This thesis investigates the key determinants of the Non-performing loans (NPLs) comparing two groups of countries from Southeastern and Western Europe, with two different levels of economic development. We try to find empirical evidence and estimate whether the determinants of NPL ratio are different for the middle and high income countries. Applying panel data models for 14 countries overall, and using the regressions of subsampled countries, we analyze the importance of the determinants at the aggregate level. The final results show that all variables considered are significant, except inflation rate under all specifications and FDI when the subsampled dummy variables are used. As for the specifications of the exchange rate determinant, we conclude that the NPL ratio is negatively and significantly influenced in the export dominant middle income economies. An additional non-economic variable, such as the educational index, constructed at the national level, is found to increase the NPL ratio. Concerning the institutional quality index, averaging all six institutional indicators, this determinant does not show a consistent result across different data sample specifications.

Doctor of Philosophy / Department of Economics / Lance J. Bachmeier / This dissertation investigates the long run and short run relationships between Saudi stock market returns and eight macroeconomic variables. We investigate the ability of these variables to predict the level and volatility of Saudi stock market returns. A wide range of Vector autoregression (VAR) and generalized autoregressive conditional heteroskedasticity (GARCH) models estimated and interpreted. A Johansen-Juselius cointegration test indicates a positive long run relationship between the Saudi stock price index and the M2 money supply, bank credit, and the price of oil, and a negative long run relationship with the M1 money supply, the short term interest rate, inflation, and the U.S. stock market. An estimated vector error correction model (VECM) suggests significant unidirectional short run causal relationships between Saudi stock market returns and the money supply and inflation. The VECM also finds a significant long run causal relationship among the macroeconomic variables in the system. The estimated speed of adjustment indicates that the Saudi stock market converges to the equilibrium within half a year. Granger causality tests show no causal relationship between Saudi stock market returns and the exchange rate. Impulse response function analysis shows no significant relationship between Saudi stock market returns and the macroeconomic variables. Forecast error variance decompositions suggest that 89% of the variation in Saudi stock market returns is attributable to its own shock, which implies that Saudi stock market returns are largely independent of the macroeconomic variables in the system. Finally, a GARCH-X model indicates a significant relationship between volatility of Saudi stock returns and short run movements of macroeconomic variables. Implications of this study include the following. (i) Prediction of stock market returns becomes more difficult as the volatility of the macroeconomic variables increases in the short run. (ii) Investors should look at the systematic risks revealed by these macroeconomic variables when structuring their portfolios and diversification strategies. (iii) Policymakers should seek to minimize macroeconomic fluctuations considering the effect of macroeconomic variables changes on the stock market when formulating economic policy.

nÃo hà / O presente trabalho tem como objetivo principal realizar um estudo sobre o spread bancÃrio no Brasil durante o perÃodo de 2011 a 2014, analisando os seus principais determinantes e construindo um modelo de previsÃo. Foi utilizado o modelo economÃtrico dos MÃnimos Quadrados OrdinÃrios (MQO) e, adicionalmente, foi feito um modelo de previsÃo do spread. A base de dados utilizada foi extraÃda do Banco Central do Brasil (BCB): spread mÃdio das operaÃÃes de crÃdito, InadimplÃncia da carteira de crÃdito (pessoa fÃsica e jurÃdica), recolhimentos obrigatÃrios de instituiÃÃes financeiras e o endividamento das famÃlias com o Sistema Financeiro Nacional. Dentre outros resultados obtidos, verificou-se que caso ocorra um aumento de 1% da inadimplÃncia do crÃdito de pessoas fÃsicas haverà um aumento de 0,38% do Spread mÃdio no Brasil. Por outro lado, uma elevaÃÃo de 1% da inadimplÃncia de crÃdito de pessoa jurÃdica elevarà o spread mÃdio em 0,63%. Sem dÃvida, isso mostra a importÃncia dessas duas variÃveis na determinaÃÃo do spread mÃdio pelos bancos brasileiros. Por outro lado, o modelo de previsÃo utilizado permitiu concluir que do perÃodo de dezembro de 2014 a junho de 2015 o Spread mÃdio serà de aproximadamente 13% em Janeiro; 12,66% em Fevereiro; 12,63% em marÃo; 12,74% em Abril e 12,87% em Maio e 13,14% em junho de 2015. / This paper aims to conduct a study on the banking spread in Brazil during the period from 2011 to 2014, analyzing its main determinants and building a predictive model. We used the econometric model of Ordinary Least Squares (OLS) and additionally was done a spread prediction model. The database used was taken from the Central Bank of Brazil (BCB): average spread of loans, Bad debt loan portfolio (individual and corporate), reserve requirements of financial institutions and the household debt to the National Financial System. Among other results, it was found that if an increase of 1% of physical persons of credit default there will be an increase of 0.38% of the average spread in Brazil. On the other hand, an increase of 1% of corporate credit default raise the average spread 0.63%. No doubt, this shows the importance of these two variables in determining the average spread for Brazilian banks. On the other hand the forecasting model concluded that the period December 2014 to June 2015, it was found that during this period the average Spread will be approximately 13% in January; 12.66% in February; 12.63% in March; 12.74% in April and 12.87% in May to 13.14% in June 2015.

In connection to the process of financialization of commodity markets which is caused by the sharp increase of money flowing into the commodity markets, the question of which factors affect commodity and commodity indices prices is discussed. The aim of the dissertation is to determine and quantify the factors affecting the prices of industrial metals during the period of financialization of commodity markets and derive the pricing model of industrial metals, which would be able to generate signals of a possible overvaluation or undervaluation. The paper examined non-ferrous industrial metals traded on the Commodity Exchange LME (London Metal Exchange), namely aluminum, copper, lead, nickel, tin and zinc. These metals are also included in the most of the world's composite commodity indices. The dissertation analyzes the contemporary developments in commodity markets; relationship between the price volatility and fundamental factors (including production, consumption and stocks of chosen metals and a wide range of macroeconomic determinants) or the relationship between risk and return of industrial metals. The closing part of the dissertation focuses on the creating of composite pricing indicator for copper and tin by using the Boosted Trees method. The results obtained in the research show that created indicator is able to explain the volatility of the 3m copper futures contracts by 94.25% and 3m futures contracts of tin by 96, 79% in the period from 1/2000 to 3/2015.

This study examined the relationship between a set of macroeconomic variables and stock market development in Hong Kong and the Philippines for the periods of 1992Q4-2016Q3 and 2001Q4-2016Q4 respectively. In recent decades, the stock markets in Hong Kong and the Philippines have experienced remarkable growth. While the literature has produced diverse views on the relationship between each determinant and the stock market, there are no relevant studies on the determinants of stock market development on these two countries. Against this background, this study enriched the literature by investigating the macroeconomic determinants of stock market development in these two countries using the autoregressive distributed lag bounds testing approach. The empirical results of this study revealed a number of interesting findings. In the case of Hong Kong, the results showed that banking sector development and economic growth exerted positive impacts, whereas the inflation rate and exchange rate exerted negative impacts on stock market development both in the long and short run. In addition, the results showed that trade openness had a positive long-run impact, but a negative short-run impact on stock market development. Therefore, policymakers should pursue policies that foster banking sector development, enhance economic growth and maintain trade openness in order to foster the development of the stock market. In addition, monetary authority should strive to maintain a low level of inflation rate and the value of the domestic currency so as to further promote stock market development. In the case of the Philippines, the study found that trade openness had a negative impact on the development of the stock market in the long run, whereas banking sector development, and the exchange rate had positive impacts in the short run. Based on these findings, policymakers should consider policies that promote the use of equity financing in the production of main exports, enhance banking sector development, and maintain the stability of the domestic currency in order to promote the development of the stock market. / Economics / D. Phil. (Economics)

This study empirically investigates the key macroeconomic determinants of economic growth in three Southern African Development Community countries, namely: Malawi, Zambia, and South Africa, using annual data for the period 1970-2013. The study uses the recently developed Autoregressive Distributed Lag bounds-testing approach to co-integration and error correction model. In Malawi, the study finds that investment, human capital development, and international trade are positively associated, while inflation is negatively associated with economic growth in the short run. In the long run, the results reveal that investment, human capital development, and international trade are positively and significantly associated, while population growth and inflation are negatively and significantly associated with economic growth. In Zambia, the short-run results reveal that investment and human capital development are positively and significantly associated, while government consumption, international trade, and foreign aid are negatively and significantly associated with economic growth. The long-run results reveal that investment and human capital development are positively and significantly associated, while foreign aid is negatively and significantly associated with economic growth. In South Africa, the study results show that in the short run, investment is positively and significantly associated, while population growth and government consumption are negatively and significantly associated with economic growth. In the long run, the results reveal that economic growth is positively and significantly associated with investment, human capital development, and international trade, but negatively and significantly associated with population growth, government consumption, and inflation. These results all have significant policy implications. It is recommended that Malawian authorities should focus on strategies that attract investment: in addition there is a need to improve the quality of education, encourage export diversification, reduce population growth, and ensure inflation stability. Similarly Zambian authorities should focus on creation of incentives that attract investment, provision of quality education: moreover they need to improve government effectiveness, encourage international trade and ensure the effectiveness of development aid. South African authorities are recommended to focus on policies that attract investments, the provision of quality education, and trade liberalisation: concomitantly there is also a need to reduce population growth, government consumption and inflation. / Economics / Ph.D. (Economics)

La récente crise financière qui a débuté aux États-Unis en 2007 a révélé les faiblesses du système bancaire international se traduisant par l’effondrement de nombreuses institutions financières aux États-Unis et aussi par l’augmentation de la part des prêts non performants dans les bilans des banques européennes. Dans ce cadre, nous proposons d’abord d’estimer et de tester l’efficacité des modèles de prévisions des défaillances bancaires. L’objectif étant d’établir un système d’alerte précoce (EWS) de difficultés bancaires basées sur des variables financières selon la typologie CAMEL (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). Dans la première étude, nous avons comparé la classification et la prédiction de l’analyse discriminante canonique (CDA) et de la régression logistique (LR) avec et sans coûts de classification en combinant ces deux modèles paramétriques avec le modèle descriptif d’analyse en composantes principales (ACP). Les résultats montrent que les modèles (LR et CDA) peuvent prédire la faillite des banques avec précision. De plus, les résultats de l’ACP montrent l’importance de la qualité des actifs, de l’adéquation des fonds propres et de la liquidité en tant qu’indicateurs des conditions financières de la banque. Nous avons aussi comparé la performance de deux méthodes non paramétriques, les arbres de classification et de régression (CART) et le nouveau modèle régression multivariée par spline adaptative (MARS), dans la prévision de la défaillance. Un modèle hybride associant ’K-means clustering’ et MARS est également testé. Nous cherchons à modéliser la relation entre dix variables financières et le défaut d’une banque américaine. L’approche comparative a mis en évidence la suprématie du modèle hybride en termes de classification. De plus, les résultats ont montré que les variables d’adéquation du capital sont les plus importantes pour la prévision de la faillite d’une banque. Enfin, nous avons étudié les facteurs déterminants des prêts non performants des banques de l’Union Européenne durant la période 2012-2015 en estimant un modèle à effets fixe sur données de panel. Selon la disponibilité des données nous avons choisi un ensemble de variables qui se réfèrent à la situation macroéconomique du pays de la banque et d’autres variables propres à chaque banque. Les résultats ont prouvé que la dette publique, les provisions pour pertes sur prêts, la marge nette d’intérêt et la rentabilité des capitaux propres affectent positivement les prêts non performants, par contre la taille de la banque et l’adéquation du capital (EQTA et CAR) ont un impact négatif sur les créances douteuses. / The recent financial crisis that began in the United States in 2007 revealed the weaknesses of the international banking system resulting in the collapse of many financial institutions in the United States and also the increase in the share of non-performing loans in the balance sheets of European banks. In this framework, we first propose to estimate and test the effectiveness of banking default forecasting models. The objective is to establish an early warning system (EWS) of banking difficulties based on financial variables according to CAMEL’s ratios (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). In the first study, we compared the classification and the prediction of the canonical discriminant analysis (CDA) and the logistic regression (LR) with and without classification costs by combining these two parametric models with the descriptive model of principal components analysis (PCA). The results show that the LR and the CDA can predict bank failure accurately. In addition, the results of the PCA show the importance of asset quality, capital adequacy and liquidity as indicators of the bank’s financial conditions. We also compared the performance of two non-parametric methods, the classification and regression trees (CART) and the newly multivariate adaptive regression splines (MARS) models, in the prediction of failure. A hybrid model combining ’K-means clustering’ and MARS is also tested. We seek to model the relationship between ten financial variables (CAMEL’s ratios) and the default of a US bank. The comparative approach has highlighted the supremacy of the hybrid model in terms of classification. In addition, the results showed that the capital adequacy variables are the most important for predicting the bankruptcy of a bank. Finally, we studied the determinants of non-performing loans from European Union banks during the period 2012-2015 by estimating a fixed effects model on panel data. Depending on the availability of data we have chosen a set of variables that refer to the macroeconomic situation of the country of the bank and other variables specific to each bank. The results showed that public debt, loan loss provisions, net interest margin and return on equity positively affect non performing loans, while the size of the bank and the adequacy of capital (EQTA and CAR) have a negative impact on bad debts.