**Course Outline**

The main purpose of this course is to provide master’s students in financial economics with knowledge they will need to conduct cutting edge research in the broad areas of financial economics namely financial econometrics, risk management, asset allocations and portfolio optimization. It is assumed that students taking this course have a good background in mathematical statistics, financial econometrics and probability theory. The course covers the following topics:

- Non-parametric econometrics: univariate and multivariate kernel regression;
- Risk management: value at risk estimation, extreme value distributions, copulas, market, operational and credit risk modeling;
- Portfolio selection: modern portfolio theory i.e. the mean-variance portfolio model and its alternatives;
- Bayesian methods in Finance: Bayesian CAPM, Bayesian GARCH models, Bayesian portfolio selection, The Black Litterman model, and the Bayesian risk management.
- Advances in behavioural finance: probability weighting, loss aversion, behavioural asset pricing and behavioural portfolio theory.

The following software will be used:

- Matlab software with statistics and optimization toolboxes fully installed,
- R software with all default packages duly installed
- WinBUGS

Although I will help you with some Matlab and R programming, I expect you to write your own Matlab/R programs in order to develop your own models. At the end of the course you are expected to submit a well written long assignment on one of the abovementioned topics that will count for a certain weight toward your final marks.

**Informative Lessons**

You are also adviced to watch these financial videos in order to familiarise yourself with the topics that will be discussed in this course.

**Marked scripts: long assignment** 2011 **MARKS FOR APPLICATIONS IN FINANCIAL ECONOMICS 2011**** **

__Links to Some Useful eBooks in html Version__: **Pdf eBooks**

Applied Quantitative Finance

Statistical Tools for Finance and Insurance

Statistics of Financial Markets

Applied Multivariate Statistical Analysis

Nonparametric and Semiparametric Models

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