3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Evaluation of market risk charge using internal model approach(2018) Malindi, Mvula MichaelThe revised valuation of capital requirement for market risk on non-securitizations in the trading book has become a critical field to model default events. The major reason for the process to take place is to reduce the variability in capital levels across banks and to address the flaws of the current measure. This paper provides an analysis of the impact of the new regulation framework on South Africa Equity Market across different sectors. The new measure is called the Expected Shortfall (CVar) which is the replacement of Value-at-Risk (Var) and Stressed Value-At-Risk (SVar) as suggested by Basel 2.5. The study is about the impact analysis of the new measure under Basel 4 against the current Basel 2.5 Var measure, the valuation of both metrics is done using Monte Carlo Simulation and Historical Simulation Approaches to evaluate which methodology is more efficient. The results are as follows. First, Value-at-Risk (Var) underestimate the potential large losses of equity benchmarks with fat-tailed properties and Expected Shortfall proves to be the robust metric to capture tail risks in the distribution. Second, observation from the findings is Expected Shortfall (CVar) can also disregard tail events, unless the metric is calibrated from the stressful period. Third, Monte Carlo Approach proves to be more efficient than Historical Simulation Approach. The methodology is also easy to calibrate and economical. Last, portfolio optimisation reduces the potential loss from both measures as a result reduces the market risk capital charge.Item The impact of shorter settlement period on risk and liquidity: the case of Johannesburg Stock Exchange(2017) Marumo, NkhahleCapital markets reforms in emerging, and particularly African markets are of a growing concern. Despite various institutional reforms that began in the early 1980s, the capital markets in emerging countries still exhibit signs of illiquidity, high volatility of returns, high concentration levels and inefficiency. Ambiguous results for such reforms have brought into question the affectivity of major capital markets reforms such as change of settlement cycles, particularly in countries where stock markets are sponsored with public funds. This thesis, therefore, intends to assess the effectiveness of capital markets reforms on development of stock markets by looking at the impact of changing settlement cycle on risk and liquidity at JSE. The objective is met through an assessment of a link between institutional structures and stock micro-structural variables, especially liquidity and risk in the literature review and an assessment of past studies on effects of stock market reforms and changes of settlement cycle on liquidity, risk and efficiency of stock markets. The study then tests the effects of settlement cycle on risk by assessing changes in abnormal returns and changes of variance of returns as a result of settlement cycle change at JSE. It also looks at the impact on liquidity by assessing the effects on the illiquidity measure first proposed by Amihund and Mendeison (2002). The study finds that change of settlement cycle at JSE had positive effects of reducing risk and increasing liquidity. The study also finds that there are no effects on trading activity and concludes that changing settlement cycle impacts largely on risk and to a smaller extend liquidity.Item Algorithmic trading, market quality and information : a dual -process account(2017) Gamzo, Rafael AlonOne of the primary challenges encountered when conducting theoretical research on the subject of algorithmic trading is the wide array of strategies employed by practitioners. Current theoretical models treat algorithmic traders as a homogenous trader group, resulting in a gap between theoretical discourse and empirical evidence on algorithmic trading practices. In order to address this, the current study introduces an organisational framework from which to conceptualise and synthesise the vast amount of algorithmic trading strategies. More precisely, using the principles of contemporary cognitive science, it is argued that the dual process paradigm - the most prevalent contemporary interpretation of the nature and function of human decision making - lends itself well to a novel taxonomy of algorithmic trading. This taxonomy serves primarily as a heuristic to inform a theoretical market microstructure model of algorithmic trading. Accordingly, this thesis presents the first unified, all-inclusive theoretical model of algorithmic trading; the overall aim of which is to determine the evolving nature of financial market quality as a consequence of this practice. In accordance with the literature on both cognitive science and algorithmic trading, this thesis espouses that there exists two distinct types of algorithmic trader; one (System 1) having fast processing characteristics, and the other (System 2) having slower, more analytic or reflective processing characteristics. Concomitantly, the current microstructure literature suggests that a trader can be superiorly informed as a result of either (1) their superior speed in accessing or exploiting information, or (2) their superior ability to more accurately forecast future variables. To date, microstructure models focus on either one aspect but not both. This common modelling assumption is also evident in theoretical models of algorithmic trading. Theoretical papers on the topic have coalesced around the idea that algorithmic traders possess a comparative advantage relative to their human counterparts. However, the literature is yet to reach consensus as to what this advantage entails, nor its subsequent effects on financial market quality. Notably, the key assumptions underlying the dual-process taxonomy of algorithmic trading suggest that two distinct informational advantages underlie algorithmic trading. The possibility then follows that System 1 algorithmic traders possess an inherent speed advantage and System 2 algorithmic traders, an inherent accuracy advantage. Inevitably, the various strategies associated with algorithmic trading correspond to their own respective system, and by implication, informational advantage. A model that incorporates both types of informational advantage is a challenging problem in the context of a microstructure model of trade. Models typically eschew this issue entirely by restricting themselves to the analysis of one type of information variable in isolation. This is done solely for the sake of tractability and simplicity (models can in theory include both variables). Thus, including both types of private information within a single microstructure model serves to enhance the novel contribution of this work. To prepare for the final theoretical model of this thesis, the present study will first conjecture and verify a benchmark model with only one type/system of algorithmic trader. More formally, iv a System 2 algorithmic trader will be introduced into Kyle’s (1985) static Bayesian Nash Equilibrium (BNE) model. The behavioral and informational characteristics of this agent emanate from the key assumptions reflected in the taxonomy. The final dual-process microstructure model, presented in the concluding chapter of this thesis, extends the benchmark model (which builds on Kyle (1985)) by introducing the System 1 algorithmic trader; thereby, incorporating both algorithmic trader systems. As said above: the benchmark model nests the Kyle (1985) model. In a limiting case of the benchmark model, where the System 2 algorithmic trader does not have access to this particular form of private information, the equilibrium reduces to the equilibrium of the static model of Kyle (1985). Likewise, in the final model, when the System 1 algorithmic trader’s information is negligible, the model collapses to the benchmark model. Interestingly, this thesis was able to determine how the strategic interplay between two differentially informed algorithmic traders impact market quality over time. The results indicate that a disparity exists between each distinctive algorithmic trading system and its relative impact on financial market quality. The unique findings of this thesis are addressed in the concluding chapter. Empirical implications of the final model will also be discussed.Item Forecasting emerging markets interest rates using optimal time-varying financial conditions index(2018) Dlamini, Lefu JonaseThis paper aims to optimise the financial conditions index (FCI) indicator that best describes the monetary policy interest rate setting behaviour of twelve emerging market central banks. This is achieved by analysing and looking at the background of modelling interest rates and forecasting interest rate setting behaviour from various regions globally. Following the credit crisis of 2008, the conventional wisdom and foundations that prevailed before were profoundly shaken. Particularly the conduct and behaviour of central banks in response to financial conditions assumed centre stage. Consequently, there has been a consensus among economists and policymakers on the importance of financial conditions, and the influence thereof, on the interest rate setting. However, in order for central banks to achieve their financial stability objectives, they need to construct an optimal indicator that best describes financial conditions. To construct such an optimal indicator, this paper firstly investigates whether the central banks of emerging markets follow the Taylor rule in setting their interest rates. Secondly, it investigates whether the FCI with optimal time-varying weights better describes interest rate movements in emerging markets, when incorporated in the Taylor rule. Lastly, it evaluates interest rate predictability by comparing various models that include non-optimized FCIs. The paper finds that the majority of emerging countries follow the Taylor rule. It also finds that most emerging markets take into account the information contained in FCIs and the majority of these countries, optimize the variables that enter the FCIs. When evaluating the forecasting accuracy of these models, the paper finds that the optimized model ranks superior in most countries in terms of forecasting accuracy. The optimization and allocation of the variables that enter the optimized FCI happen in a similar manner that was proposed by Markowitz in portfolio allocation theory.