol. 11, No. 1, 25-45, 2014
Number of pages: 21 Posted: 02 Feb 2015
Dominique Guegan and Bertrand Hassani
Université Paris I Panthéon-Sorbonne and Université Paris I Panthéon-Sorbonne
Downloads 176 (186,372)

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Operational risks, EVT, AMA, Expert, Value-at-Risk, Expected Shortfall

Should the Advanced Measurement Approach be Replaced with the Standardized Measurement Approach for Operational Risk?

Journal of Operational Risk, Vol 11, No. 3, pp. 1–49, 2016, DOI: 10.21314/JOP.2016.177
Number of pages: 38 Posted: 14 Jun 2016 Last Revised: 15 Sep 2016
Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Macquarie University, Université Paris I Panthéon-Sorbonne and University College London - Department of Computer Science
Downloads 172 (190,199)
Citation 6

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Operational Risk, Standarised Measurement Approach, Advanced Measurement Approach

5.

The Spectral Stress VaR (SSVaR)

University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 17/WP/2015
Number of pages: 23 Posted: 25 Jun 2015
Dominique Guegan, Bertrand Hassani and Kehan Li
Université Paris I Panthéon-Sorbonne, Université Paris I Panthéon-Sorbonne and Université Paris I Panthéon-Sorbonne
Downloads 85 (320,293)

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Value at Risk, Asymptotic theory, Distribution, Spectral analysis, Stress, Risk measure, Regulation

6.

Risk or Regulatory Capital? Bringing Distributions Back in the Foreground

University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 18/WP/2015
Number of pages: 42 Posted: 25 Jun 2015
Dominique Guegan and Bertrand Hassani
Université Paris I Panthéon-Sorbonne and Université Paris I Panthéon-Sorbonne
Downloads 37 (474,481)

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Risk measures, Sub-additivity, Level of confidence, Extreme value distributions, Financial regulation, aggregation

7.

Societal Biases Reinforcement Through Machine Learning – A Credit Scoring Perspective

Number of pages: 14 Posted: 09 Jul 2020
Bertrand Hassani
Université Paris I Panthéon-Sorbonne
Downloads 13 (613,236)

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SMOTE, Machine Learning, Social Bias, Credit Scoring, Random Forest, SVM

8.

Shapley Allocation, Diversification and Services in Operational Risk

Journal of Operational Risk, Forthcoming
Number of pages: 15 Posted: 02 Mar 2018
Peter Mitic and Bertrand Hassani
Santander Bank and Université Paris I Panthéon-Sorbonne
Downloads 1 (708,149)
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capital value, diversification, game theory, operational risk, service, Shapley.

9.

Risk Appetite in Practice: Vulgaris Mathematica

The IUP Journal of Financial Risk Management, Vol. XII, No. 1, March 2015, pp. 7-22
Posted: 12 Oct 2015
Bertrand Hassani
Université Paris I Panthéon-Sorbonne

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10.

Reconsidering Corporate Ratings

Economic Notes, Vol. 44, Issue 2, pp. 177-209, 2015
Number of pages: 33 Posted: 30 Jun 2015
Bertrand Hassani and Xin Zhao
Université Paris I Panthéon-Sorbonne and Université Paris I Panthéon-Sorbonne
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