The impact of oil and gold price fluctuations on the South African equity market : volatility spillovers and implications for portfolio management
- Morema, Kgotso Phiki Reginald
- Authors: Morema, Kgotso Phiki Reginald
- Date: 2017
- Subjects: Hedge funds , GARCH model , Stock exchanges , Business cycles
- Language: English
- Type: Masters (Thesis)
- Identifier: http://ujcontent.uj.ac.za8080/10210/379560 , http://hdl.handle.net/10210/271916 , uj:28929
- Description: M.Com. (Financial Economics) , Abstract: This paper aims to study the impact of gold and oil price fluctuations on the volatility of the South African stock market and its component indices or sectors – namely, the financial, industrial and resource sectors – making use of the asymmetric dynamic conditional correlation (ADCC) generalised autoregressive conditional heteroskedasticity (GARCH) model. Moreover, the study assesses the magnitude of the optimal portfolio weight, hedge ratio and hedge effectiveness for portfolios that are constituted of a pair of assets, namely oil-stock and gold-stock pairs. The findings of the study show that there is significant volatility spillover between the gold and the stock markets, and the oil and stock markets. This finding suggests the importance of the link between futures commodity markets and the stock markets, which is essential for portfolio management. Moreover, the results on the dynamic correlation between the two pairs of markets show high variation in their correlations over time, varying between positive and negative values. This finding indicates an opportunity for meaningful portfolio diversification during periods of negative correlation. With reference to portfolio optimisation and the possibility of hedging when using the pairs of assets under study, the findings suggest the importance of combining oil and stocks as well as gold and stocks for effective hedging against any risks.
- Full Text:
- Authors: Morema, Kgotso Phiki Reginald
- Date: 2017
- Subjects: Hedge funds , GARCH model , Stock exchanges , Business cycles
- Language: English
- Type: Masters (Thesis)
- Identifier: http://ujcontent.uj.ac.za8080/10210/379560 , http://hdl.handle.net/10210/271916 , uj:28929
- Description: M.Com. (Financial Economics) , Abstract: This paper aims to study the impact of gold and oil price fluctuations on the volatility of the South African stock market and its component indices or sectors – namely, the financial, industrial and resource sectors – making use of the asymmetric dynamic conditional correlation (ADCC) generalised autoregressive conditional heteroskedasticity (GARCH) model. Moreover, the study assesses the magnitude of the optimal portfolio weight, hedge ratio and hedge effectiveness for portfolios that are constituted of a pair of assets, namely oil-stock and gold-stock pairs. The findings of the study show that there is significant volatility spillover between the gold and the stock markets, and the oil and stock markets. This finding suggests the importance of the link between futures commodity markets and the stock markets, which is essential for portfolio management. Moreover, the results on the dynamic correlation between the two pairs of markets show high variation in their correlations over time, varying between positive and negative values. This finding indicates an opportunity for meaningful portfolio diversification during periods of negative correlation. With reference to portfolio optimisation and the possibility of hedging when using the pairs of assets under study, the findings suggest the importance of combining oil and stocks as well as gold and stocks for effective hedging against any risks.
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A comparative analysis of the synchronisation of business cycles for developed and developing economies with the world business cycle
- Authors: Botha, Ilse
- Date: 2010
- Subjects: Business cycles
- Type: Article
- Identifier: uj:5522 , http://hdl.handle.net/10210/13922
- Description: Globalisation brought about worldwide changes, including economic and financial integration between countries. The objective of this paper is to establish if there is synchronisation between developed and developing countries with the world cycle. Research results show that business cycles have become less volatile after globalisation, but there is not much consensus on whether business cycles have become less or more synchronised since globalisation. Little research has been done on co-movement between emerging markets, such as South Africa, and the world business cycle. This paper derives common factors for developed and developing countries by applying principal component analysis (PCA) to output, consumption and investment data, which represents the countries’ business cycles. The empirical analysis shows co-movement between some countries and the world business cycle (G7 countries as proxy). The results suggest that there are idiosyncratic and globally common shocks, which play different roles over time in different countries. The paper goes on to suggest that there are clear differences in how developed and emerging markets co-move with the world business cycle. A key finding is that the co-movement between developing economies and the world business cycle has increased since globalisation. This research also confirms previous research that most economies follow the world business cycle when large shocks – such as the recent economic downturn – occur. This has implications for forecasting the business cycle, especially in times of economic turmoil.
- Full Text:
- Authors: Botha, Ilse
- Date: 2010
- Subjects: Business cycles
- Type: Article
- Identifier: uj:5522 , http://hdl.handle.net/10210/13922
- Description: Globalisation brought about worldwide changes, including economic and financial integration between countries. The objective of this paper is to establish if there is synchronisation between developed and developing countries with the world cycle. Research results show that business cycles have become less volatile after globalisation, but there is not much consensus on whether business cycles have become less or more synchronised since globalisation. Little research has been done on co-movement between emerging markets, such as South Africa, and the world business cycle. This paper derives common factors for developed and developing countries by applying principal component analysis (PCA) to output, consumption and investment data, which represents the countries’ business cycles. The empirical analysis shows co-movement between some countries and the world business cycle (G7 countries as proxy). The results suggest that there are idiosyncratic and globally common shocks, which play different roles over time in different countries. The paper goes on to suggest that there are clear differences in how developed and emerging markets co-move with the world business cycle. A key finding is that the co-movement between developing economies and the world business cycle has increased since globalisation. This research also confirms previous research that most economies follow the world business cycle when large shocks – such as the recent economic downturn – occur. This has implications for forecasting the business cycle, especially in times of economic turmoil.
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The performance of different asset classes during the stages of the investment cycle
- Authors: Van Heerden, Roelof Eugene
- Date: 2012-08-15
- Subjects: Business cycles , Investments - Management.
- Type: Mini-Dissertation
- Identifier: uj:9413 , http://hdl.handle.net/10210/5848
- Description: M.Comm. , The objectives of the study are to explore the different macro and micro forecasting techniques used in determining the stage of the economic cycle which will ultimately assist in deriving the stage of the investment cycle. Following on the top-down analysis, is the analysis of the impact of the interest rate cycle and its influence on asset classes through the evolution of the investment cycle as the impact of the interest rate cycle holds different investment consequences for equities, bonds and cash. Different investment styles are also analyzed in order to highlight the changing dynamics of fund management. The value and growth investment styles are analyzed in conjunction with momentum and size investing. Hedge funds were excluded for the purposes of this study. Stock market valuation and liquidity influence markets differently during the course of the business cycle although they do ultimately filter through in the tactical asset allocation process. Deriving the investment cycle through its different stages is the final step in the investment analysis process prior to the formulation of an investment strategy. The dynamic interaction between the mentioned macro and micro variables result in different tactical asset allocation strategies by different fund managers which ultimately determine their success as fund managers. Being cognisant of the interaction between the asset classes during the different stages of the investment cycle will assist in maximizing investment returns for a given level of risk.
- Full Text:
- Authors: Van Heerden, Roelof Eugene
- Date: 2012-08-15
- Subjects: Business cycles , Investments - Management.
- Type: Mini-Dissertation
- Identifier: uj:9413 , http://hdl.handle.net/10210/5848
- Description: M.Comm. , The objectives of the study are to explore the different macro and micro forecasting techniques used in determining the stage of the economic cycle which will ultimately assist in deriving the stage of the investment cycle. Following on the top-down analysis, is the analysis of the impact of the interest rate cycle and its influence on asset classes through the evolution of the investment cycle as the impact of the interest rate cycle holds different investment consequences for equities, bonds and cash. Different investment styles are also analyzed in order to highlight the changing dynamics of fund management. The value and growth investment styles are analyzed in conjunction with momentum and size investing. Hedge funds were excluded for the purposes of this study. Stock market valuation and liquidity influence markets differently during the course of the business cycle although they do ultimately filter through in the tactical asset allocation process. Deriving the investment cycle through its different stages is the final step in the investment analysis process prior to the formulation of an investment strategy. The dynamic interaction between the mentioned macro and micro variables result in different tactical asset allocation strategies by different fund managers which ultimately determine their success as fund managers. Being cognisant of the interaction between the asset classes during the different stages of the investment cycle will assist in maximizing investment returns for a given level of risk.
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The impact of an economic recession on the working capital management of small and medium enterprises in South Africa
- Authors: Shadung, Ledile
- Date: 2015-09-28
- Subjects: Small business - South Africa , Recessions - South Africa , Business cycles , Depressions , Working capital - Management , Johannesburg Stock Exchange
- Type: Thesis
- Identifier: uj:14177 , http://hdl.handle.net/10210/14620
- Description: M.Com. (Financial Management) , Working capital management (WCM) is considered critical for the success of all business and especially for small businesses. A recession (such as the one that took place in 2009) complicates the working capital management of small businesses. Working capital management of a sample of small and medium enterprises in South Africa were investigated to determine how they manage their working capital during challenging economic conditions. The impact of the 2009 economic recession on WCM was specifically investigated by following a quantitative descriptive research approach. The study sample consisted of 44 companies listed on the JSE Ltd AltX Index. A trend analysis was applied on WCM variables to determine significant changes overthe study period. Because variables were not normally distributed, the Mann Whitney U test was conducted to determine the statistical significance of the WCM mean ranks pre-, during and post-recession phases. The trend analysis of working capital management over the six-year study period exhibited a significant improvement in the working capital management level during the economic recession. This was largely attributed to delaying payment to creditors. The analysis of the WCM variables pre-, during and post-recession phases indicated that there were no significant changes in WCM that can be attributed to the 2009 economic recession. It was concluded that although there were changes in working capital management over the study period, the changes could not only be attributed to the 2009 recession.
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- Authors: Shadung, Ledile
- Date: 2015-09-28
- Subjects: Small business - South Africa , Recessions - South Africa , Business cycles , Depressions , Working capital - Management , Johannesburg Stock Exchange
- Type: Thesis
- Identifier: uj:14177 , http://hdl.handle.net/10210/14620
- Description: M.Com. (Financial Management) , Working capital management (WCM) is considered critical for the success of all business and especially for small businesses. A recession (such as the one that took place in 2009) complicates the working capital management of small businesses. Working capital management of a sample of small and medium enterprises in South Africa were investigated to determine how they manage their working capital during challenging economic conditions. The impact of the 2009 economic recession on WCM was specifically investigated by following a quantitative descriptive research approach. The study sample consisted of 44 companies listed on the JSE Ltd AltX Index. A trend analysis was applied on WCM variables to determine significant changes overthe study period. Because variables were not normally distributed, the Mann Whitney U test was conducted to determine the statistical significance of the WCM mean ranks pre-, during and post-recession phases. The trend analysis of working capital management over the six-year study period exhibited a significant improvement in the working capital management level during the economic recession. This was largely attributed to delaying payment to creditors. The analysis of the WCM variables pre-, during and post-recession phases indicated that there were no significant changes in WCM that can be attributed to the 2009 economic recession. It was concluded that although there were changes in working capital management over the study period, the changes could not only be attributed to the 2009 recession.
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Does uncertainty predict cryptocurrency returns? A copula based approach
- Authors: Koumba, Ur Armand
- Date: 2018
- Subjects: Cryptocurrencies , Business cycles , Foreign exchange rates
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/295891 , uj:32230
- Description: M.Com. (Financial Economics) , Abstract: Please refer to full text to view abstract.
- Full Text:
- Authors: Koumba, Ur Armand
- Date: 2018
- Subjects: Cryptocurrencies , Business cycles , Foreign exchange rates
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/295891 , uj:32230
- Description: M.Com. (Financial Economics) , Abstract: Please refer to full text to view abstract.
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Equity price predictions of selected African emerging markets
- Authors: Kavenga, Dunmore
- Date: 2018
- Subjects: Forecasting - Marketing , Business cycles , Investment analysis , Economic forecasting , Money market
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/403189 , uj:33773
- Description: Abstract : Predicting equity share prices could be useful to various stakeholders. The common methods used to forecast equity share price besides the naïve model are the Autoregressive Conditional Heteroskedasticity (ARCH) and General Autoregressive Conditional Heteroskedasticity (GARCH) models, however, no conclusion has been reached as to which model produces the most accurate predictions. In this research, ARCH and GARCH forecasting models (and their extended variants), as well as the Monte Carlo Simulation, were used to forecast price-weighted equity indices that were constructed from the South African, Nigerian, and Kenyan share markets. These three countries were selected based on their significance in the African continent due to the relative size of their economies and the liquidity of their share markets. The daily closing share prices for companies listed on the FTSE/JSE Top 40 Index, NSE Top 30 Index, and the NrSE Top 20 Index were collected between the 4th of January 2010 and the 30th of June 2015. The companies that were selected from each of these indices to construct the price-weighted indices for each country, were based on criteria to eliminate bias. Different autoregressive models were fitted for the mean equation. The EViews statistical programme was used to analyse the data. The ARCH effects were tested using the ARCH LM test. The ARCH/GARCH family models selected were GARCH (2,1), EGARCH (2,2), and EGARCH (2,1) for Nigeria, Kenya, and South Africa respectively. A Monte Carlo Simulation with 1 200 iterations was also performed to forecast the equity share prices. Post estimation and performance evaluation metrics were performed using the RMSE, MSE, MAD, and MAPE. The results based on the evaluation metrics indicated that the ARCH/GARCH models in-sample forecasts were more accurate than out-of-sample forecasts. The accuracy of the ARCH/GARCH models’ predictions was sounder than that of the Monte Carlo Simulation based on the evaluation metrics. Comparing the forecasting models to the actual graphs, in most cases the ARCH/GARCH models were closer to the actuals than the Monte Carlo II Simulation. The accuracy of the model predictions were also influenced by the sample size, the nature of the data, the leverage effect, and the macro economic conditions. In conclusion, the African equity markets cannot be predicted accurately using the ARCH/GARCH models and the Monte Carlo Simulation. The predictions from the forecasting models are not sufficiently accurate for investors, traders, and company management to use to make informed decisions. However, these predictions are better than the naïve model. The researcher also concluded that the markets are efficient, as the publicly available information cannot be used to gain abnormal returns. This study’s findings are similar to those of previous studies carried out in South Africa and globally. , M.Com. (Finance)
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- Authors: Kavenga, Dunmore
- Date: 2018
- Subjects: Forecasting - Marketing , Business cycles , Investment analysis , Economic forecasting , Money market
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/403189 , uj:33773
- Description: Abstract : Predicting equity share prices could be useful to various stakeholders. The common methods used to forecast equity share price besides the naïve model are the Autoregressive Conditional Heteroskedasticity (ARCH) and General Autoregressive Conditional Heteroskedasticity (GARCH) models, however, no conclusion has been reached as to which model produces the most accurate predictions. In this research, ARCH and GARCH forecasting models (and their extended variants), as well as the Monte Carlo Simulation, were used to forecast price-weighted equity indices that were constructed from the South African, Nigerian, and Kenyan share markets. These three countries were selected based on their significance in the African continent due to the relative size of their economies and the liquidity of their share markets. The daily closing share prices for companies listed on the FTSE/JSE Top 40 Index, NSE Top 30 Index, and the NrSE Top 20 Index were collected between the 4th of January 2010 and the 30th of June 2015. The companies that were selected from each of these indices to construct the price-weighted indices for each country, were based on criteria to eliminate bias. Different autoregressive models were fitted for the mean equation. The EViews statistical programme was used to analyse the data. The ARCH effects were tested using the ARCH LM test. The ARCH/GARCH family models selected were GARCH (2,1), EGARCH (2,2), and EGARCH (2,1) for Nigeria, Kenya, and South Africa respectively. A Monte Carlo Simulation with 1 200 iterations was also performed to forecast the equity share prices. Post estimation and performance evaluation metrics were performed using the RMSE, MSE, MAD, and MAPE. The results based on the evaluation metrics indicated that the ARCH/GARCH models in-sample forecasts were more accurate than out-of-sample forecasts. The accuracy of the ARCH/GARCH models’ predictions was sounder than that of the Monte Carlo Simulation based on the evaluation metrics. Comparing the forecasting models to the actual graphs, in most cases the ARCH/GARCH models were closer to the actuals than the Monte Carlo II Simulation. The accuracy of the model predictions were also influenced by the sample size, the nature of the data, the leverage effect, and the macro economic conditions. In conclusion, the African equity markets cannot be predicted accurately using the ARCH/GARCH models and the Monte Carlo Simulation. The predictions from the forecasting models are not sufficiently accurate for investors, traders, and company management to use to make informed decisions. However, these predictions are better than the naïve model. The researcher also concluded that the markets are efficient, as the publicly available information cannot be used to gain abnormal returns. This study’s findings are similar to those of previous studies carried out in South Africa and globally. , M.Com. (Finance)
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