MODERN PORTFOLIO THEORY
PRESENTAZIONE
An introduction to mathematical methods focusing on portfolio optimization.
OBIETTIVI E CONTENUTI
OBIETTIVI FORMATIVI
The course aims at providing models and methods to theoretical and practical analysis of asset allocation problems. Special attention will be devoted to classical portfolio theory and empirical studies.
OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO
An introduction to mathematical methods focusing on portfolio optimization. Starting from the model of asset allocation of Markowitz, the student will be introduced to classical portfolio theory, to move to allocation methods based on Value at Risk, Expected Shortfall, as well as to techniques relying on bootstrap.
Modalità didattiche
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Modalità didattiche |
Lessons held by the instructor as well as cases study. The course will utilize R data analysis and statistical modeling. |
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Presente su Aulaweb |
Yes X |
PROGRAMMA/CONTENUTO
Part I. Basic notations and conventions
Returns calculation. Stylized facts: lack of correlation; Quadratic Positive Correlation; Absence of Normality. Introduction to Technical Analysis.
Part II: Portfolio selection à la Markowitz
Mean-Variance Model: the case of two assets and the general case. Graphical analysis,. Implications. The separation theorem and its financial interpretation. Efficient portfolios by way of matrix algebra. The efficient frontier. The model with a risk-free asset. An outline on CAPM and market line.
Part III: Risk Measures.
A quantile-based approach. Coherent risk measures. Value-at-Risk: definition and statistical implications. Expected Shortfall: definition and statistical implications. Some tests on VaR.
Part IV: Advanced Asset Allocation.
Outline of bootstrap techniques. The resampling approach by Michaud. The Black-Litterman model. Mean-variance-skewness models of asset allocation. Portfolio optimization based on risk measures.
TESTI/BIBLIOGRAFIA
The classes material will be set in the classroom at the beginning of the lessons, as well as published on Aulaweb.
DOCENTI E COMMISSIONI
Ricevimento: Primo semestre: Giovedì dalle 10:30 alle 11:30. Secondo semestre: da concordare via e-mail con il docente.
Commissione d'esame
PIERPAOLO UBERTI (Presidente)
CRISTINA LUIGIA GOSIO
MARINA RESTA (Presidente)
LUCA PERSICO
LEZIONI
Modalità didattiche
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Modalità didattiche |
Lessons held by the instructor as well as cases study. The course will utilize R data analysis and statistical modeling. |
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Presente su Aulaweb |
Yes X |
INIZIO LEZIONI
Sem: II
ESAMI
Modalità d'esame
Written examination.
Modalità di accertamento
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Modalità di accertamento |
Written examination. The students can alternatively present and discuss a report according to the indications provided by the teacher during the lessons. |
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Ripetizione dell’esame |
Three times in the first session. It is mandatory to sign for the examination through the web portal. |
Calendario appelli
| Data | Ora | Luogo | Tipologia | Note |
|---|---|---|---|---|
| 10/07/2020 | 09:00 | GENOVA | Scritto | |
| 10/09/2020 | 09:00 | GENOVA | Scritto |
ALTRE INFORMAZIONI
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The course will use R data analysis and statistical modeling. |
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Testi di studio |
The classes material will be set in the classroom at the beginning of the lessons, as well as published on Aulaweb. |
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Modalità di accertamento |
Esame X scritto ☐ orale ☐ altro: The students will present and discuss a report according to the indications provided by the teacher during the lessons. |
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Ripetizione dell’esame |
Three times in the first session. It is mandatory to sign for the examination through the web portal. |