Risk-based asset allocation : a forward looking approach
- Mantshimuli, Lamukanyani Alson
- Authors: Mantshimuli, Lamukanyani Alson
- Date: 2016
- Subjects: Asset allocation , Financial crises , Portfolio management , Financial risk
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/237248 , uj:24306
- Description: M.Com. (Financial Economics) , Abstract: The portfolio allocation problem is characterised by two factors; risk and expected return. This is mainly explained by the Markowitz (1952) mean-variance framework. The frequency and severity of recent financial crises has led to an increase in calls for improved asset allocation methods in the asset management industry. Asset allocation strategies should protect investor capital and result in higher relative returns in turbulent times. Modern portfolio theory has been heavily criticised (Lee, 2011; Roncalli, 2013) for failure to provide adequate diversification to protect fund managers during crises, hence the emergence of risk-based asset allocation methods that focus on portfolio construction based on risk and diversification. The crises led to poor performance of different portfolios and funds, especially those with high exposure to equities. Risk-based allocation methods try to achieve investors’ goals of safety and higher returns, irrespective of future market behaviour. Six risk-based asset allocation strategies were explored and contrasted; Equally weighted, Risk parity, Most Diversified, Minimum Correlation, Minimum variance and the Minimum CVaR portfolio. This was done in an effort to find the method which performs better when investors have different investment goals. Predicted risk measures were applied as inputs in these risk-based asset allocation methods (i.e. a forward looking approach was taken). The study focused on comparisons of the risk-based asset allocation methods using forwardlooking risk measures in the South African market. The main results of the study include the finding that risk-based asset allocation methods are effective in protecting investors’ capital and achieve higher returns than the market portfolio during crisis periods compared to other periods as expected. It was also found that the Minimum Correlation Portfolio performed better than all other risk-based asset allocation during the crisis period, which means it is the best risk-based asset allocation method to use during crisis periods in the South African market . There has not been a lot of studies on the perfomance of the Minimum Correlation Portfolio, and this result shows the need for a comprehensive study on all risk-based asset allocation methods in different countries/regions to determine which risk-based asset allocation technique is best for different regions.
- Full Text:
- Authors: Mantshimuli, Lamukanyani Alson
- Date: 2016
- Subjects: Asset allocation , Financial crises , Portfolio management , Financial risk
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/237248 , uj:24306
- Description: M.Com. (Financial Economics) , Abstract: The portfolio allocation problem is characterised by two factors; risk and expected return. This is mainly explained by the Markowitz (1952) mean-variance framework. The frequency and severity of recent financial crises has led to an increase in calls for improved asset allocation methods in the asset management industry. Asset allocation strategies should protect investor capital and result in higher relative returns in turbulent times. Modern portfolio theory has been heavily criticised (Lee, 2011; Roncalli, 2013) for failure to provide adequate diversification to protect fund managers during crises, hence the emergence of risk-based asset allocation methods that focus on portfolio construction based on risk and diversification. The crises led to poor performance of different portfolios and funds, especially those with high exposure to equities. Risk-based allocation methods try to achieve investors’ goals of safety and higher returns, irrespective of future market behaviour. Six risk-based asset allocation strategies were explored and contrasted; Equally weighted, Risk parity, Most Diversified, Minimum Correlation, Minimum variance and the Minimum CVaR portfolio. This was done in an effort to find the method which performs better when investors have different investment goals. Predicted risk measures were applied as inputs in these risk-based asset allocation methods (i.e. a forward looking approach was taken). The study focused on comparisons of the risk-based asset allocation methods using forwardlooking risk measures in the South African market. The main results of the study include the finding that risk-based asset allocation methods are effective in protecting investors’ capital and achieve higher returns than the market portfolio during crisis periods compared to other periods as expected. It was also found that the Minimum Correlation Portfolio performed better than all other risk-based asset allocation during the crisis period, which means it is the best risk-based asset allocation method to use during crisis periods in the South African market . There has not been a lot of studies on the perfomance of the Minimum Correlation Portfolio, and this result shows the need for a comprehensive study on all risk-based asset allocation methods in different countries/regions to determine which risk-based asset allocation technique is best for different regions.
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The ability of GARCH models in forecasting stock volatility on the JSE Limited
- Authors: Mokoena, Tholoana
- Date: 2016
- Subjects: GARCH model , Stock exchanges , Forecasting , Johannesburg Stock Exchange
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/124226 , uj:20891
- Description: Abstract: This study compares the fit and forecast performance of a selected group of parametric Generalised Autoregressive Conditional Heteroskedasticity GARCH (1, 1) models using various underlying distributions. The GARCH (1, 1) type models are empirically tested on the returns of the All Share Index (ALSI), a diversified portfolio of all the shares on the South African Johannesburg Stock Exchange (JSE). Estimates and forecasts generated by each model are compared and analysed to establish the validity and performance of the models. Forecasts given by the various GARCH (1, 1) models are bootstrapped and the efficiency of the models is also investigated through Value at Risk backtesting. The data used is composed of the returns of the ALSI from the 30th of September 2003 to the 14th of August 2013 and the data frequency is daily data. The best fitting distribution is the skewed normal distribution. With regards to the best fitting GARCH (1, 1) model, the E-GARCH (1, 1) model using the normal distribution performed best. The forecasting analysis showed the outperformance of the E-GARCH (1, 1) model and the best underlying distribution is the student’s t-distribution followed by the skewed normal distribution. , M.Com. (Financial Economics)
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- Authors: Mokoena, Tholoana
- Date: 2016
- Subjects: GARCH model , Stock exchanges , Forecasting , Johannesburg Stock Exchange
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/124226 , uj:20891
- Description: Abstract: This study compares the fit and forecast performance of a selected group of parametric Generalised Autoregressive Conditional Heteroskedasticity GARCH (1, 1) models using various underlying distributions. The GARCH (1, 1) type models are empirically tested on the returns of the All Share Index (ALSI), a diversified portfolio of all the shares on the South African Johannesburg Stock Exchange (JSE). Estimates and forecasts generated by each model are compared and analysed to establish the validity and performance of the models. Forecasts given by the various GARCH (1, 1) models are bootstrapped and the efficiency of the models is also investigated through Value at Risk backtesting. The data used is composed of the returns of the ALSI from the 30th of September 2003 to the 14th of August 2013 and the data frequency is daily data. The best fitting distribution is the skewed normal distribution. With regards to the best fitting GARCH (1, 1) model, the E-GARCH (1, 1) model using the normal distribution performed best. The forecasting analysis showed the outperformance of the E-GARCH (1, 1) model and the best underlying distribution is the student’s t-distribution followed by the skewed normal distribution. , M.Com. (Financial Economics)
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The effect of extreme value distributions on market risk estimation
- Authors: Beytell, Donovan
- Date: 2016
- Subjects: Risk management - South Africa , GARCH model , Stock exchanges - South Africa
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/215607 , uj:21437
- Description: Abstract: Please refer to full text to view abstract , M.Com. (Financial Economics)
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- Authors: Beytell, Donovan
- Date: 2016
- Subjects: Risk management - South Africa , GARCH model , Stock exchanges - South Africa
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/215607 , uj:21437
- Description: Abstract: Please refer to full text to view abstract , M.Com. (Financial Economics)
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