This work focuses on the factors influencing the accessibility of credit lines for the companies in the Czech Republic. Its methodology follows the respected works written in the field of credit markets or in the field of econometrical methods suitable for the estimation of such markets. The main econometrical tool of this work is the Maximum Likelihood Estimation. Dependent variable is always the percentage change of the total volume of corporate loans and the independent variables are the percentage changes of different macroeconomic indicators. This work brings key findings important for the understanding the of the Czech corporate credit market. JEL Classification C32, C51, E40, E41, G10, G20, G21 Keywords Corporate Loans, Credit, Credit Lines, Credit Market, Credit Supply, Czech Banking Sector, Demand for Credit, Loans Author's e-mail pavelhanak@seznam.cz Supervisor's e-mail petr.gapko@seznam.cz

Credit models are essential to control credit risk and accurately predicting bankruptcy and financial distress is even more necessary after the recent global financial crisis. Although accounting and financial information have been the main variables in corporate credit models for decades, academics continue searching for new attributes to model the probability of default. This thesis investigates the use of corporate efficiency and corporate governance measures in standard statistical credit models using cross-sectional and hazard models. Relative efficiency as calculated by Data Envelopment Analysis (DEA) can be used in prediction but most previous literature that has used such variables has failed to follow the assumptions of Variable Returns to Scale and sample homogeneity and hence the efficiency may not be correctly measured. This research has built industry specific models to successfully incorporate DEA efficiency scores for different industries and it is the first to decompose overall Technical Efficiency into Pure Technical Efficiency and Scale Efficiency in the context of modelling financial distress. It has been found that efficiency measures can improve the predictive accuracy and Scale Efficiency is a more important measure of efficiency than others. Furthermore, as no literature has attempted a panel analysis of DEA scores to predict distress, this research has extended the cross sectional analysis to a survival analysis by using Malmquist DEA and discrete hazard models. Results show that dynamic efficiency scores calculated with reference to the global efficiency frontier have the best discriminant power to classify distressed and non-distressed companies. Four groups of corporate governance measures, board composition, ownership structure, management compensation and director and manager characteristics, are incorporated in the hazard models to predict financial distress. It has been found that state control, institutional ownership, salaries to independent directors, the Chair’s age, the CEO’s education, the work location of independent directors and the concurrent position of the CEO have significant associations with the risk of financial distress. The best predictive accuracy is made from the model of governance measures, financial ratios and macroeconomic variables. Policy implications are advised to the regulatory commission.

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: For many organisations who wish to sell their debt, or investors who are looking to invest in an organisation, company credit ratings are an important surrogate measure for the marketability or risk associated with a particular issue. Credit ratings are issued by a limited number of authorised companies – with the predominant being Standard & Poor’s, Moody’s and Fitch – who have the necessary experience, skills and motive to calculate an objective credit rating. In the wake of some high profile bankruptcies, there has been recent conjecture about the accuracy and reliability of current ratings. Issues relating specifically to the lack of competition in the rating market have been identified as possible causes of the poor timeliness of rating updates. Furthermore, the cost of obtaining (or updating) a rating from one of the predominant agencies has also been identified as a contributing factor. The high costs can lead to a conflict of interest where rating agencies are obliged to issue more favourable ratings to ensure continued patronage. Based on these issues, there is sufficient motive to create more cost effective alternatives to predicting corporate credit ratings. It is not the intention of these alternatives to replace the relevancy of existing rating agencies, but rather to make the information more accessible, increase competition, and hold the agencies more accountable for their ratings through better transparency. The alternative method investigated in this report is the use of a backpropagation artificial neural network to predict corporate credit ratings for companies in the manufacturing sector of the United States of America. Past research has shown that backpropagation neural networks are effective machine learning techniques for predicting credit ratings because no prior subjective or expert knowledge, or assumptions on model structure, are required to create a representative model. For the purposes of this study only public information and data is used to develop a cost effective and accessible model. The basis of the research is the assumption that all information (both quantitive and qualitative) that is required to calculate a credit rating for a company, is contained within financial data from income statements, balance sheets and cash flow statements. The premise of the assumption is that any qualitative or subjective assessment about company creditworthiness will ultimately be reflected through financial performance. The results show that a backpropagation neural network, using 10 input variables on a data set of 153 companies, can classify 75% of the ratings accurately. The results also show that including collinear inputs to the model can affect the classification accuracy and prediction variance of the model. It is also shown that latent projection techniques, such as partial least squares, can be used to reduce the dimensionality of the model without making any assumption about data relevancy. The output of these models, however, does not improve the classification accuracy achieved using selected un-correlated inputs. / AFRIKAANSE OPSOMMING: Vir baie organisasies wat skuldbriewe wil verkoop, of beleggers wat in ʼn onderneming wil belê is ʼn maatskappy kredietgradering ’n belangrike plaasvervangende maatstaf vir die bemarkbaarheid van, of die risiko geassosieer met ʼn betrokke uitgifte. Kredietgraderings word deur ʼn beperkte aantal gekeurde maatskappye uitgereik – met die belangrikste synde Standard & Poor’s, Moody’s en Fitch. Hulle het almal die nodige ervaring, kundigheid en rede om objektiewe kredietgraderings te bereken. In die nadraai van ʼn aantal hoë profiel bankrotskappe was daar onlangs gissings oor die akkuraatheid en betroubaarheid van huidige graderings. Kwessies wat spesifiek verband hou met die gebrek aan kompetisie in die graderingsmark is geïdentifiseer as ‘n moontlike oorsaak vir die swak tydigheid van gradering opdatering. Verder word die koste om ‘n gradering (of opdatering van gradering) van een van die dominante agentskappe te bekom ook geïdentifiseer as ʼn verdere bydraende faktor gesien. Die hoë koste kan tot ‘n belange konflik lei as graderingsagentskappe onder druk kom om gunstige graderings uit te reik om sodoende volhoubare klante te behou. As gevolg van hierdie kwessies is daar voldoende motivering om meer koste doeltreffende alternatiewe vir die skatting van korporatiewe kredietgraderings te ondersoek. Dit is nie die doelwit van hierdie alternatiewe om die toepaslikheid van bestaande graderingsagentskappe te vervang nie, maar eerder om die inligting meer toeganklik te maak, mededinging te verhoog en om die agentskappe meer toerekenbaar vir hul graderings te maak deur beter deursigtigheid. Die alternatiewe manier wat in hierdie verslag ondersoek word, is die gebruik van ‘n kunsmatige neurale netwerk om die kredietgraderings van vervaardigingsmaatskappye in die VSA te skat. Vorige navorsing het getoon dat neurale netwerke doeltreffende masjienleer tegnieke is om kredietgraderings te skat omdat geen voorafkennis of gesaghebbende kundigheid, of aannames oor die modelstruktuur nodig is om ‘n verteenwoordigende model te bou. Vir doeleindes van hierdie navorsingsverslag word slegs openbare inligting en data gebruik om ʼn kostedoeltreffende en toeganklike model te bou. Die grondslag van hierdie navorsing is die aanname dat alle inligting (beide kwantitatief en kwalitatief) wat benodig word om ʼn kredietgradering vir ʼn onderneming te bereken, opgesluit is in die finansiële data in die inkomstestate, balansstate en kontantvloei state. Die aanname is dus dat alle kwalitatiewe of subjektiewe assessering oor ‘n maatskappy se kredietwaardigheid uiteindelik in die finansiële prestasie sal reflekteer. Die resultate toon dat ʼn neurale netwerk met 10 toevoer veranderlikes op ‘n datastel van 153 maatskappye 75% van die graderings akkuraat klassifiseer. Die resultate toon ook dat die insluiting van kollineêre toevoere tot die model die klassifikasie akkuraatheid en die variansie van die skatting kan beïnvloed. Daar word verder getoon dat latente projeksietegnieke, soos parsiële kleinste kwadrate, die dimensies van die model kan verminder sonder om enige aannames oor data toepaslikheid te maak. Die afvoer van hierdie modelle verhoog egter nie die klassifikasie akkuraatheid wat behaal is met die gekose ongekorreleerde toevoere nie. 121 pages.

abstract: I study the importance of financial factors and real exchange rate shocks in explaining business cycle fluctuations, which have been considered important in the literature as non-technological factors in explaining business cycle fluctuations. In the first chapter, I study the implications of fluctuations in corporate credit spreads for business cycle fluctuations. Motivated by the fact that corporate credit spreads are countercyclical, I build a simple model in which difference in default probabilities on corporate debts leads to the spread in interest rates paid by firms. In the model, firms differ in the variance of the firm-level productivity, which is in turn linked to the difference in the default probability. The key mechanism is that an increase in the variance of productivity for risky firms relative to safe firms leads to reallocation of capital away from risky firms toward safe firms and decrease in aggregate output and productivity. I embed the above mechanism into an otherwise standard growth model, calibrate it and numerically solve for the equilibrium. In my benchmark case, I find that shocks to variance of productivity for risky and safe firms account for about 66% of fluctuations in output and TFP in the U.S. economy. In the second chapter, I study the importance of shocks to the price of imports relative to the price of final goods, led by the real exchange rate shocks, in accounting for fluctuations in output and TFP in the Korean economy during the Asian crisis of 1997-98. Using the Korean data, I calibrate a standard small open economy model with taxes and tariffs on imported goods, and simulate it. I find that shocks to the price of imports are an important source of fluctuations in Korea's output and TFP in the Korean crisis episode. In particular, in my benchmark case, shocks to the price of imports account for about 55% of the output deviation (from trend), one third of the TFP deviation and three quarters of the labor deviation in 1998. / Dissertation/Thesis / Ph.D. Economics 2011

[pt] O presente trabalho analisa o movimento recente nos preços de eurobonds emitidos por empresas brasileiras e a sua relação com a evolução do risco soberano brasileiro. Com o objetivo de verificar possíveis assimetrias na reação dos preços desses títulos a novas informações sobre as empresas emissoras em diferentes níveis do CDS associado à dívida soberana brasileira, testamos o comportamento dos preços dos eurobonds à divulgação de informações indicadoras dos fundamentos específicos das firmas emissoras de dívida, a saber: (i) alterações do rating atribuído por agências especializadas e (ii) anúncio do lucro por ação trimestral das empresas de capital aberto. Em linha com a literatura sobre o assunto, encontramos evidências de uma relação importante entre risco soberano e corporativo para o caso brasileiro. / [en] The present work analyzes the recent movement in the prices of Eurobonds issued by Brazilian companies and its relationship with the evolution of Brazilian sovereign risk. To identify possible asymmetries in the price reaction of these securities to new information regarding the debt issuers at different levels of the CDS associated with Brazilian sovereign debt, we tested the behavior of eurobonds prices to the disclosure of information about the specific fundamentals of issuers (i) changes in the rating attributed by specialized rating agencies and (ii) announcement of the quarterly earnings per share of publicly traded companies. In line with the literature on the subject, we find evidence of an important relationship between sovereign and corporate risk for the Brazilian case.

This dissertation proposes stress testing of a bank’s corporate credit portfolio in a Basel Internal Ratings Based (IRB) framework, using publicly available macroeconomic variables. Corporate insolvencies are used to derive a credit cycle index, which is linked to macroeconomic variables through a multiple regression model. Probability of default (PD) and loss given default (LGD) that are conditional on the worst state of the credit cycle are derived from through-the-cycle PDs and LGDs. These are then used as stressed inputs into the Basel regulatory and Economic capital calculation for credit risk. Contrary to the usual expert judgement stress testing approaches, where management apply their subjective view to stress the portfolio, this approach allows macroeconomic variables to guide the severity of selected stress testing scenarios. The result is a robust stress testing framework using Rösch and Scheule (2008) conditional LGD that is correlated to the stressed PD. The downturn LGD used here is an alternative to the widely used Federal Reserve downturn LGD which assumes no correlation between PDs and LGDs. / Dissertation (MSc)--University of Pretoria, 2014. / gm2014 / Mathematics and Applied Mathematics / Unrestricted

This dissertation comprises two essays; the first of which investigates sovereign credit risk interdependencies, while the second examines the reaction of corporate credit risk to sovereign credit risk events. The first essay titled, Characterizing Sovereign Credit Risk Interdependencies: Evidence from the Credit Default Swap Market, investigates the relationships that exist among disparate sovereign credit default swaps (CDS) and the implications on sovereign creditworthiness. We exploit emerging market sovereign CDS spreads to examine the reaction of sovereign credit risk to changes in country-specific and global financial factors. Utilizing aVAR model fitted with DCC GARCH, we find that comovements of spreads generally exhibit significant time-varying correlations, suggesting that spreads are commonly affected by global financial factors. We construct 19 country-specific commodity price indexes to instrument for country terms of trade, obtaining significant results. Our commodity price indexes account for significant variation in CDS spreads, controlling for global financial factors. In addition, sovereign spreads are found to be related to U.S. stock market returns and the VIX volatility risk premium global factors. Notwithstanding, our results suggest that terms of trade and commodity prices have a statistically and economically significant effect on the sovereign credit risk of emerging economies. Our results apply broadly to investors, financial institutions and policy makers motivated to utilize profitable factors in global portfolios. The second essay is titled, Differential Stock Market Returns and Corporate Credit Risk of Listed Firms. This essay explores the information transfer effect of shocks to sovereign credit risk as captured in the CDS and stock market returns of cross-listed and local stock exchange listed firms. Based on changes in sovereign credit ratings and outlooks, we find that widening CDS spreads of firms imply that negative credit events dominate, whereas tightening spreads indicate positive events. Grouping firms into companies with cross-listings and those without, we compare the spillover effects and find strong evidence of contagion across equity and CDS markets in both company groupings. Our findings suggest that the sensitivity of corporate CDS prices to sovereign credit events is significantly larger for non-cross-listed firms. Possible reasons for this finding could in fact be due to cross-listed firms’ better access to external capital and less degree of asymmetric information, relative to non-cross-listed peers with lower level of investor recognition. Our results provide new evidence relevant to investors and financial institutions in determining sovereign credit risk germane to corporate financial risk, for the construction of debt and equity portfolios, and hedging considerations in today’s dynamic environment.

Les normes IFRS, de part leur affiliation directe au modèle comptable anglo-saxon, soulèvent la question de la pertinence de leur mise en application au sein de l’Europe Continentale.Cette recherche tente d’éclairer cette question, en présentant, à partir d’une étude de cas de l’activité de cotation crédit Banque de France (BdF), les incidences du passage aux IFRS sur leur méthodologie d’analyse financière et sur le diagnostic crédit des groupes français.Cette recherche fait état d’un effet favorable des normes IFRS sur le diagnostic du risque de crédit des groupes français.Cet effet favorable passe aussi bien par un gain informatif des normes IFRS que par l’adaptation sélective et prudentielle de la méthodologie d’analyse financière BdF aux conventions IFRS. / IFRS, by their direct affiliation to the Anglo-Saxon accounting model, raise the question of their relevance to the Continental Europe context.This study aims to shed light on this question by highlighting, from a practical case study of the French Central Bank credit rating system, the implications of IFRS adoption in France for the methodology of financial statement analysis and credit rating of French groups.Results show a positive effect of new standards regarding the credit risk of non financial groups. They point out also that this effect can be equally attributed to the gain of transparency occasioned by the IFRS but also to the selective and conservative approach of the French Central Bank Credit Methodology.