Analysis of risk measures in portfolio optimization for the Uganda Securities Exchange

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dc.contributor.authorBirungi, Criscent
dc.date.accessioned2022-06-13T05:56:32Z
dc.date.available2022-06-13T05:56:32Z
dc.date.issued2021
dc.descriptionResearch thesis submitted to Strathmore University in fulfillment of the requirements for the Master of Science in Mathematical Financeen_US
dc.description.abstractFor the most recent years, risk has become one of the essential parameters in portfolio optimization problems. Today most practitioners and researchers in portfolio optimization have used variance as a standard risk measure. This approach has been found subjective. The Markowitz (1952) mean-variance model considered variance as an adequate portfolio risk measure, and asset returns are multivariate normally distributed and that investors have a quadratic utility function which is subjective too. Other risk measures have been suggested to overcome the limitations of the mean-variance model. This paper analyzes which portfolio optimization models can better explain the optimal portfolio performance (high return, low risk) for the Uganda Security Exchange(USE). We compare Mean-Variance (MV), Mean Absolute Deviation (MAD), Robust Portfolios and Covariance Estimation Models( The Shrinked Mean-Variance (SMV) Models & Alternative Covariance Estimator (ACE) Models ) and Mean-Conditional Value-at-Risk (Mean-CVaR) models in terms of the risk and performance. Portfolios were developed by employing the MV, MAD, SMV, ACE and Mean-CVaR models. For the computed monthly returns and price data (February 2010 to January 2021) for USE selected stocks, we considered the results show that Mean-CVaR and ACE portfolios have the highest performance ratio compared to other models. We find that VaR is the best risk measure for portfolio optimization for the USE since it has lower values across all models than other risk measures. It is vital to consider all the available risk measures for a regulator or practitioner to make a good decision since using one can be subjective; as seen in our results, different risk measures yield different results.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12812
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectPortfolio optimizationen_US
dc.subjectRisk measuresen_US
dc.subjectConditional Value-at- Risken_US
dc.subjectVarianceen_US
dc.subjectUganda Security Exchangeen_US
dc.titleAnalysis of risk measures in portfolio optimization for the Uganda Securities Exchangeen_US
dc.typeThesisen_US
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MSc.MF Theses and Dissertations (2021)