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Precision on targeted returns

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Undergraduate Courses
BCom Honours
MCom Financial Economics

The M.Com Financial Economics Bridging Courses can be found here


Course Outline:


The main purpose of this page 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 studying toward a master's degree in financial economics have a good background in mathematical statistics, financial econometrics and probability theory. The modules I teach cover the following topics:

  1. Non-parametric econometrics: univariate and multivariate kernel regression;
  2. Risk management: value at risk estimation, extreme value distributions, copulas, market, operational and credit risk modeling;
  3. Portfolio selection: modern portfolio theory i.e. the mean-variance portfolio model and its alternatives;
  4. Bayesian methods in Finance: Bayesian CAPM, Bayesian GARCH models, Bayesian portfolio selection, The Black Litterman model, and the Bayesian risk management.
  5. 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. 


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.



Links to Some Useful eBooks in html Version:                                           


Applied Quantitative Finance

Statistical Tools for Finance and Insurance


Statistics of Financial Markets


Applied Multivariate Statistical Analysis

Nonparametric and Semiparametric Models 




Financial Econometrics 2014


Session 1: AR models: ,

               R codes AR models


Session 2:  GARCH models,   


Session 3: Alternative GARCH models,

                R codes GARCH models


Session 4:  PCA, and Factor models,


Session 5: TAR, STAR, Markov Switching, Kernel regression, Bayesian Models

               R codes for Markov Switching model,

               R codes for TAR model,

               R codes Bayesian regression,

               R codes for Neural Network single layer,

               R codes for Non-parametric regression,


Session 6: SVAR and VAR models, 


               R codes for VAR models,


Applications in Financial Economics 2014


Session 1: Market risk 


Session 2: Credit risk


Session 3: Operational risk,

               R codes for Op.Risk,

Session 4: Portfolio Theory,

                R codes for portfolio selection,

Session 5: R codes for VaR and Portfolio optimization using DCC, GO-GARCH, PCA and Copulas


Session 6: Black Litterman model,

                R codes for Black Litterman,


Session 7: Performance analysis


Session 8: Advanced behavioural portfolio optimization