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.

      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.

      URL Aula web
      MODERN PORTFOLIO THEORY
      https://2018.aulaweb.unige.it/course/view.php?id=1349
      URL Orario lezioni
      MODERN PORTFOLIO THEORY
      http://diec.unige.it/orario-lezioni
  • Chi
    • Docenti
    • Marina Resta
      tel. (+39) 010 2095469,interno 55469
      resta@economia.unige.it
    • Commissione d’esame
      41605 - MODERN PORTFOLIO THEORY
      Luca Persico
      Marina Resta (Presidente)
  • Come
    • 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 

       

      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.

  • Dove e quando
    • URL Aula web
      MODERN PORTFOLIO THEORY
      https://2018.aulaweb.unige.it/course/view.php?id=1349
      URL Orario lezioni
      MODERN PORTFOLIO THEORY
      http://diec.unige.it/orario-lezioni
      INIZIO LEZIONI

      Sem: II

       

      RICEVIMENTO STUDENTI
      Marina Resta

      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 (e fino al 31/5/2019) il ricevimento si terrà il Mercoledì dalle 10.30 alle 11.30.

      Appelli
      Data Ora Tipo Luogo Note
      21 dicembre 2018 9:30 Scritto Genova
      17 gennaio 2019 9:30 Scritto Genova
      7 febbraio 2019 9:30 Scritto Genova
      7 giugno 2019 9:30 Scritto Genova
      26 giugno 2019 9:30 Scritto Genova
      8 luglio 2019 9:30 Scritto Genova
      10 settembre 2019 9:30 Scritto Genova
  • 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.

       

       

  • Contatti