3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Sources of risk in insurance markets(2020) Dladla, PholileThis thesis focuses on insurance risk management in select developed and emerging market economies, the impact of macroeconomic factors on insurance and bank risk, insurance share returns and the linkages between banks and insurances over the period 1988-2017. The thesis is divided into five chapters and three of them are empirical. Chapter 1 is the introduction. Chapters 2, 3 and 4 are empirical chapters examining the impact of macroeconomic factors on various risk indicators of insurance companies and banks. Chapter 5 concludes by highlighting the key issues and giving policy recommendations. In chapter 1, we provide a layout of the background, research problem, objectives, and contributions of the study and the methodologies of each empirical chapter. Chapter 2 examines the macroeconomic determinants of insurance risk indicators for life and non-life insurance companies, in both the developed and emerging market economies. We use a linear consumption-based and a profit maximization model to theoretically derive the determinants of risk indicators. Our derivation is based on the utility and profit function. The results show that the most influential macroeconomic variables, on insurance risks, are exchange rates, interest rates and the variable on the consumption of other goods, across all countries in both the life and non life subsectors. Chapter 3 investigates the sensitivity of the share returns of life and non-life insurance companies to macroeconomic variables. We use a single linear equation to estimate the sensitivity of insurance company returns to these variables. We extend the Dladla and Malikane (2018) and Berendes et al. (2013) models by incorporating other macroeconomic variables through the Taylor rule. The main results highlight that most macroeconomic variables have a weak effect on the share returns of insurers, with the exception of interest rates, which plays a leading role. Chapter 4 analyses the linkages between bank and insurance risk measures, the potential common drivers of these risks as well as the causal relationship between these risks. The results show that firstly, there are linkages between banks and insurance risk variables, with the most notable link being between banks and non-life insurers. Secondly, GDP, long-term interest rates and exchange rates are the common drivers of risk in these two sectors. Lastly, that bank have the most notable spillover effects on insurance, in both life and non-life, as such banks risk variables contain useful information for predicting insurance risk variables. Chapter 5 is the conclusion. We provide a summary of the k e y issues covered, the main findings and the policy recommendations. We also suggest areas of future research.Item Sources of risk in insurance markets(2021) Dladla, PholileThis thesis focuses on insurance risk management in select developed and emerging market economies, the impact of macroeconomic factors on insurance and bank risk, insurance share returns and the linkages between banks and insurances over the period 1988-2017. The thesis is divided into five chapters and three of them are empirical. Chapter 1 is the introduction. Chapters 2, 3 and 4 are empirical chapters examining the impact of macroeconomic factors on various risk indicators of insurance companies and banks. Chapter 5 concludes by highlighting the key issues and giving policy recommendations. In chapter 1, we provide a layout of the background, research problem, objectives, and contributions of the study and the methodologies of each empirical chapter. Chapter 2 examines the macroeconomic determinants of insurance risk indicators for life and non-life insurance companies, in both the developed and emerging market economies. We use a linear consumption-based and a profit maximization model to theoretically derive the determinants of risk indicators. Our derivation is based on the utility and profit function. The results show that the most influential macroeconomic variables, on insurance risks, are exchange rates, interest rates and the variable on the consumption of other goods, across all countries in both the life and non life subsectors. Chapter 3 investigates the sensitivity of the share returns of life and non-life insurance companies to macroeconomic variables. We use a single linear equation to estimate the sensitivity of insurance company returns to these variables. We extend the Dladla and Malikane (2018) and Berendes et al. (2013) models by incorporating other macroeconomic variables through the Taylor rule. The main results highlight that most macroeconomic variables have a weak effect on the share returns of insurers, with the exception of interest rates, which plays a leading role. Chapter 4 analyses the linkages between bank and insurance risk measures, the potential common drivers of these risks as well as the causal relationship between these risks. The results show that firstly, there are linkages between banks and insurance risk variables, with the most notable link being between banks and non-life insurers. Secondly, GDP, long-term interest rates and exchange rates are the common drivers of risk in these two sectors. Lastly, that bank have the most notable spillover effects on insurance, in both life and non-life, as such banks risk variables contain useful information for predicting insurance risk variables. Chapter 5 is the conclusion. We provide a summary of the k e y issues covered, the main findings and the policy recommendations. We also suggest areas of future researchItem Asset price dynamics and Taylor rule fundamentals(2015-02-03) Dladla, PholileThe purpose of this paper is to develop a forecasting equation from the dividend discount model. Our reduced form asset pricing equation features lagged dividend per share, term spread, short-term interest rates, infl ation rates, the output gap and real exective exchange rates. The results indicate that our forecasting model has significant and powerful relationships and outperforms the other models which are compared against it. We conclude that the reduced form forecasting model has merit and can influence the portfolio decisions of profit-seeking investor.