Roberto Torresetti

Università degli Studi di Milano

Adjunct Professor

via Festa del Perdono, 7

Milano

Italy

Intesa SanPaolo

Finance & Investments

Milan

Italy

SCHOLARLY PAPERS

13

DOWNLOADS
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Top 14,041

in Total Papers Downloads

6,631

SSRN CITATIONS
Rank 17,800

SSRN RANKINGS

Top 17,800

in Total Papers Citations

5

CROSSREF CITATIONS

74

Scholarly Papers (13)

1.

Implied Correlation in CDO Tranches: A Paradigm to be Handled with Care

Number of pages: 9 Posted: 25 Nov 2006
Roberto Torresetti, Damiano Brigo and Andrea Pallavicini
Università degli Studi di Milano, Imperial College London - Department of Mathematics and Intesa Sanpaolo
Downloads 1,338 (28,540)
Citation 8

Abstract:

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Implied Correlation, Base Correlation, Compound Correlation, Expected Tranche Loss, DJ iTraxx, CDX, CDO Tranche, Back-Test, No-Arbitrage Conditions

2.

Risk Neutral Versus Objective Loss Distribution and CDO Tranches Valuation

Number of pages: 15 Posted: 14 May 2006
Roberto Torresetti, Damiano Brigo and Andrea Pallavicini
Università degli Studi di Milano, Imperial College London - Department of Mathematics and Intesa Sanpaolo
Downloads 1,136 (36,189)
Citation 11

Abstract:

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Default Rate distribution, CDO, CDO tranches, Perfect Copula, Transition Matrices, Rating Classes, Risk Premium, Recovery Rate

3.

Credit Models and the Crisis, or: How I Learned to Stop Worrying and Love the CDOs

Number of pages: 66 Posted: 31 Dec 2009 Last Revised: 18 Feb 2010
Damiano Brigo, Andrea Pallavicini and Roberto Torresetti
Imperial College London - Department of Mathematics, Intesa Sanpaolo and Università degli Studi di Milano
Downloads 1,078 (39,088)
Citation 5

Abstract:

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Credit Crisis, Credit Derivatives, Gaussian Copula Model, Implied Correlation, Base Correlation, Compound Correlation, Implied Copula, Dynamic Loss Model, GPL Model, Arbitrage Free Models, Collateralized Debt Obligations, DJi-Traxx and CDX Tranches, CDO Tranche Calibration

4.

Calibration of CDO Tranches with the Dynamical Generalized-Poisson Loss Model

Number of pages: 35 Posted: 11 May 2006 Last Revised: 30 Apr 2010
Damiano Brigo, Andrea Pallavicini and Roberto Torresetti
Imperial College London - Department of Mathematics, Intesa Sanpaolo and Università degli Studi di Milano
Downloads 834 (55,842)
Citation 53

Abstract:

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Loss Distribution, Loss Dynamics, Calibration, CDO Tranches and Tranchelets, Generalized Poisson Processes, Gamma intensity, Spread Dynamics

5.

Implied Expected Tranched Loss Surface from CDO Data

Number of pages: 13 Posted: 28 Sep 2006
Roberto Torresetti, Damiano Brigo and Andrea Pallavicini
Università degli Studi di Milano, Imperial College London - Department of Mathematics and Intesa Sanpaolo
Downloads 559 (93,521)
Citation 17

Abstract:

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expected tranche loss, loss surface, implied correlation, CDO, tranches, interpolation

6.

Stressing Rating Criteria Allowing for Default Clustering: the CPDO case

Number of pages: 37 Posted: 15 Jan 2008 Last Revised: 09 Sep 2009
Roberto Torresetti and Andrea Pallavicini
Università degli Studi di Milano and Intesa Sanpaolo
Downloads 332 (171,139)
Citation 2

Abstract:

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CPDO Rating, Rating Arbitrage, Structured Finance, Loss Distribution, Loss Dynamics, Cluster Default Dynamics, Gap Risk

7.

Handling Model Uncertainty in the Estimation of Satellite Models in Bank’s Credit Risk Projections

Number of pages: 41 Posted: 18 Feb 2021 Last Revised: 28 Mar 2022
Roberto Torresetti and Sergio Caprioli
Università degli Studi di Milano and Intesa SanPaolo SpA
Downloads 298 (191,893)
Citation 1

Abstract:

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Satellite Model, Credit Risk, Probability of Default, PD, Bayesian Estimator, Complexity Prior, LASSO, RIDGE, BIC, Model Averaging.

8.

Default Correlation, Cluster Dynamics and Single Names: The GPCL Dynamical Loss Model

Number of pages: 35 Posted: 14 Jan 2007
Damiano Brigo, Andrea Pallavicini and Roberto Torresetti
Imperial College London - Department of Mathematics, Intesa Sanpaolo and Università degli Studi di Milano
Downloads 293 (195,263)
Citation 6

Abstract:

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Loss Distribution, Loss Dynamics, Single Name Default Dynamics, Cluster Default Dynamics, Calibration, Generalized Poisson Process, Stochastic Intensity, Spread Dynamics, Common Poisson Shock Models

9.

Scaling Operational Loss Data and Its Systemic Risk Implications

Number of pages: 15 Posted: 27 Nov 2013 Last Revised: 12 Feb 2014
Roberto Torresetti and Claudio Nordio
Università degli Studi di Milano and illimity bank
Downloads 244 (234,566)
Citation 2

Abstract:

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Operational Risk, Power Law, Loss Distribution Approach, Advanced Measurement Approach, VaR, Single Loss Approximation, Extreme Value Theory, External Loss Data, Consortium Loss Data, Rescaling, Scaling, Mixture Distribution, Asymptotic Approximation.

10.

Classification and Diversification Benefit for Operational Risk Data

Number of pages: 15 Posted: 15 Nov 2013 Last Revised: 21 Nov 2023
Roberto Torresetti and Giacomo Le Pera
Università degli Studi di Milano and illimity Bank
Downloads 208 (272,939)
Citation 3

Abstract:

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Operational Risk, Classification, Loss Distribution Approach, Advanced Measurement Approach, VaR, Diversification, Correlation, Single Loss Approximation, Extreme Events, Sub-exponential Distribution, Regularly Varying Distributions, Poisson Process, Compound Process, Mixture Distribution

11.

Operational Risk Bias Quanti fication and Correction

Number of pages: 12 Posted: 30 Dec 2014
Roberto Torresetti and Giacomo Le Pera
Università degli Studi di Milano and illimity Bank
Downloads 106 (473,234)

Abstract:

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12.

Truncated Lognormals as a Power Law Mimicry in Operational Risk

Number of pages: 12 Posted: 30 Dec 2014
Roberto Torresetti and Claudio Nordio
Università degli Studi di Milano and illimity bank
Downloads 104 (479,938)
Citation 2

Abstract:

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13.

A comparison of the performance of alternative Machine Learning algorithms on a credit risk dataset

Number of pages: 10 Posted: 09 Mar 2021
Roberto Torresetti
Università degli Studi di Milano
Downloads 101 (489,633)

Abstract:

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Credit Risk, Probability of Default, PD, Machine Learning, Bayesian Model Average, Markov Chain Monte Carlo, MCMC, Regression Trees, Random Forest, Ensemble Learning, AdaBoost, Adaptive Boosting, GrdBoost, Gradient Boosting, Bootstrap Aggregating, Bagging.