CODICE 41605 ANNO ACCADEMICO 2018/2019 CFU 6 cfu anno 1 ECONOMIA E ISTITUZIONI FINANZIARIE 8700 (LM-56) - GENOVA SETTORE SCIENTIFICO DISCIPLINARE SECS-S/06 LINGUA Inglese SEDE GENOVA PERIODO 2° Semestre MATERIALE DIDATTICO AULAWEB PRESENTAZIONE An introduction to mathematical methods focusing on portfolio optimization. OBIETTIVI E CONTENUTI OBIETTIVI FORMATIVI Starting from the model of asset allocation of Markowitz, the student will be introduced to classical portfolio theory, including the CAPM, to move then to allocation methods based on Value at Risk, Expected Shortfall, as well as to techniques relying on bootstrap. 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. MODALITA' 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 MARINA RESTA Ricevimento: Durante il primo semestre (e fino al 22/12/2018) il ricevimento si terrà il Martedì dalle 10.40 alle 12.00. Durante il secondo semestre (18/02/2019 fino al 31/5/2019) il ricevimento si terrà il Mercoledì dalle 10.30 alle 11.30. Negli altri periodi si prega di contattare preventivamente la docente via mail all'indirizzo: marina PUNTO resta AT economia PUNTO unige PUNTO it Commissione d'esame MARINA RESTA (Presidente) LUCA PERSICO LEZIONI INIZIO LEZIONI Sem: II Orari delle lezioni MODERN PORTFOLIO THEORY ESAMI MODALITA' D'ESAME Written examination. MODALITA' 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. Calendario appelli Data appello Orario Luogo Tipologia Note 17/01/2019 09:30 GENOVA Scritto 07/02/2019 09:30 GENOVA Scritto 07/06/2019 09:30 GENOVA Scritto 26/06/2019 09:30 GENOVA Scritto 08/07/2019 09:30 GENOVA Scritto 10/09/2019 09:30 GENOVA Scritto 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.