The yield spread as a predictor for buy or sell signals for sectoral indices of the JSE
- Authors: Roeber, Christine
- Date: 2023-10-13
- Subjects: Yield curve , Rate of return South Africa , Yield spread , Interest rate , Johannesburg Stock Exchange
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/419687 , vital:71666
- Description: The predictive nature of the yield curve has been of interest to researchers for years. In this thesis, the evidence for the yield curve as a predictor is examine, specifically as a predictor for bear markets in the JSE stock market for 8 sub-sectoral indices. The study explores a dynamic market timing strategy for timing the South African stock market compared to a normal buy-and-hold strategy. First, probit models are estimated for each of the sectoral indices which did not prove to have tracked well all the bear market phases. Then a dynamic market timing portfolio is simulated against a buy-and-hold only strategy, the dynamic market timing portfolio proved to have outperformed a buy-and-hold strategy for almost all the indices. Thus, a Henriksson-Merton parametric model test which tests for market timing ability was done on these sub-indices. The research finds that the yield curve in South Africa is not a useful tool for a buy-sell strategy for most of the sub-sectoral indices of the JSE. , Thesis (MCom) -- Faculty of Commerce, Economics and Economic History, 2023
- Full Text:
- Authors: Roeber, Christine
- Date: 2023-10-13
- Subjects: Yield curve , Rate of return South Africa , Yield spread , Interest rate , Johannesburg Stock Exchange
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/419687 , vital:71666
- Description: The predictive nature of the yield curve has been of interest to researchers for years. In this thesis, the evidence for the yield curve as a predictor is examine, specifically as a predictor for bear markets in the JSE stock market for 8 sub-sectoral indices. The study explores a dynamic market timing strategy for timing the South African stock market compared to a normal buy-and-hold strategy. First, probit models are estimated for each of the sectoral indices which did not prove to have tracked well all the bear market phases. Then a dynamic market timing portfolio is simulated against a buy-and-hold only strategy, the dynamic market timing portfolio proved to have outperformed a buy-and-hold strategy for almost all the indices. Thus, a Henriksson-Merton parametric model test which tests for market timing ability was done on these sub-indices. The research finds that the yield curve in South Africa is not a useful tool for a buy-sell strategy for most of the sub-sectoral indices of the JSE. , Thesis (MCom) -- Faculty of Commerce, Economics and Economic History, 2023
- Full Text:
Stock market volatility during times of crisis: a comparative analysis of the conditional volatilities of JSE stock indices during the 2007/08 global financial crisis and COVID-19
- Authors: Wang, Zixiao
- Date: 2022-04-06
- Subjects: Stock exchanges , Johannesburg Stock Exchange , Global Financial Crisis, 2008-2009 , COVID-19 (Disease) Economic aspects , Economic forecasting , Stock exchanges and current events , GARCH model
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/284603 , vital:56078
- Description: This research analyses the comparative behaviour of stock market volatility during two crises. The goal of this research is to determine whether assumed cyclical and defensive sectors have either retained or revealed their expected properties during both the Global Financial Crisis (GFC) and COVID-19 by analysing sectoral volatility amid these two crises. Understanding how volatility changes amid crises helps to determine whether the volatility assumptions of diversified investment portfolios for both defensive and cyclical sectors still held given the different causes of each crisis. In turn, this knowledge can assist with risk management and portfolio allocation in stock market investments. The study can also contribute towards the enhancement of financial markets’ resistance against systemic risks through portfolio diversification, and aid government decision-making targeted at tackling the weaknesses of different economic sectors especially in times of overall economic weakness. This research makes use of the GARCH model to analyse a group of daily time series that consists of eleven sectoral indices and one benchmark index, all based on the South African stock markets. These observed series are categorised into two full sample periods, one designated to the Global Financial Crisis (January 2006 to May 2009) and the other for COVID-19 (January 2018 to May 2021). These are further divided into two sets of sub-sample periods, each made up of a pre-crisis and during-crisis. Furthermore, the dummy variables representing the occurrence of structural breaks are inserted into the full sample periods’ conditional variance equations. This is aimed at capturing the asymmetrical impact of the crises themselves on all observed series. Based on the movement of volatility persistency from pre-crisis to during-crisis for both crises, the results show that, firstly, Health Care and Consumer Goods are considered defensive Sectors. Secondly, Banks, Basic Materials, Chemicals, Telecommunications, and Financials are considered cyclical Sectors. Thirdly, Automobiles & Parts, Consumer Services, and Technology are considered indeterminable Sectors due to the inconsistent behaviour of these sectors’ volatility persistency throughout the sub-sample periods of both crises. Overall, according to the average volatility persistency, the observed series for COVID-19’s full sample period are generally less volatile than those of the GFC. However, the sub-sample periods suggest that the observed series for both pre-crisis and during-crisis periods of COVID-19 are more volatile than those same sub-samples of the Global Financial Crisis. Being able to analyse the characteristics of stock market sectors is crucial for risk management and optimal portfolio allocation of stock market investments. This can be achieved through portfolio diversification by investing in a variety of stocks, both cyclical and defensive, and adjusted over time based the needs of stock market investors. Diversified portfolios do not only serve the interests of individual investors, but can also enhance the financial markets’ overall resistance against systemic risks. , Thesis (MCom) -- Faculty of Commerce, Economics and Economic History, 2022
- Full Text:
- Authors: Wang, Zixiao
- Date: 2022-04-06
- Subjects: Stock exchanges , Johannesburg Stock Exchange , Global Financial Crisis, 2008-2009 , COVID-19 (Disease) Economic aspects , Economic forecasting , Stock exchanges and current events , GARCH model
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/284603 , vital:56078
- Description: This research analyses the comparative behaviour of stock market volatility during two crises. The goal of this research is to determine whether assumed cyclical and defensive sectors have either retained or revealed their expected properties during both the Global Financial Crisis (GFC) and COVID-19 by analysing sectoral volatility amid these two crises. Understanding how volatility changes amid crises helps to determine whether the volatility assumptions of diversified investment portfolios for both defensive and cyclical sectors still held given the different causes of each crisis. In turn, this knowledge can assist with risk management and portfolio allocation in stock market investments. The study can also contribute towards the enhancement of financial markets’ resistance against systemic risks through portfolio diversification, and aid government decision-making targeted at tackling the weaknesses of different economic sectors especially in times of overall economic weakness. This research makes use of the GARCH model to analyse a group of daily time series that consists of eleven sectoral indices and one benchmark index, all based on the South African stock markets. These observed series are categorised into two full sample periods, one designated to the Global Financial Crisis (January 2006 to May 2009) and the other for COVID-19 (January 2018 to May 2021). These are further divided into two sets of sub-sample periods, each made up of a pre-crisis and during-crisis. Furthermore, the dummy variables representing the occurrence of structural breaks are inserted into the full sample periods’ conditional variance equations. This is aimed at capturing the asymmetrical impact of the crises themselves on all observed series. Based on the movement of volatility persistency from pre-crisis to during-crisis for both crises, the results show that, firstly, Health Care and Consumer Goods are considered defensive Sectors. Secondly, Banks, Basic Materials, Chemicals, Telecommunications, and Financials are considered cyclical Sectors. Thirdly, Automobiles & Parts, Consumer Services, and Technology are considered indeterminable Sectors due to the inconsistent behaviour of these sectors’ volatility persistency throughout the sub-sample periods of both crises. Overall, according to the average volatility persistency, the observed series for COVID-19’s full sample period are generally less volatile than those of the GFC. However, the sub-sample periods suggest that the observed series for both pre-crisis and during-crisis periods of COVID-19 are more volatile than those same sub-samples of the Global Financial Crisis. Being able to analyse the characteristics of stock market sectors is crucial for risk management and optimal portfolio allocation of stock market investments. This can be achieved through portfolio diversification by investing in a variety of stocks, both cyclical and defensive, and adjusted over time based the needs of stock market investors. Diversified portfolios do not only serve the interests of individual investors, but can also enhance the financial markets’ overall resistance against systemic risks. , Thesis (MCom) -- Faculty of Commerce, Economics and Economic History, 2022
- Full Text:
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