TAs: Gabriel BOBEICA

This course gives an overview of advanced econometric models, methods, and techniques applicable to financial economic modeling, bringing the graduate students to the research frontier in empirical finance. The main purpose of this course is to further develop the students’ skills to analyzing financial data by teaching them to implement models in an econometric software programming language.

Eviews and R (with the package Rmetrics) are used intensively in the course. Students can employ other econometric software (i.e. Gauss, Matlab, Octave) if preferred.

Students should have prior knowledge of probability theory, statistical inference theory, basic econometric techniques, basic time series analysis as well as advanced knowledge of financial theory (portfolio theory, CAPM, derivatives valuation, term structure models).

25% project 1 + 25% project 2 + 50% final exam (open book)

There is no required textbook for the course. However, there are some reference books that are recommended:

- Alexander, C., (2001), Market Models - A Guide to Financial Data Analysis, John Wiley & Sons
- Campbell, J.Y., A.W. Lo and A.C. MacKinlay, (1997), The Econometrics of Financial Markets, Princeton University Press
- Gourieroux, C. and J. Jasiak, (2001), Financial Econometrics: Problems, Models, and Methods, Princeton University Press
- Hamilton, J., (1994), Time Series Analysis, Princeton University Press
- Tsay, R.S., (2005), Analysis of Financial Time Series, 2th Edition, John Wiley & Sons

1. Introduction to R and Rmetrics

- basic file operations
- basic statistical indicators
- basic statistical tests
- histograms and kernel estimation

2. Properties of Asset Returns

- distributional properties: heavy tails, tests for normality
- volatility clustering: tests for
heteroskedasticity
- long memory: unit root tests and tests for fractional integration
- multifractal: estimating the scaling law

3. Modeling Asset Returns Distribution

- Gaussian distributions: advantages and pitfalls
- t-Student distribution, GED
- alpha - stable distributions
- Generalized Hyperbolic Distributions

4. Volatility Modeling

- GARCH models: properties and classification
- GARCH models estimation
- Gaussian GARCH models
- Heavy Tailed GARCH models
- Volatility forecasting using GARCH models

5. Monte Carlo simulation

- the basics of MC simulation
- variance reduction techniques
- valuation of plain vanilla derivatives using MC simulation
- valuation of path dependent derivatives using MC simulation
- computing VaR using MC simulation

6. Modeling the Dependency Structure between Asset Returns

- the linear correlation coefficient: advantages and pitfalls
- other correlations coefficients: Kendall, Spearman
- copula functions
- estimating the multi-dimensional distribution between asset returns using copula functions
- Copula-GARCH models

7. Term Structure of Interest Rates Modeling

- interpolating the term structure using splines
- calibration of Ho-Lee model to the empirical term structure

8. Markov Switching Models

9. State Space Models and the Kalman Filter

Course Materials