The first part of the lecture recaps important concepts of ARIMA models.

The second part deals with volatility modelling, in particular GARCH and stochatsic volatility model. Such conditional heteroscedasticity models are able to capture the stylized facts of financial return data, that periods of high volatility are followed by periods of low volatility.

The third part deals with modelling multivariate time series. First, vector autoregressive models are introduced and the presentation develops as follows: posing the problem, model specification, listing the assumptions, interpretation, estimation, inference, model selection, model sensitivity, and empirical applications. Second, VEC or cointegrated VARare discussed that allow for cointegrated series leading to non standard estimation procedures and inference.

The fourth part introduces the class of state space models. This model class comprises the ARIMA class,  stochastic volatility modelling and time-varying parameter model. The flexibility of the class will be demonstrated and the estimation via Kalman filtering is explained. Empirical examples are used for illustration.

The final part discusses selected topics from machine learning techniques in financial econometrics.

After completion of the course the student will be able

• to understand and apply more specific methods for modeling data of financial markets.
• to apply a selection of frequently used procedures for financial data, covering ARIMA models, volatility models, VAR (vector autoregressive models), VEC (vector error correction models), Kalman filter and state space models.
• to interpret the output of empirical estimates.

Participants will be trained in

• manipulating formulas,
• reading and executing R scripts
• interpreting the results of small empirical projects

when doing the assignments and presenting them in class.

The fnal exam cover theoretical aspects, deeper understanding as well as empirical applications.

Attendance is mandatory for at least 80% of the lectures.

The course is organized as follows: Lecture with slides. The methods are illustrated using real data sets. There is a discussion of the assignments in class.

Grading will be based on homework assignments, presentations of the assignments in class, and on the final exam. The assignments have to be solved individually. The student indicate before the beginning of the class which assigments they are willing to discuss in class.

The contributions to the grade are

• 12% for each homework assignments and classroom presentation; in total 5 assignments, i.e. max 60% of the grade
•   5% active participation in discussions during the course
• 35% final exam; the final exam is voluntary  and no minimum requirement of credit points is therefore needed to pass the class

Grading is as follows:

1 (at least 90% of total credit points),  2 (at least 80%),  3 (at least  70%),  4 (at least 60%)

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sylvia.fruehwirth-schnatter@wu.ac.at