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STATISTICAL MODELS FOR ECONOMICS AND FINANCE Single discipline educational activity
Course Sheet Academic Year of enrolment:
Professor and Collaborators:
Hours of classroom activity:
Prerequisites:
A sound familiarity with undergraduate statistics.
Objectives
Contents First module
1. Exploratory data analysis of Prices and Returns
2. Value at Risk and Expected Shortfall
3. Mean-variance analysis and modern portfolio theory
4. Capital Asset Pricing Model
Second module
5. Time-Serie Analysis, ARMA models: specification, inference and forecasting
6. Models for volatility analysis and prediction: ARCH and GARCH models
7. Models for macro—finance analysis: Vector Autoregressive models.
Extended Syllabus First module
1. Exploratory data analysis of Prices and Returns
2. Value at Risk and Expected Shortfall
3. Mean-variance analysis and modern portfolio theory
4. Capital Asset Pricing Model
Second module
5. Time-Serie Analysis, ARMA models: specification, inference and forecasting
6. Models for volatility analysis and prediction: ARCH and GARCH models
7. Models for macro—finance analysis: Vector Autoregressive models.
Recommended Bibliography Slides and course materials.
Suggested reading: Gallo, G. M., Pacini, B., Metodi quantitativi per i mercati finanziari, Carocci, Roma, 2013 (VII Ristampa).
Di Fonzo, T., Lisi F., Serie storiche economiche, Carocci, 2012.
Methods of Provision
Teaching Methods Classes, recitations, R tutorials.
Evaluation methods Verification of learning:
Written (70%) and oral exam (30%).
Contacts/More Information Additional material for exam preparation (Slides, recitations, R routines, datasets) is available on the TEAM course channel.
If Health Laws and University regulations allow, teaching activities, teachers office hours’, and exams may take place online (in whole or in part).
For any further information and updates, please refer to the University website.