MODERN PORTFOLIO THEORY

MODERN PORTFOLIO THEORY

_
it ｜ en
Codice
41605
ANNO ACCADEMICO
2019/2020
CFU
6 cfu al 1° anno di 8700 ECONOMIA E ISTITUZIONI FINANZIARIE (LM-56) GENOVA
SETTORE SCIENTIFICO DISCIPLINARE
SECS-S/06
LINGUA
Inglese
SEDE
GENOVA (ECONOMIA E ISTITUZIONI FINANZIARIE )
periodo
2° Semestre
materiale didattico
AULAWEB

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

Modalità didattiche

Lessons held by the instructor as well as cases study. The course will utilize R data analysis and statistical modeling.

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

Pierpaolo Uberti

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

Modalità didattiche

Lessons held by the instructor as well as cases study. The course will utilize R data analysis and statistical modeling.

Presente su Aulaweb

Yes   X 

 

INIZIO LEZIONI

Sem: II

 

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

Vedi anche:

MODERN PORTFOLIO THEORY

ESAMI

Modalità d'esame

Written examination.

Modalità di accertamento

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.

Ripetizione dell’esame

Three times in the first session. It is mandatory to sign for the examination through the web portal.

ALTRE INFORMAZIONI

 
 
 
  • Knowledge and understanding.  Students must acquire adequate knowledge and understanding of effective asset allocation tools.
  • Applying knowledge and understanding. Students should be able to apply their knowledge to solv problems of optimal allocation in the presence of risk.
  • Independent judgment capabilities. The students should know how to use the learned skills both at the conceptual and at the operational level in different application contexts.
  • Communication skills. Students should acquire the technical language of the discipline to keep in touch, both clearly and unambiguously, with specialists.
  • Learning skills. Students must develop proper learning skills to to independently investigate major issues of the field, withinh their operative working framework.

 

 

 The course will use R data analysis and statistical modeling.

 

  

Testi di studio

The classes material will be set in the classroom at the beginning of the lessons, as well as published on Aulaweb.

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.

Ripetizione dell’esame

Three times in the first session. It is mandatory to sign for the examination through the web portal.