3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Financial modeling and risk measurement on an emerging market using Lévy processes(2018) Chipoyera, Charlene Tariro.Efficient financial risk management is fundamental to good business decision making. Risk management heavily relies on the use of mathematical techniques to measure risk; hence it is important to use accurate models when measuring risk. The movement from using models based on geometric Brownian motion is due to its inability to capture many stylized facts of asset returns. Some of the well-known shortcomings of using Brownian motion when modeling asset returns that can be addressed by using Lévy processes include jumps, skewness and heavy tails. This thesis focuses on generalized hyperbolic Lévy processes to model asset returns. The two representations of the generalized hyperbolic distribution (GHD) considered in this thesis are the normal mean variance mixture introduced by McNeil et al. (2005) and the subordinated Brownian motion representation. The results presented in this thesis argue the case for using GHD models to model intraday data to using the normal distribution. The goodness of fit tests performed showed that there were no significant differences between the performance of the two representations of the generalized hyperbolic distribution. Risk measures based on the GHD and normal distribution are defined and evaluated. The results show that the GHD risk measures perform remarkably better than the Gaussian risk measures.Item Investor sentiment as a factor in an APT model: an international perspective using the FEARS index(2017) Solanki, Kamini NarendaTraditional finance theory surrounding the risk-return relationship is underpinned by the CAPM which posits that a single risk factor, specifically market risk, is priced into asset returns. Even though it is a popular asset pricing model, the CAPM has been widely criticised due to its unrealistic assumptions and the APT was developed to address the CAPM’s weaknesses. The APT framework allows for a multitude of risk factors to be priced into asset returns; implying that it can be used to model returns using either macroeconomic or microeconomic factors. As such, the APT allows for non-traditional factors, such as investor sentiment, to be included. A macroeconomic APT framework was developed for nine countries using the variables outlined by Chen, Roll, and Ross (1986) and investor sentiment was measured by the FEARS index (Da, Engelberg, & Gao, 2015). Regression testing was used to determine whether FEARS is a statistically significant explanatory variable in the APT model for each country. The results show that investor sentiment is a statistically significant explanatory variable for market returns in five out of the nine countries examined. These results add to the existing APT literature as they show that investor sentiment has a significant explanatory role in explaining asset prices and their associated returns. The international nature of this study allows it to be extended by considering the role that volatility spill-over or the contagion effect would have on each model.Item Macroeconomic risks and REITs : a comparative analysis(2016) Kola, Katlego VioletPurpose - The paper provides an investigation of the relationship of macroeconomic risk factors and REITs. The study considers the conditional volatilities of macroeconomic variables on the excess returns and conditional variance of excess returns in developing and developed markets and provides a comparison thereof. Methodology approach - The study employs three-step approach estimation in the methodology (Principal Component Analysis, GARCH (1,1) and GMM) to estimate the asset pricing model. The preliminary study indicated that there are only two developing economies (Bulgaria and South Africa), as defined by National Association of Real Estate Investment Trust (NAREIT), with REIT indices. We additionally included the United States as the developed economy. Findings – Our results indicate that the real economy and business cycles (proxied by GDP growth rate and industrial production index), price stability (proxied by the GDP deflator), exchange rates and interest rates do not explain developing country REIT returns represented by Bulgaria and South Africa, as well as in developed markets, represented by the US. However unlike the developing markets, changes in industrial production and inflation are important variables that affect the conditional variance of REIT returns in the US.Item Investigating emerging market economies Reverse REIT-Bond Yield Gap anomalies: a case for tactical asset allocation under the multivariate Markov regime switching model(2017) Videlefsky, Daryn MichaelThis paper presents a first time application of a variant of the concepts underpinning the Fed Model, amalgamated with the Bond-Stock Earnings Yield Differential, by applying it to the dividend yields of REIT indices. This modification is termed the yield gap, quantitatively constructed and adapted in this paper as the Reverse REIT-Bond Yield Gap. This metric is then used as the variable of interest in a multivariate Markov regime switching model framework, along with a set of three regressors. The REIT indices trailing dividend yield and associated metrics are the FTSE/EPRA NAREIT series. All data are from Bloomberg Terminals. This paper examines 11 markets, of which the EMEs are classified as Brazil, Mexico, Turkey and South Africa, whereas the advanced market counterparts are Australia, France, Japan, the Netherlands, Singapore, the United Kingdom, and the United States. The time-frame spans the period June 2013 until November 2015 for the EMEs, whilst their advanced market counterparts time-span covers the period November 2009 until November 2015. This paper encompasses a tri-fold research objective, and aims to accomplish them in a scientifically-based, objective and coherent fashion. Specifically, the purpose is in an attempt to gauge the reasons underlying EMEs observed anomalies entailing reverse REIT-Bond yield gaps, whereby their tenyear nominal government bonds out-yield their trailing dividend yields on their associated REIT indices; what drives fluctuations in this metric; and whether or not profitable tactical asset allocation strategies can be formulated to exploit any arbitrage mispricing opportunities. The Markov models were unable to generate clear-cut, definitive reasons regarding why EMEs experience this anomaly. Objectives two and three were achieved, except for France and Mexico. The third objective was also met. The REIT-Bond Yield Gaps static conditions have high probabilities of continuing in the same direction and magnitude into the future. In retrospection, the results suggest that by positioning an investment strategy, taking cognisance of the chain of economic events that are likely to occur following static REIT-Bond Yield Gaps, then investors, portfolio rebalancing and risk management techniques, hedging, targeted, tactical and strategic asset allocation strategies could be formulated to exploit any potential arbitrage profits. The REIT-Bond Yield Gaps are considered highly contentious, yet encompasses the potential for significant reward. The Fed Model insinuates that EME REIT markets are overvalued relative to their respective government bonds, whereas their advanced market counterparts exhibit the opposite phenomenon.Item An ICA-GARCH approach to computing portfolio VAR with applications to South African financial markets(2017) Mombeyarara, VictorThe Value-at-Risk (VaR) measurement – which is a single summary, distribution independent statistical measure of losses arising as a result of market movements – has become the market standard for measuring downside risk. There are some diverse ways to computing VaR and with this diversity comes the problem of determining which methods accurately measure and forecast Value-at-Risk. The problem is two-fold. First, what is the distribution of returns for the underlying asset? When dealing with linear financial instruments – where the relationship between the return on the financial asset and the return on the underlying is linear– we can assume normality of returns. This assumption becomes problematic for non-linear financial instruments such as options. Secondly, there are different methods of measuring the volatility of the underlying asset. These range from the univariate GARCH to the multivariate GARCH models. Recent studies have introduced the Independent Component Analysis (ICA) GARCH methodology which is aimed at computational efficiency for the multivariate GARCH methodologies. In our study, we focus on non-linear financial instruments and contribute to the body of knowledge by determining the optimal combination for the measure for volatility of the underlying (univariate-GARCH, EWMA, ICA-GARCH) and the distributional assumption of returns for the financial instrument (assumption of normality, the Johnson translation system). We use back-testing and out-of-sample tests to validate the performance of each of these combinations which give rise to six different methods for value-at-risk computations.Item Statistical arbitrage on the FTSE/JSE TOP 40 index(2017) Ngcobo-Koyana, Mandlenkosi SvatoThe mid 2000’s saw the materialization of research into the financial engineering field of high frequency trading. It is arguable that the most prominent model to emerge from the research has been pairs trading. This idea can be extended to allow for more than two assets in a modelling method now known as statistical arbitrage. The research identifies a collection of assets with a deterministic component; it then follows a multiple linear regression to exploit persistent mispricings among these assets. Further, multiple linear regression metrics are used to identify the analytic form of the trading rule and to validate the performance of the model. The first part of model constructs combinations of assets which contain a significant predictable component by co-integration, the second part builds a predictive models for the dynamics of the mispricing using statistical model. The success of the model is demonstrated with reference to a statistical analysis of 5-minute closing prices on the Johannesburg Stock Exchange (JSE) TOP40 Index and the constituent shares of the JSE TOP40 Index.Item Oil price shocks, oil and the stock market volatility relationship of Africa's emerging and frontier markets(2017) Molepo, MakgalemeleThe study examined the relationship between oil price shocks, volatilities and stock indices in the African emerging markets. The ARDL and Bivariate BEKK GARCH models are used in this study. The countries examined are Botswana, Egypt, Mauritius, Morocco, Namibia, Nigeria, South Africa, Tanzania, Kenya, Ghana, Tunisia, and the MSCI’s World Index. The study shows a bidirectional relationship between oil price shocks for Nigeria and the MSCI, but unidirectional flow from oil price shocks to Botswana, Egypt, Mauritius, Morocco, Namibia, South Africa, Tanzania, Kenya, Ghana, and Tunisia. In addition, there is evidence of unidirectional volatility spill over from oil returns to Botswana, Namibia, Tanzania, Mauritius and Kenyan, Nigeria, Tanzania, Kenya and Ghana. Finally, the study found bidirectional volatility between oil and index returns in MSCI, South Africa, and Tunisia.Item The volatility factor and the performance of South African hedge funds(2017) Momoza, BongiweThe study focuses on determining the driving factors of the performance of different hedge fund strategies in the South African industry. This is done through the application of an augmented capital asset pricing model. The model is predicated on the original (Sharpe, 1964) and (Lintner, 1965) Capital Asset Pricing Model. The researcher uses the excess market returns and the South African Volatility index as independent variables in the explanation of hedge fund returns at strategy and portfolio level. Through the analysis, the researcher finds that the excess market returns and the South African Volatility Index characterize the hedge fund expected returns for some of the strategies using OLS and GMM techniques. The second section uses a system of seemingly unrelated regressions for both the OLS and GMM techniques to determine if the two explanatory variables are priced into the different strategies; this indeed is shown to be the case for some of the strategies examined in the analysis.Item Quantitative easing in developed countries and middle income countries' financial markets(2017) Ntuli, ThuthukaThis study examines Quantitative Easing policy programs of developed countries and their potential impact on Middle Income Countries through capital inflows. The study specifically focuses on the United States and European Union Quantitative Easing programs and investigates potential effects through the various transmission channels. An Autoregressive Multifactor MIDAS approach is used to carry out the empirical analysis and the study finds that lagged capital inflows are highly significant across the different models run and that there is evidence of transmission of quantitative easing to capital inflows to Middle Income Countries along the portfolio rebalancing and liquidity channels.Item Individualism as a driver of overconfidence, and its effect on industry level returns and volatility across multiple countries(2016) Horne, ChadThis study attempts to determine the possible effects of individualism on industry volatility. The implications of this for behavioural finance are extensive, showing firstly that different industries react differently to behavioural biases and secondly that overconfidence is a possible driver of the positive effect of individualism on industry volatility. The country selection process was relatively objective, taking two countries with high individualism indexes and two with low indexes and including one with a medium index value. The result was a sample of the United States of America, the United Kingdom, South Africa, China and Taiwan. The industry selection process was more subjective. Industries were selected which should have a higher propensity to behavioural biases with lower book to market ratios (software and computer services industry and pharmaceutical and biotechnology industry) and other industries which should not be as strongly affected by behavioural biases (banks, mining, oil and gas producers, and mobile telecommunications industries). In order to correct for ARCH effects the series’ were modelled using a GARCH (1, 1) model. The resulting residuals, which showed no autocorrelation, were then used to conduct panel data regressions on each of the industries. The results confirmed that individualism had a positive effect on volatility in the industries which were expected (software and computer services and pharmaceuticals and biotechnology industries). However, it was also determined that the banks industry was significantly affected by individualism, an effect which it was hypothesised, was due to the individualism of employees as opposed to investors.