Default prediction of SMEs by a generalized extreme value additive model

Raffaella Calabrese; Silvia Angela Osmetti

Default prediction of SMEs by a generalized extreme value additive model

Raffaella Calabrese
, Silvia Angela Osmetti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A new model is proposed for default prediction of Small and Medium Enterprises (SMEs). The main weakness of the scoring models proposed in the literature is not to consider the default as a rare event. To take into account this characteristic, Calabrese and Osmetti (2011) suggested the quantile function of the Generalized Extreme Value (GEV) distribution as a link function in a Generalized Linear Model (GLMs). In the GLMs, the relationship between the independent variable and the predictor is constrained to be linear. Since this assumption is not usually satisfied by scoring models, a Generalized Additive Model (GAM) is suggested with the quantile function of the GEV distribution as link function. Hence, the Generalized Extreme Value Additive (GEVA) model is proposed. Finally, our proposal is applied to empirical data on Italian SMEs. It is obtained that the GEVA model shows a high accuracy for predicting defaults.

Original language	English
Title of host publication	Book of Abstracts, CFE 2012 6th CSDA International and Finantial Econometrics, ERCIM 2012, 5th International Conference of the ERCIM Workin Group on Computing & Statistics
Pages	26
Number of pages	1
Publication status	Published - 2012
Event	CFE-ERCIM 2012 - Oviedo-Spain Duration: 1 Dec 2012 → 3 Dec 2012

Conference

Conference	CFE-ERCIM 2012
City	Oviedo-Spain
Period	1/12/12 → 3/12/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure

Keywords

generalized additive model
generalized extreme value distribution,

Cite this

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title = "Default prediction of SMEs by a generalized extreme value additive model",

abstract = "A new model is proposed for default prediction of Small and Medium Enterprises (SMEs). The main weakness of the scoring models proposed in the literature is not to consider the default as a rare event. To take into account this characteristic, Calabrese and Osmetti (2011) suggested the quantile function of the Generalized Extreme Value (GEV) distribution as a link function in a Generalized Linear Model (GLMs). In the GLMs, the relationship between the independent variable and the predictor is constrained to be linear. Since this assumption is not usually satisfied by scoring models, a Generalized Additive Model (GAM) is suggested with the quantile function of the GEV distribution as link function. Hence, the Generalized Extreme Value Additive (GEVA) model is proposed. Finally, our proposal is applied to empirical data on Italian SMEs. It is obtained that the GEVA model shows a high accuracy for predicting defaults.",

keywords = "generalized additive model, generalized extreme value distribution,, generalized additive model, generalized extreme value distribution,",

author = "Raffaella Calabrese and Osmetti, \{Silvia Angela\}",

year = "2012",

language = "English",

isbn = "978-84-937822-2-1",

pages = "26",

booktitle = "Book of Abstracts, CFE 2012 6th CSDA International and Finantial Econometrics, ERCIM 2012, 5th International Conference of the ERCIM Workin Group on Computing \& Statistics",

note = "CFE-ERCIM 2012 ; Conference date: 01-12-2012 Through 03-12-2012",

}

Default prediction of SMEs by a generalized extreme value additive model. / Calabrese, Raffaella; Osmetti, Silvia Angela.
Book of Abstracts, CFE 2012 6th CSDA International and Finantial Econometrics, ERCIM 2012, 5th International Conference of the ERCIM Workin Group on Computing & Statistics. 2012. p. 26.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Default prediction of SMEs by a generalized extreme value additive model

AU - Calabrese, Raffaella

AU - Osmetti, Silvia Angela

PY - 2012

Y1 - 2012

N2 - A new model is proposed for default prediction of Small and Medium Enterprises (SMEs). The main weakness of the scoring models proposed in the literature is not to consider the default as a rare event. To take into account this characteristic, Calabrese and Osmetti (2011) suggested the quantile function of the Generalized Extreme Value (GEV) distribution as a link function in a Generalized Linear Model (GLMs). In the GLMs, the relationship between the independent variable and the predictor is constrained to be linear. Since this assumption is not usually satisfied by scoring models, a Generalized Additive Model (GAM) is suggested with the quantile function of the GEV distribution as link function. Hence, the Generalized Extreme Value Additive (GEVA) model is proposed. Finally, our proposal is applied to empirical data on Italian SMEs. It is obtained that the GEVA model shows a high accuracy for predicting defaults.

AB - A new model is proposed for default prediction of Small and Medium Enterprises (SMEs). The main weakness of the scoring models proposed in the literature is not to consider the default as a rare event. To take into account this characteristic, Calabrese and Osmetti (2011) suggested the quantile function of the Generalized Extreme Value (GEV) distribution as a link function in a Generalized Linear Model (GLMs). In the GLMs, the relationship between the independent variable and the predictor is constrained to be linear. Since this assumption is not usually satisfied by scoring models, a Generalized Additive Model (GAM) is suggested with the quantile function of the GEV distribution as link function. Hence, the Generalized Extreme Value Additive (GEVA) model is proposed. Finally, our proposal is applied to empirical data on Italian SMEs. It is obtained that the GEVA model shows a high accuracy for predicting defaults.

KW - generalized additive model

KW - generalized extreme value distribution,

KW - generalized additive model

KW - generalized extreme value distribution,

UR - http://hdl.handle.net/10807/42780

M3 - Conference contribution

SN - 978-84-937822-2-1

SP - 26

BT - Book of Abstracts, CFE 2012 6th CSDA International and Finantial Econometrics, ERCIM 2012, 5th International Conference of the ERCIM Workin Group on Computing & Statistics

T2 - CFE-ERCIM 2012

Y2 - 1 December 2012 through 3 December 2012

ER -

Default prediction of SMEs by a generalized extreme value additive model

Abstract

Conference

UN SDGs

Keywords

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Cite this