A time-varying analysis of the sensitivity in commercial bank stock returns to market, interest rate and foreign exchange risk exposures in South Africa
- Authors: Mazomba, Xolani
- Date: 2017
- Subjects: Banks and banking - South Africa , Interest rates - South Africa , Foreign exchange rates - South Africa , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/245884 , uj:25477
- Description: M.Com. , Abstract: The objective of this study is to investigate the sensitivity of the South African commercial banks to the market, interest rate and exchange rate risk exposures. The study estimates a GARCH (1,1) model using the above variables including their conditional variances. To investigate the impact of the risk premium factor, a GARCH-in-Mean model with implied volatility of the exogenous variables as explanatory variables is used. The research relies on data of the JSE Top 40 companies and the major commercial banks. The data series ranges from 2003 to 2016. Using the TED spread, the data is split into three sub-samples the period prior to the crisis, during the crisis and the post-crisis period. It was found that the bank stock returns are sensitive to the market, interest rate and exchange rate risk. The banks are found to be influenced mostly by the 1-year and 10-year rates during the low volatility periods, while during the crisis period the impact extends to even shorter periods of 1-month, 3-month and 6-month yields. Furthermore, the banks are found to be more sensitive to the exchange rate during the low volatile periods, while the small banks are the most affected during the high volatility periods. Regarding the conditional variance, the study found that the bank stock returns follow a GARCH generating process. Furthermore, the study found that the conditional volatility from the GARCH-in-Mean model was irrelevant in pricing the bank stocks during the high volatility periods. The conditional variance of the GARCH-M was estimated with an inclusion of the implied volatility of the exogenous variables: market, interest rate and foreign exchange rate returns. The study found that the parameters have a very low significance overall and the impact of the volatility from the market and foreign exchange rate tended to decline during the high volatility period; while the effect of the interest rate volatility rises during the same period.
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- Authors: Mazomba, Xolani
- Date: 2017
- Subjects: Banks and banking - South Africa , Interest rates - South Africa , Foreign exchange rates - South Africa , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/245884 , uj:25477
- Description: M.Com. , Abstract: The objective of this study is to investigate the sensitivity of the South African commercial banks to the market, interest rate and exchange rate risk exposures. The study estimates a GARCH (1,1) model using the above variables including their conditional variances. To investigate the impact of the risk premium factor, a GARCH-in-Mean model with implied volatility of the exogenous variables as explanatory variables is used. The research relies on data of the JSE Top 40 companies and the major commercial banks. The data series ranges from 2003 to 2016. Using the TED spread, the data is split into three sub-samples the period prior to the crisis, during the crisis and the post-crisis period. It was found that the bank stock returns are sensitive to the market, interest rate and exchange rate risk. The banks are found to be influenced mostly by the 1-year and 10-year rates during the low volatility periods, while during the crisis period the impact extends to even shorter periods of 1-month, 3-month and 6-month yields. Furthermore, the banks are found to be more sensitive to the exchange rate during the low volatile periods, while the small banks are the most affected during the high volatility periods. Regarding the conditional variance, the study found that the bank stock returns follow a GARCH generating process. Furthermore, the study found that the conditional volatility from the GARCH-in-Mean model was irrelevant in pricing the bank stocks during the high volatility periods. The conditional variance of the GARCH-M was estimated with an inclusion of the implied volatility of the exogenous variables: market, interest rate and foreign exchange rate returns. The study found that the parameters have a very low significance overall and the impact of the volatility from the market and foreign exchange rate tended to decline during the high volatility period; while the effect of the interest rate volatility rises during the same period.
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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.
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- 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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Non-parametric approach to VaR for portfolios in the South African equity market
- Authors: Saffy, Kyle
- Date: 2017
- Subjects: Stock exchanges - South Africa , Risk management - South Africa , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/271875 , uj:28924
- Description: M.Com. (Economics) , Abstract: The best measure for market risk is still a question that has remained largely unanswered. There are a variety of different methodologies that attempt to answer this question. The goal of this study is to assess how combining different elements of different Value at Risk (VaR) models contributes to better estimations of the risk inherent within a portfolio, thereby resulting is a sufficient risk capital allocation without over providing for risk that does not exist within the system. This study makes use of three VaR models, namely Constant Volatility Portfolio VaR approach, Conditional Volatility Single Asset VaR approach, and Conditional Volatility Portfolio VaR approach. The Constant Volatility Portfolio VaR approach consists of using Standard Deviation in order to estimate the volatility and the Pearson Correlation Coefficient in order to estimate how the constituents of the portfolio interact with one another; this is constructed using a standard Variance Covariance approach. The Conditional Volatility Single Asset VaR approach is constructed using a Historical Simulation VaR approach, where the historical returns dataset is scaled using the most recent volatility within the portfolio in order to give the estimation some symmetry based on what has occurred during stressed periods. No decomposition of the portfolio is used and therefore the end of day price of the portfolio is used to generate the returns dataset, thereby giving the portfolio a single asset appearance. The Conditional Volatility Portfolio VaR approach uses a GARCH(1,1) process in order to estimate volatility on a constituent based approach and then uses the Variance Covariance approach to combine the constituent’s volatility and the correlation, which is calculated using Kendall’s Tau. In order to evaluate the performance of each VaR model, back-testing is used. The techniques used are the Traffic Light Test, Probability of Failure and a measure of the amount of capital that is used. The use of capital test is designed to assess how much capital is utilised as a proportion of the underlying volatility. This test is only useful if the calculation passes the two aforementioned tests. Using daily financial time series of the JSE TOP40 index and the JSE DTOP index from 02 June 2008 to 14 April 2016, the VaR calculations are generated and the back-testing is appropriately conducted. Based on the back-testing results, it is found that the best approach was Conditional Volatility Portfolio VaR approach because it passed both of the back-tests, as well as having the lowest capital usage figure if only a portion of the back-testing passes are considered. It is found that the capital requirement obtained with this methodology find that the capital number should be bound between 3.77% and 6.70% on the TOP40 and 3.82% and 6.78% on the DTOP.
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- Authors: Saffy, Kyle
- Date: 2017
- Subjects: Stock exchanges - South Africa , Risk management - South Africa , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/271875 , uj:28924
- Description: M.Com. (Economics) , Abstract: The best measure for market risk is still a question that has remained largely unanswered. There are a variety of different methodologies that attempt to answer this question. The goal of this study is to assess how combining different elements of different Value at Risk (VaR) models contributes to better estimations of the risk inherent within a portfolio, thereby resulting is a sufficient risk capital allocation without over providing for risk that does not exist within the system. This study makes use of three VaR models, namely Constant Volatility Portfolio VaR approach, Conditional Volatility Single Asset VaR approach, and Conditional Volatility Portfolio VaR approach. The Constant Volatility Portfolio VaR approach consists of using Standard Deviation in order to estimate the volatility and the Pearson Correlation Coefficient in order to estimate how the constituents of the portfolio interact with one another; this is constructed using a standard Variance Covariance approach. The Conditional Volatility Single Asset VaR approach is constructed using a Historical Simulation VaR approach, where the historical returns dataset is scaled using the most recent volatility within the portfolio in order to give the estimation some symmetry based on what has occurred during stressed periods. No decomposition of the portfolio is used and therefore the end of day price of the portfolio is used to generate the returns dataset, thereby giving the portfolio a single asset appearance. The Conditional Volatility Portfolio VaR approach uses a GARCH(1,1) process in order to estimate volatility on a constituent based approach and then uses the Variance Covariance approach to combine the constituent’s volatility and the correlation, which is calculated using Kendall’s Tau. In order to evaluate the performance of each VaR model, back-testing is used. The techniques used are the Traffic Light Test, Probability of Failure and a measure of the amount of capital that is used. The use of capital test is designed to assess how much capital is utilised as a proportion of the underlying volatility. This test is only useful if the calculation passes the two aforementioned tests. Using daily financial time series of the JSE TOP40 index and the JSE DTOP index from 02 June 2008 to 14 April 2016, the VaR calculations are generated and the back-testing is appropriately conducted. Based on the back-testing results, it is found that the best approach was Conditional Volatility Portfolio VaR approach because it passed both of the back-tests, as well as having the lowest capital usage figure if only a portion of the back-testing passes are considered. It is found that the capital requirement obtained with this methodology find that the capital number should be bound between 3.77% and 6.70% on the TOP40 and 3.82% and 6.78% on the DTOP.
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Estimation of value-at-risk in BRICS economies : use of multivariate GARCH models
- Authors: Nleya, Lebogang
- Date: 2015
- Subjects: Risk - Econometric models , GARCH model , Stock exchanges , BRIC countries
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/75087 , uj:18630
- Description: Abstract: A substantial amount of studies have estimated market risk by employing multivariate GARCH models but none of these studies according to be best of our knowledge has applied this technique on BRICS data . The aim of this paper is to compare the performance of three multivariate risk models (DCC-GARCH, ADCC-GARCH and CCC-GARCH) in estimating portfolio Value-at-Risk (VaR). Unlike the previous literature, we employ in our study the BRICS data and different weights to assess how changes in these weights affect the performance of the different multivariate risk models. The equity market indexes from the five countries that the paper employs are; the Brazilian Ibovespa Brasil Sao Paulo Stock Exchange Index (IBOV), Russian MICEX index, Indian S&P BSE SENSEX Index (SENSEX), Chinese Shanghai Stock Exchange Composite Index (SHCOMP) and the South African Johannesburg All Share Index (ALSI). In addition the Brazilian real/USD (brl/usd), Russian ruble/usd, Indian rupee/USD (inr/usd), renminbi/USD (cyn/usd) and the rand/USD (zar/usd) exchange rates are also employed in the study. The Average Deviations, Quadratic Probability Function Score and the Root Mean Square Error are used to backtest the performance of the models at 90%. The results indicate that multivariate GARCH models of dynamic correlation, in particular the DCC and ADCC-GARCH perform better than the CCC. In addition, giving more weight to currencies and less to equities proves to be the best way of minimizing risk in BRICS when holding a portfolio made of foreign exchanges and equities. , M.Com.
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- Authors: Nleya, Lebogang
- Date: 2015
- Subjects: Risk - Econometric models , GARCH model , Stock exchanges , BRIC countries
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/75087 , uj:18630
- Description: Abstract: A substantial amount of studies have estimated market risk by employing multivariate GARCH models but none of these studies according to be best of our knowledge has applied this technique on BRICS data . The aim of this paper is to compare the performance of three multivariate risk models (DCC-GARCH, ADCC-GARCH and CCC-GARCH) in estimating portfolio Value-at-Risk (VaR). Unlike the previous literature, we employ in our study the BRICS data and different weights to assess how changes in these weights affect the performance of the different multivariate risk models. The equity market indexes from the five countries that the paper employs are; the Brazilian Ibovespa Brasil Sao Paulo Stock Exchange Index (IBOV), Russian MICEX index, Indian S&P BSE SENSEX Index (SENSEX), Chinese Shanghai Stock Exchange Composite Index (SHCOMP) and the South African Johannesburg All Share Index (ALSI). In addition the Brazilian real/USD (brl/usd), Russian ruble/usd, Indian rupee/USD (inr/usd), renminbi/USD (cyn/usd) and the rand/USD (zar/usd) exchange rates are also employed in the study. The Average Deviations, Quadratic Probability Function Score and the Root Mean Square Error are used to backtest the performance of the models at 90%. The results indicate that multivariate GARCH models of dynamic correlation, in particular the DCC and ADCC-GARCH perform better than the CCC. In addition, giving more weight to currencies and less to equities proves to be the best way of minimizing risk in BRICS when holding a portfolio made of foreign exchanges and equities. , M.Com.
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Volatility transmission between commodities and the exchange rate in the South African market
- Authors: Dlamini, Shanon Amanda
- Date: 2018
- Subjects: Commodity exchanges , Stock exchanges , Commodity futures , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/292145 , uj:31744
- Description: Abstract: This study examines the volatility transmission for the traded commodities: precious metals (that is, gold and platinum) and crude oil while accounting for volatility transmission between the commodities and the FTSE/JSE Top 40 Index, as well as the USD/ZAR exchange rate within a multivariate system. In order to investigate the volatility transmission, the two Multivariate GARCH models, namely CCC-MGARCH and BEKK- MGARCH were employed on daily data from 1 January 2012 to 31 December 2017. The findings indicated that there were persistent volatility effects for individual markets, suggesting that strong ARCH and GARCH effects were present in the data. The ARCH and GARCH estimates of conditional variance were statistically significant and the conditional correlations between the volatility of precious metals, crude oil, the real exchange rate, and the stock exchange were significant as well. The empirical results indicated an existence of significant shock and volatility transmission across commodities and FTSE/JSE Top 40 Index, as well as between the South African Rand (against the United States Dollar) and the commodities, however, the amount of volatility interactions differs from one market to another. , M.Com. (Finance)
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- Authors: Dlamini, Shanon Amanda
- Date: 2018
- Subjects: Commodity exchanges , Stock exchanges , Commodity futures , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/292145 , uj:31744
- Description: Abstract: This study examines the volatility transmission for the traded commodities: precious metals (that is, gold and platinum) and crude oil while accounting for volatility transmission between the commodities and the FTSE/JSE Top 40 Index, as well as the USD/ZAR exchange rate within a multivariate system. In order to investigate the volatility transmission, the two Multivariate GARCH models, namely CCC-MGARCH and BEKK- MGARCH were employed on daily data from 1 January 2012 to 31 December 2017. The findings indicated that there were persistent volatility effects for individual markets, suggesting that strong ARCH and GARCH effects were present in the data. The ARCH and GARCH estimates of conditional variance were statistically significant and the conditional correlations between the volatility of precious metals, crude oil, the real exchange rate, and the stock exchange were significant as well. The empirical results indicated an existence of significant shock and volatility transmission across commodities and FTSE/JSE Top 40 Index, as well as between the South African Rand (against the United States Dollar) and the commodities, however, the amount of volatility interactions differs from one market to another. , M.Com. (Finance)
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The predictability of share return volatility on the JSE limited
- Authors: Van Jaarsveld, Paul Anrich
- Date: 2018
- Subjects: Johannesburg Stock Exchange , Economic forecasting , Stocks - Rate of return , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/272271 , uj:28975
- Description: M.Com. (Finance) , Abstract: Predicting share return volatility accurately in financial markets has become increasingly important, as it offers investors, market analysts and risk managers the ability to correctly price financial instruments, effectively manage portfolios and conduct accurate risk assessments. The most popular method to predict this volatility is by using GARCH models, but there is no consensus as to which model offers the most accurate forecasts for volatility. Factors such as sample size, country characteristics and the leverage effect all have an influence in determining which model delivers the most accurate forecasts for volatility of share returns. The goal of this study is to determine how accurately share returns can be predicted by using GARCH, GJR GARCH and EGARCH models as well as a benchmark ARMA model. In-sample and out-of-sample forecasts will be conducted to determine which model offers the most accurate forecast of share returns on the JSE Top 40 Index. Results indicated that the ARMA (1,2) model produced the most accurate in-sample forecast, while the asymmetric EGARCH (2,1) produced the most accurate out-of-sample forecast. These findings are consistent with those from other South African studies.
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- Authors: Van Jaarsveld, Paul Anrich
- Date: 2018
- Subjects: Johannesburg Stock Exchange , Economic forecasting , Stocks - Rate of return , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/272271 , uj:28975
- Description: M.Com. (Finance) , Abstract: Predicting share return volatility accurately in financial markets has become increasingly important, as it offers investors, market analysts and risk managers the ability to correctly price financial instruments, effectively manage portfolios and conduct accurate risk assessments. The most popular method to predict this volatility is by using GARCH models, but there is no consensus as to which model offers the most accurate forecasts for volatility. Factors such as sample size, country characteristics and the leverage effect all have an influence in determining which model delivers the most accurate forecasts for volatility of share returns. The goal of this study is to determine how accurately share returns can be predicted by using GARCH, GJR GARCH and EGARCH models as well as a benchmark ARMA model. In-sample and out-of-sample forecasts will be conducted to determine which model offers the most accurate forecast of share returns on the JSE Top 40 Index. Results indicated that the ARMA (1,2) model produced the most accurate in-sample forecast, while the asymmetric EGARCH (2,1) produced the most accurate out-of-sample forecast. These findings are consistent with those from other South African studies.
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The determination of minimum capital requirement using expected shortfall : a case study of a South African equity market
- Authors: Diniz De Moura, Pedro
- Date: 2019
- Subjects: Stock exchanges - South Africa , Financial risk management - South Africa , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/295802 , uj:32219
- Description: M.Com. (Financial Economics) , Abstract: Please refer to full text to view abstract.
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- Authors: Diniz De Moura, Pedro
- Date: 2019
- Subjects: Stock exchanges - South Africa , Financial risk management - South Africa , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/295802 , uj:32219
- Description: M.Com. (Financial Economics) , Abstract: Please refer to full text to view abstract.
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Forecasting volatility on the Resources 10, Financial 15 and Industrial 25 FTSE/JSE indices
- Authors: Petja, Albert Pogiso
- Date: 2018
- Subjects: JSE Limited , Forecasting , GARCH model , Investments - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/282383 , uj:30415
- Description: M.Com. (Investment Management) , Abstract: The focus of this study is primarily based on the significance of forecasting volatility on the JSE Limited. The study investigates the appropriateness of using volatility models to forecast volatility on the Resource 10 (RESI), Financial 15 (FINI), and Industrial 25 (INDI) FTSE/JSE sector-indices classified according to the Industry Classification Benchmark (ICB). This study uses historical closing values of the three FTSE/JSE indices which are then converted into log returns. Quantitative data are used to investigate whether volatility on the RESI, FINI, and INDI FTSE/JSE indices is correctly specified by ARCH class of models. The data are obtained from McGregor I-NET BFA databases and spans the period from 17 February 2006 to 16 February 2016. The 10 year period is also divided into two 5 year sub-periods and five 2 year sub-periods for each FTSE/JSE index. This study employs the Autoregressive Conditional Heteroscedasticity (ARCH) model, the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, and the Threshold (Generalised) Autoregressive Conditional Heteroscedasticity (TARCH) model. These models are used to generate in-sample forecasts of volatility on the three aforementioned FTSE/JSE indices. The performance of the volatility models used in this study is evaluated based on three statistical loss functions: the root mean squared error, mean absolute error, and the mean absolute percent error. The results of this study evidence the presence of ARCH effects in the data of the three FTSE/JSE indices. The ARCH, GARCH and TARCH specifications are statistically significant for all indices; though there are some sub-periods of each of the FTSE/JSE indices which show no statistical significance in the parameter estimates of the volatility models employed. There is also evidence of volatility asymmetry in all of the FTSE/JSE indices considered in this study. There is no single superior volatility model between all three ARCH models that specifies the volatility of the FTSE/JSE indices over all the others when the forecasts are evaluated based on the statistical loss functions. However, the TARCH model outperforms the ARCH and GARCH models in most cases. This means that accounting for asymmetries in volatility is important in generating reliable volatility forecasts.
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- Authors: Petja, Albert Pogiso
- Date: 2018
- Subjects: JSE Limited , Forecasting , GARCH model , Investments - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/282383 , uj:30415
- Description: M.Com. (Investment Management) , Abstract: The focus of this study is primarily based on the significance of forecasting volatility on the JSE Limited. The study investigates the appropriateness of using volatility models to forecast volatility on the Resource 10 (RESI), Financial 15 (FINI), and Industrial 25 (INDI) FTSE/JSE sector-indices classified according to the Industry Classification Benchmark (ICB). This study uses historical closing values of the three FTSE/JSE indices which are then converted into log returns. Quantitative data are used to investigate whether volatility on the RESI, FINI, and INDI FTSE/JSE indices is correctly specified by ARCH class of models. The data are obtained from McGregor I-NET BFA databases and spans the period from 17 February 2006 to 16 February 2016. The 10 year period is also divided into two 5 year sub-periods and five 2 year sub-periods for each FTSE/JSE index. This study employs the Autoregressive Conditional Heteroscedasticity (ARCH) model, the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, and the Threshold (Generalised) Autoregressive Conditional Heteroscedasticity (TARCH) model. These models are used to generate in-sample forecasts of volatility on the three aforementioned FTSE/JSE indices. The performance of the volatility models used in this study is evaluated based on three statistical loss functions: the root mean squared error, mean absolute error, and the mean absolute percent error. The results of this study evidence the presence of ARCH effects in the data of the three FTSE/JSE indices. The ARCH, GARCH and TARCH specifications are statistically significant for all indices; though there are some sub-periods of each of the FTSE/JSE indices which show no statistical significance in the parameter estimates of the volatility models employed. There is also evidence of volatility asymmetry in all of the FTSE/JSE indices considered in this study. There is no single superior volatility model between all three ARCH models that specifies the volatility of the FTSE/JSE indices over all the others when the forecasts are evaluated based on the statistical loss functions. However, the TARCH model outperforms the ARCH and GARCH models in most cases. This means that accounting for asymmetries in volatility is important in generating reliable volatility forecasts.
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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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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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Risk-return nexus in a GARCH-M framework : empirical evidence from the South African stock market
- Authors: Morahanye, Hlompho
- Date: 2019
- Subjects: Financial risk management , Johannesburg Stock Exchange , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/414319 , uj:34939
- Description: Abstract: This paper studies the association between risk and returns in the Johannesburg Stock Exchange. In particular, the study is interested in modelling this relationship during periods of high volatility with special reference to the 2007-2009 financial crises. The objective is to highlight the effect that a high volatility period might have on the relationship. To achieve this objective, daily data for the market index, JSE Top 40 and the two JSE sectoral indices for the period 1/1/2004 to 3/5/2017 are used. The GARCHM, E-GARCH-M and TARCH-M models and the same aforementioned models with dummy variables to account for two volatility regimes are used. The CAPM prediction that the expected return on a stock above the risk-free rate is positive is not supported by the study. The tests conducted to examine the relationship observed that the risk premiums were either positive but insignificant, or negative and significant, which is inconsistent with the theory. The observed outcomes indicate that the risk premium is not necessarily positive, even after accounting for different regimes. These results are generally in line with observations made by other authors who investigated the relationship within the South African context. The findings of this paper are useful in financial decision-making, such as in providing investors with information on which sectors to invest in based on their risk appetite, as well as providing information regarding the performance of the different stocks in the market in terms of risk and return. , M.Com. (Financial Economics)
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- Authors: Morahanye, Hlompho
- Date: 2019
- Subjects: Financial risk management , Johannesburg Stock Exchange , GARCH model
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/414319 , uj:34939
- Description: Abstract: This paper studies the association between risk and returns in the Johannesburg Stock Exchange. In particular, the study is interested in modelling this relationship during periods of high volatility with special reference to the 2007-2009 financial crises. The objective is to highlight the effect that a high volatility period might have on the relationship. To achieve this objective, daily data for the market index, JSE Top 40 and the two JSE sectoral indices for the period 1/1/2004 to 3/5/2017 are used. The GARCHM, E-GARCH-M and TARCH-M models and the same aforementioned models with dummy variables to account for two volatility regimes are used. The CAPM prediction that the expected return on a stock above the risk-free rate is positive is not supported by the study. The tests conducted to examine the relationship observed that the risk premiums were either positive but insignificant, or negative and significant, which is inconsistent with the theory. The observed outcomes indicate that the risk premium is not necessarily positive, even after accounting for different regimes. These results are generally in line with observations made by other authors who investigated the relationship within the South African context. The findings of this paper are useful in financial decision-making, such as in providing investors with information on which sectors to invest in based on their risk appetite, as well as providing information regarding the performance of the different stocks in the market in terms of risk and return. , M.Com. (Financial Economics)
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