This thesis investigates whether estimating the inputs of the Markowitz (1952) Mean- Variance framework using various econometric techniques leads to improved optimal portfolio allocations at the country, sector and stock levels over a number of time periods. We build upon previous work by using various combinations of conventional and Bayesian expected returns and covariance matrix estimators in a Mean-Variance framework that incorporates a benchmark reference, an allowable deviation range from the benchmark weights and short-selling constraints so as to achieve meaningful and realistic outcomes. We found that models based on the classical maximum likelihood method performed just as well as the more sophisticated Bayesian return estimators in the study. We also found that the covariance matrix estimators analysed created covariance matrices that were similar to one another and, as a result, did not seem to have a large effect on the overall portfolio allocation. A sensitivity analysis on the level of risk aversion confirmed that the simulation results were robust for the different levels of risk aversion.
| Identifer | oai:union.ndltd.org:ADTP/234309 |
| Date | January 2006 |
| Creators | Kouch, Richard, Banking & Finance, Australian School of Business, UNSW |
| Publisher | Awarded by:University of New South Wales. School of Banking and Finance |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Richard Kouch, http://unsworks.unsw.edu.au/copyright |
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