Παρουσίαση/Προβολή

(STAT179) -  Vrontos Ioannis

Περιγραφή Μαθήματος

Aim of the course

This module provides a broad introduction to the theory and empirical analysis of econometric models to financial applications. Statistics/Econometrics is concerned with the systematic study of empirical financial problems using observed data. The aim of the course is to develop the relevant econometric tools for analyzing empirical problems in finance such as optimal portfolio construction, risk management, etc. The student will acquire the necessary statistical and econometric tools to estimate the parameters of various models for analyzing different problems in finance.

 

Key Outcomes

The aim of this module is to provide students with advanced statistical and econometric skills required to analyze empirical problems in finance. On completion of this module, students will be able to:

• interpret the concepts of return and risk in financial markets
• model the expected returns of financial assets
• model the variances and covariances/correlations of financial returns
• use advanced econometric tools to analyze models used in financial applications
• forecast financial returns
• assess the performance of portfolio managers
• understand modern portfolio theory
• solve mean-variance optimization problems
• estimate the risk of financial assets

 

Books

Recommended textbooks:

• Elton, E.J., Gruber, M.J., Brown, S.J., and Goetzmann W.N. (2014). Modern Portfolio Theory and Investment Analysis, 9th edition, Wiley.
• Sharpe, W.F., Alexander, G.J, and Bailey, J.V. (1999). Investments, 6th edition, Prentice-Hall.
• Tsay, Ruey S. (2010). Analysis of Financial Time Series, New York: Wiley.
• Vrontos, I.D. (2016) Financial Econometrics, Lecture Notes (In Greek).

 

Software/Computing requirements

The computational aspects of this course will be implemented in Matlab. 

 

Grading

There will be two projects during the course. The final grade will be a combination of the projects and the final exams (80% exams + 20% project)

 

Course Syllabus

The course comprises of six units of three hours each.

 

Unit 1: Introduction - Portfolio Theory

Introduction to Course. Mean-Variance Portfolio Theory. Return and risk. Portfolio diversification. Construction of optimal portfolios. Basic empirical application.

 

Unit 2: Performance Evaluation

Performance Evaluation of Financial Assets. Capital asset pricing model. Treynor measure. Sharpe measure. Jensen’s alpha. Multifactor models. Alternative measures. Empirical application.

 

Unit 3: Time Series Models of Heteroscedasticity

Characteristics of Financial Data. Fat tails. Volatility clustering phenomenon. Leverage effect.

Heteroskedasticity Models. ARCH, GARCH and EGARCH models. Properties of time-varying models. Estimation of heteroskedastic models. Illustration of estimating GARCH-type models to financial time series using Matlab. Applications to real financial series: modeling and forecasting financial return series.

 

Unit 4: Multivariate multifactor models – Multivariate Heteroscedasticity models

Multivariate Factor models. Single index models. General multivariate multifactor model. Multivariate Heteroskedasticity Models. Multivariate ARCH/GARCH models. Constant conditional correlation model. Empirical application.

 

Unit 5: Risk Measures

Risk Measures. Value at Risk. Expected Shortfall. Empirical application.

 

Unit 6: Panel Data – Panel Models

Introduction to panel data/models. Fixed effects model. Random effects model. Empirical application.

Ημερομηνία δημιουργίας

Παρασκευή, 6 Μαρτίου 2015