This study investigates the association between traditional and alternative working capital measures and the returns of industrial firms listed on the Johannesburg Stock E"change. Twenty five variables for all industrial firms listed for the most recent 10 years were derived from standardised annual balance sheet data of the University of Pretoria's Bureau of Financial Analysis. Traditional liquidity ratios measuring working capital position, activity and leverage, and alternative liquidity measures, were calculated for each of the 135 participating firms for the 1 0 years. These working capital measures were tested for association with five return measures for every firm over the same period. This was done by means of a chi-square test for association, followed by stepwise multiple regression undertaken to quantify the underlying structural relationships between the return measures and the working capital measures. The results of the tests indicated that the traditional working capital leverage measures, in particular, total current liabilities divided by funds flow, and to a lesser e"tent, long-term loan capital divided by net working capital, displayed the greatest associations, and e"plained the majority of the variance in the return measures. At-test, undertaken to analyse the size effect on the working capital measures employed by the participating firms, compared firms according to total assets. The results revealed significant differences between the means of the top quartile of firms and the bottom quartile, for eight of the 13 working capital measures included in the study. A nonparametric test was applied to evaluate the sector effect on the working capital measures employed by the participating firms. The rank scores indicated significant differences in the means across the sectors for si" of the 13 working capital measures. A decrease in the working capital leverage measures of current liabilities divided by funds flow, and long-term loan capital divided by net working capital, should signal an increase in returns, and vice versa. It is recommended that financial managers consider these findings when forecasting firm returns. / Business Management / D. Com. (Business Management)

This study investigates the association between traditional and alternative working capital measures and the returns of industrial firms listed on the Johannesburg Stock E"change. Twenty five variables for all industrial firms listed for the most recent 10 years were derived from standardised annual balance sheet data of the University of Pretoria's Bureau of Financial Analysis. Traditional liquidity ratios measuring working capital position, activity and leverage, and alternative liquidity measures, were calculated for each of the 135 participating firms for the 1 0 years. These working capital measures were tested for association with five return measures for every firm over the same period. This was done by means of a chi-square test for association, followed by stepwise multiple regression undertaken to quantify the underlying structural relationships between the return measures and the working capital measures. The results of the tests indicated that the traditional working capital leverage measures, in particular, total current liabilities divided by funds flow, and to a lesser e"tent, long-term loan capital divided by net working capital, displayed the greatest associations, and e"plained the majority of the variance in the return measures. At-test, undertaken to analyse the size effect on the working capital measures employed by the participating firms, compared firms according to total assets. The results revealed significant differences between the means of the top quartile of firms and the bottom quartile, for eight of the 13 working capital measures included in the study. A nonparametric test was applied to evaluate the sector effect on the working capital measures employed by the participating firms. The rank scores indicated significant differences in the means across the sectors for si" of the 13 working capital measures. A decrease in the working capital leverage measures of current liabilities divided by funds flow, and long-term loan capital divided by net working capital, should signal an increase in returns, and vice versa. It is recommended that financial managers consider these findings when forecasting firm returns. / Business Management / D. Com. (Business Management)

Cette thèse est articulée autour de trois risques financiers que sont : la liquidité, la contagion et le risque systémique. Ces derniers sont au centre de toutes les attentions depuis la crise de 2007-08 et resteront d’actualité à la vue des évènements que rencontrent les marchés financiers. Le premier chapitre de cette thèse présente un facteur de liquidité de financement obtenu par l’interprétation d’un phénomène de contagion en termes de risque de liquidité de marché. Nous proposons dans le second chapitre, une méta-mesure de cette liquidité de marché. Cette dernière tient compte de l’ensemble des dimensions présentes dans la définition de la liquidité en s’intéressant à la dynamique de plusieurs mesures de liquidité simultanément. L’objectif du troisième chapitre est de présenter une modélisation des rendements du marché permettant la prise en compte de la liquidité de financement dans l’estimation de la DCoVaR. Ainsi, ce travail propose une nouvelle mesure du risque systémique ayant un comportement contracyclique. Pour finir, nous nous intéressons à l’hypothèse de non-linéarité de la structure de dépendance entre les rendements de marché et ceux des institutions financières. Au cœur de la mesure du risque systémique, cette hypothèse apparait contraignante puisqu’elle n’a que peu d’impact sur l’identification des firmes les plus risquées mais peut compliquer considérablement l’estimation de ces mesures. / The aim of this thesis is to improve the management of financial risks through the employment of econometric methods. We focus on liquidity (market and funding), contagion and systemic risk, which have attracted a particularly large interest in the last years of financial turmoil. Firstly, we construct a funding liquidity factor based on the contagion effects that market liquidity risks encounter. This procedure can be useful to provide a better management of the liquidity mismatch among the assets and liabilities of a fund. Secondly, we propose a meta-measure of liquidity which incorporates multiple liquidity measures through the use of a conditional correlation model. As a result, we are able to detect drastic liquidity problems by using a single measure. Thirdly, we propose a new modeling framework for financial returns by adding an extra component related to funding liquidity to the standard DCoVaR model. In this way we obtain a countercyclical measure of systemic risk. Finally, we study to which extent a change in the estimation method affects the identification of systemically relevant Financial Institutions. In particular, the most popular measures aim at capturing the nonlinearity of the dependence structure between financial firms and market returns. We show, however, that similar results can be obtained by simply assuming a linear dependence, which can also largely simplify the estimation.

This master thesis concentrates on the influence of liquidity measures on the prediction of volatility and given the magic triangle phenomena subsequently on the expected return. Liquidity measures Amihud Illiquidity, Amivest Liquidity and Roll adjusted for high frequency data have been utilized. Dataset used for the modeling was consisting of 98 shares that were traded on S&P 100. The time range was from 1st January 2013 to 31st December 2014. We have found out that the liquidity truly enters into the return-volatility relationship and influences these variables - the magic triangle interacts. However, contrary to our hypothesis, the model shows up that lower liquidity signifies lower realized risk. This inference has been suggested by all three models (3SLS, 2SLS and OLS). Furthermore, we have used the realized variance and bi-power variation to separate the jump. Our second hypothesis that lower liquidity signifies higher frequency of jumps was confirmed only for one of two liquidity proxies (Roll) included in the resulting logit FE model. Keywords liquidity, risk, volatility, expected return, magic triangle, price jumps, realized variance, bi-power variation, three-stage least squares model, logit, high-frequency data, S&P 100 Author's e-mail david.brandejs@seznam.cz Supervisor's e-mail...