• Obiettivi e contenuti
    • OBIETTIVI FORMATIVI
      The course provides a survey of the theory and application of time series models in financial econometrics. Students are introduced to time series analysis of linear univariate and multivariate covariance stationary models with short and long memory parameterization. The course then employs linear time series knowledge to introduce studen ts to time series financial econometrics models, particularly discrete-time parametric ARCH models. The main objective of this course is to develop the skills needed for modelling and forecasting assets volatilities and their co-movements in financial markets. The course aims to provide students with a strong theoretical understanding of volatility models and techniques for estimations, assessment and forecasting in financial markets under a variety of degree of shock persistence. Theoretical lectures are complemented by computer classes whose aim is to enable the students to develop computational skills in MATLAB for empirical research
      OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

      Il corso si pone l'obbiettivo di introdurre i principali strumenti usati nell'analisi delle serie storiche e finanziarie, oltre che fornire la comprensione del'origine e caratteristiche dei dati finanziari, oltre che le applicazioni possibili. Durante il corso viene inoltre sottolineato ed enfatizzato il legame tra teoria ed analisi empirica. Viene affrontata l'analisi dei modelli per le serie stazionarie univariate e multivariate parametrizzati a lunga e breve memoria, La conoscenza delle serie storiche è utilizzata per introdurre i modelli econometrici per l'analisi delle serie finanziarie, con aprticolare attenzione ai modelli discreti ARCH. L'obbiettivo del corso è fornire una solida base teorica per l'analisi della volatilità e sviluppare le compentenze necessarie a modellizzare e fare previsioni nnell'ambito del mercato finanziario,

      PROGRAMMA/CONTENUTO

      TOPIC I: LINEAR TIME SERIES ANALYSIS .

      • Stochastic processes, covariance stationarity, strict stationarity, unit root processes, fractionally integrated processes, Wold decomposition theorem.
      • AR, MA, ARMA,ARIMA,ARFIMA univariate models: estimation and principles of forecasting.
      • Unit root tests,long memory tests, cointegration,model diagnostic.

      TOPIC II: UNIVARIATE GARCH MODELS.

      • Introduction of asset returns
      • ARCH model: identification and covariance stationarity conditions ,order identification, estimation, evaluation
      • GARCH model: identification and covariance stationarity conditions ,order identification, estimation, evaluation and forecasting.
      • Asymmetric GARCH models and leverage effects:EGARCH,QGARCH,GJGARCH,TGARCH: identification and covariance stationarity conditions ,order identification, estimation, evaluation and forecasting.
      • Long memory in univariate GARCH models: testing for long memory in the time series domain, forecasting in presence of long memory.

      TOPIC  III: VAR MODELS.

      • Introduction to VAR models: properties and characteristics
      • Econometric approach to VAR and estimation

      TOPIC  IV: MULTIVARIATE GARCH MODELS.

      • Introduction to Multivariate GARCH MODELS
      • Co-movements of financial returns: empirical and theoretical examples. Introduction to MGARCH models and specific issues.
      • FACTOR MODELS
      • NON PARAMETRIC models
      • Testing in MGARCH models
      TESTI/BIBLIOGRAFIA

      Hamilton  J. (1994) "Time Series Analysis", Princeton University Press

      Franq Zaquoian "Garch models"

      ulteriori testi o articoli verranno indicati durante il corso

      URL Aula web
      FINANCIAL ECONOMETRICS
      https://2018.aulaweb.unige.it/course/view.php?id=1354
      URL Orario lezioni
      FINANCIAL ECONOMETRICS
      http://diec.unige.it/orario-lezioni
  • Come
  • Dove e quando