| Course name |
Finance and quantitative methods for economics |
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| Study-unit Code |
20A00026 |
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| Location |
PERUGIA |
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| Curriculum |
Finanza ed assicurazione |
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| Lecturer |
Elena Stanghellini |
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| Lecturers |
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| Hours |
- 42 ore - Elena Stanghellini
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| CFU |
6 |
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| Course Regulation |
Coorte 2018 |
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| Supplied |
2019/20 |
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| Learning activities |
Caratterizzante |
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| Area |
Matematico, statistico, informatico |
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| Sector |
SECS-S/01 |
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| Type of study-unit |
Opzionale (Optional) |
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| Type of learning activities |
Attività formativa monodisciplinare |
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| Language of instruction |
English |
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| Contents |
Theoretical and practical notions of Credit Scoring. Definition and phases; probability and independence; logistic models as classifiers; ROC and CAP curves and other validation methods. Some notions of discriminant analysis will also be addressed
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| Reference texts |
English translation of Stanghellini (2009) Introduzione ai metodi statistici per il Credit Scoring -- Springer Italia, Capp: 1-4. (in Italian) Thomas L.C., Edelman D.B., Crook J.N. (2002) Credit Scoring and Its Applications -- SIAM
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| Educational objectives |
Students will acquire knowledge of the major statistic techniques to measure the probability of default of a credit position. The analysis of real data and of case studies through the software R will give the students confidence on how to perform a data analysis in this context and learn how to buld a statistical model to actually measure the risk of default.
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| Prerequisites |
In order to successfully complete the module, students should have completed the module Statistical Methods for Finance (or any other advanced statistics course with analogous content). To be more specific: students should have successfully completed a module with Multiple Linear Regression covering: a) assumptions and unknown parameters; b) inferential procedures to estimate the parameters: Ordinary Least Squares, Maximum Likelihood; c) Sampling distribution of the estimators. Large sample distributions of the estimators; d) Confidence intervals. Hypothesis testing: on the parameter, on the model. F-test for the model; e) Heteroskedasticity: problems and inference in heteroskedastic models.
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| Teaching methods |
There will be four hours of lectures and two hours of practical exercises in the computer lab (weekly). Students are strongly advised to attend the lectures and the excercises. Furthermore, every two/three weeks, students are proposed an homework. The homework may be completed in groups of 3 or 4 students. The partecipation of the homweork scheme exempt the students from providing the document 3 days prior the exams session (see Modalità di verifica dell'apprendimento below). Students are strongly advised to join the scheme.
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| Other information |
Incoming students in Erasmus and other Exchange programs are most welcome.
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| Learning verification modality |
Oral examination on both the theoretical aspects covered during the lectures and their application to real data analysis. Students are requested to complete a written report of the analysis on some given datasets, following the instructions on the file uploaded on the web page of the course in Unistudium. This document should be sent to the instructor via email three days before the exam date. Students that attend the lectures may subscribe to the programme of regular homeworks to be completed on an forthnight base. Students may do these exercises in groups. The exercises will be provided by the instructor during the lecturing time and involve solving real problem on real data. This will substitute the above requested written document.
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| Extended program |
The course addresses the major statistical methods to quantify the credit risk of a position or to classify a potential customer. The most important Credit Scoring (or Credit Rating) techniques are addressed, namely logistic regression and discriminant analysis, which are the most used methods in the area. Tools for assessing the efficacy of the classifier and the accuracy of the predictors are presented, such as the ROC and CAP curves, the confusion matrix, the Hosmer-Lemeshow test. Implementation of the techniques through the software R for statistical computing will also be part of the course.
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