Exchange rate risk and international equity portfolio diversification in emerging markets : a South African investor perspective
- Tchuinkam Djemo, Charles Raoul
- Authors: Tchuinkam Djemo, Charles Raoul
- Date: 2017
- Subjects: Foreign exchange rates - South Africa , Risk
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/282493 , uj:30433
- Description: Abstract: This study empirically analyses exchange rate risk in a portfolio of ten stock indices in emerging markets from the viewpoint of a South African investor. The aim of this study is to understand the effect of exchange rate risk on expected return of such a portfolio and to find out whether this investment provides benefits for South African investors. To this end, we covered ten stock markets namely Malaysia, Philippines, South Africa, Brazil, China, Russia, India, Argentina, Mexico and Singapore. We make use of Value at Risk (VaR)-based GARCH model to model extreme currency deviation. We collect daily stock prices, daily spot and forward exchange rate of the South African rand against the currencies of the above mentioned countries for the period between 1 January 2005 and 31 October 2016. Firstly we filter each market returns with an Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model to eliminate the presence of heterokedasticity and autocorrelation in the distribution of returns. We then fit the residual of the above GARCH model to the Generalized Pareto distribution (GPD) in order to account for extreme events. The estimation results of the EGARCH (1, 1) model show that all parameters are statistically significant for all stock markets including the leverage effect which arguably proves that bad news have higher impact on stock market volatility compared to good news. The parameters of the GPD in the lower tail are also estimated, the resulting shape parameters are significantly positive indicating that these stock markets are prone to price swing during periods of economic downturn. Lastly, we compute individual market risk measures using the EGARCH-EVT – based techniques and backtest them using the Kupiec method. Our VaR show that Russia has the highest market risk whereas Malaysia has the lowest VaR implies that it is the lowest market risk. The result of unconditional coverage test show that the likelihood ratio statistic fails to reject the null hypothesis of correct number of exceptions implies that our model is accurate. We find that for a South African investor to maximize his/her entire portfolio return, S/he needs to invest 9.85% in ALSI, 6.59% in SHANGHAI, 4.44% in BOVESPA, 7.46% in SENSEX, 2.28% in MICEX, 12.15% in MEXBOL, 3.9% in MERVAL, 31.4% in KLCI, 12.74% in SINGA and 9.19% in PSEI with the portfolio risk of 1.59; 0.62; 1.3; 5.76; 2.48; 1.45; 1.36; 0.24 and 2.21 respectively. We analyses the impact of exchange rate risk on the portfolio, the result show that Singaporean dollar, Russian rouble, Mexican peso and Indian rupee have positive impact on the portfolio return while the Argentine peso and Chinese yuan have negative impact on portfolio return therefore, South African investor in order to maximize his investment taking into account exchange rate risk have to put more weight in stock market with positive impact of exchange rate fluctuation on the portfolio. , M.Com. (Financial Economics)
- Full Text:
- Authors: Tchuinkam Djemo, Charles Raoul
- Date: 2017
- Subjects: Foreign exchange rates - South Africa , Risk
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/282493 , uj:30433
- Description: Abstract: This study empirically analyses exchange rate risk in a portfolio of ten stock indices in emerging markets from the viewpoint of a South African investor. The aim of this study is to understand the effect of exchange rate risk on expected return of such a portfolio and to find out whether this investment provides benefits for South African investors. To this end, we covered ten stock markets namely Malaysia, Philippines, South Africa, Brazil, China, Russia, India, Argentina, Mexico and Singapore. We make use of Value at Risk (VaR)-based GARCH model to model extreme currency deviation. We collect daily stock prices, daily spot and forward exchange rate of the South African rand against the currencies of the above mentioned countries for the period between 1 January 2005 and 31 October 2016. Firstly we filter each market returns with an Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model to eliminate the presence of heterokedasticity and autocorrelation in the distribution of returns. We then fit the residual of the above GARCH model to the Generalized Pareto distribution (GPD) in order to account for extreme events. The estimation results of the EGARCH (1, 1) model show that all parameters are statistically significant for all stock markets including the leverage effect which arguably proves that bad news have higher impact on stock market volatility compared to good news. The parameters of the GPD in the lower tail are also estimated, the resulting shape parameters are significantly positive indicating that these stock markets are prone to price swing during periods of economic downturn. Lastly, we compute individual market risk measures using the EGARCH-EVT – based techniques and backtest them using the Kupiec method. Our VaR show that Russia has the highest market risk whereas Malaysia has the lowest VaR implies that it is the lowest market risk. The result of unconditional coverage test show that the likelihood ratio statistic fails to reject the null hypothesis of correct number of exceptions implies that our model is accurate. We find that for a South African investor to maximize his/her entire portfolio return, S/he needs to invest 9.85% in ALSI, 6.59% in SHANGHAI, 4.44% in BOVESPA, 7.46% in SENSEX, 2.28% in MICEX, 12.15% in MEXBOL, 3.9% in MERVAL, 31.4% in KLCI, 12.74% in SINGA and 9.19% in PSEI with the portfolio risk of 1.59; 0.62; 1.3; 5.76; 2.48; 1.45; 1.36; 0.24 and 2.21 respectively. We analyses the impact of exchange rate risk on the portfolio, the result show that Singaporean dollar, Russian rouble, Mexican peso and Indian rupee have positive impact on the portfolio return while the Argentine peso and Chinese yuan have negative impact on portfolio return therefore, South African investor in order to maximize his investment taking into account exchange rate risk have to put more weight in stock market with positive impact of exchange rate fluctuation on the portfolio. , M.Com. (Financial Economics)
- Full Text:
Analysis of operational risk in the South African banking sector using the standardised measurement approach
- Authors: Nyathi, Mandla
- Date: 2018
- Subjects: Banks and banking - South Africa , Banks and banking - Risk management - South Africa
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/403085 , uj:33761
- Description: Abstract : Over the last decade, financial markets across the world have been devastated by operational risk-related incidents. These incidents were caused by a number of aspects, such as, inter alia, fraud, improper business practices, natural disasters, and technology failures. As new losses are incurred, they become part of each financial institution’s internal loss database. The inclusion of these losses has caused notable upward spikes in the operational risk Pillar I regulatory capital charge for financial institutions across the board. The inherent imperfections in people, processes, and systems–be it by intention or oversight–are exposures that cannot be entirely eliminated from bank operations. Thus, the South African Reserve Bank mandates South African financial institutions to reserve capital to cover their idiosyncratic operational risk exposures. Investors fund capital reserves that are held by financial institutions, and these stakeholders demand a viable return on their investment. Consequently, the risk exposure and capital held relationship should be fully understood, managed, and optimised. This thesis extends Sundmacher (2007)’s work through the use of one instance of the Standardised Measurement Approach data against that of the Advanced Measurement Approach, the Standardised Approach, and the Basic Indicator Approach to estimate the potential financial benefit that financial institutions in South Africa could attain or lose, should they move from a Basic Indicator Approach to a Standardised Approach, or from a Standardised Approach to an Advanced Measurement Approach, or from an Advanced Measurement Approach to a Standardised Measurement Approach. The Advanced Measurement Approach, a Loss Distribution Approach coupled with a Monte Carlo simulation was used. Parametric models were imposed to generate the annual loss distribution through the convolution of the annual loss severity and frequency distribution. To fit the internal loss data for each class, the mean annual number of losses was calculated and was assumed to follow a Poisson distribution. The Maximum Likelihood Estimator was used to fit four severity distributions: Lognormal;Weibull; Generalized Pareto; and Burr distributions. To determine the goodness of fit, the Kolmogorov-Smirnov Test at a 5% level of significance was used. To select the best fitting distribution, the Akaike Information Criterion was used. Robustness and stability tests where then performed, using bootstrapping and stress-testing respectively. Overall, we find that the Basel Committee on Banking Supervision’s primary consideration that postulates that there is value in a financial institution moving from the Basic Indicator Approach to the Standardised Approach, or from the Standardised Approach to the Advanced Measurement Approach is indeed valid, but fails in the movement from an Advanced Measurement Approach to a Standardised Measurement Approach. The best Pillar I Capital reprieve is offered by the Diversified Advanced Measurement Approach, whilst the second best is the Standardised Measurement Approach based on an average total loss threshold of €100k (0.87% higher than the Diversified Advanced Measurement Approach), closely followed by the default Standardised Measurement Approach based on average total loss threshold of €20k (5.63% higher than the Diversified Advanced Measurement Approach). To the best of our abilities, we could not find any work that is comprehensive enough to include all four available operational risk quantification approaches (Basic Indicator Approach, Standardised Approach, Advanced Measurement Approach, and Standardised Measurement Approach), for the South African market in particular. This work foresees South African financial institutions pushing back on the implementation of SMA, and potentially lobbying the regulator to remain in AMA – as the alternative might mean increased capital requirements leading to reduced Economic Value Added to shareholders (as more capital is required at the same level of profitability or business activity). The financial institutions are anticipated to sight advanced modelling techniques as helping management have a deeper understanding of their exposures – whilst the Scenario Analysis process allows them a method of identifying their key risks and quantifying them (adding to management’s tools set). However, if South African financial institutions want to compete at a global stage and wanted to be accepted among ‘internationally active’ institutions – their adoption of SMA may not be a choice but an obligation and an entry ticket to the game (global trade). , M.Com. (Financial Economics)
- Full Text:
- Authors: Nyathi, Mandla
- Date: 2018
- Subjects: Banks and banking - South Africa , Banks and banking - Risk management - South Africa
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/403085 , uj:33761
- Description: Abstract : Over the last decade, financial markets across the world have been devastated by operational risk-related incidents. These incidents were caused by a number of aspects, such as, inter alia, fraud, improper business practices, natural disasters, and technology failures. As new losses are incurred, they become part of each financial institution’s internal loss database. The inclusion of these losses has caused notable upward spikes in the operational risk Pillar I regulatory capital charge for financial institutions across the board. The inherent imperfections in people, processes, and systems–be it by intention or oversight–are exposures that cannot be entirely eliminated from bank operations. Thus, the South African Reserve Bank mandates South African financial institutions to reserve capital to cover their idiosyncratic operational risk exposures. Investors fund capital reserves that are held by financial institutions, and these stakeholders demand a viable return on their investment. Consequently, the risk exposure and capital held relationship should be fully understood, managed, and optimised. This thesis extends Sundmacher (2007)’s work through the use of one instance of the Standardised Measurement Approach data against that of the Advanced Measurement Approach, the Standardised Approach, and the Basic Indicator Approach to estimate the potential financial benefit that financial institutions in South Africa could attain or lose, should they move from a Basic Indicator Approach to a Standardised Approach, or from a Standardised Approach to an Advanced Measurement Approach, or from an Advanced Measurement Approach to a Standardised Measurement Approach. The Advanced Measurement Approach, a Loss Distribution Approach coupled with a Monte Carlo simulation was used. Parametric models were imposed to generate the annual loss distribution through the convolution of the annual loss severity and frequency distribution. To fit the internal loss data for each class, the mean annual number of losses was calculated and was assumed to follow a Poisson distribution. The Maximum Likelihood Estimator was used to fit four severity distributions: Lognormal;Weibull; Generalized Pareto; and Burr distributions. To determine the goodness of fit, the Kolmogorov-Smirnov Test at a 5% level of significance was used. To select the best fitting distribution, the Akaike Information Criterion was used. Robustness and stability tests where then performed, using bootstrapping and stress-testing respectively. Overall, we find that the Basel Committee on Banking Supervision’s primary consideration that postulates that there is value in a financial institution moving from the Basic Indicator Approach to the Standardised Approach, or from the Standardised Approach to the Advanced Measurement Approach is indeed valid, but fails in the movement from an Advanced Measurement Approach to a Standardised Measurement Approach. The best Pillar I Capital reprieve is offered by the Diversified Advanced Measurement Approach, whilst the second best is the Standardised Measurement Approach based on an average total loss threshold of €100k (0.87% higher than the Diversified Advanced Measurement Approach), closely followed by the default Standardised Measurement Approach based on average total loss threshold of €20k (5.63% higher than the Diversified Advanced Measurement Approach). To the best of our abilities, we could not find any work that is comprehensive enough to include all four available operational risk quantification approaches (Basic Indicator Approach, Standardised Approach, Advanced Measurement Approach, and Standardised Measurement Approach), for the South African market in particular. This work foresees South African financial institutions pushing back on the implementation of SMA, and potentially lobbying the regulator to remain in AMA – as the alternative might mean increased capital requirements leading to reduced Economic Value Added to shareholders (as more capital is required at the same level of profitability or business activity). The financial institutions are anticipated to sight advanced modelling techniques as helping management have a deeper understanding of their exposures – whilst the Scenario Analysis process allows them a method of identifying their key risks and quantifying them (adding to management’s tools set). However, if South African financial institutions want to compete at a global stage and wanted to be accepted among ‘internationally active’ institutions – their adoption of SMA may not be a choice but an obligation and an entry ticket to the game (global trade). , M.Com. (Financial Economics)
- Full Text:
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