This thesis investigates two aspects of credit risk measurement in the context of Basel 11: The International Convergence of Capital Measurement and Capital Standards. The first is the problem arising when two credit rating agencies disagree over the rating assigned to an issuer and a split rating arises. The second area is the determination of internal credit rating models for use under the Internal ratings-based approach. This thesis presents a variety of bank rating modes for individual and long term ratings across different agencies and regions. Using an extensive database of credit rating agencies with a sample of over 52,000 split ratings covering a four year period from 1999 - 2004 the first study shows that there is a ranking of agencies from the most to least generous that is stable over time. In most cases, the differences between the mean ratings of the agencies are significantly different from each other at the 1% level. The greatest differences arise between the US and Japanese agencies. When the split ratings are compared in terms of Basel II risk weights the differences between the US and Japanese agencies are still highly significant and the conclusion is that supervisors should alter the mapping of the Japanese agencies to the risk assessments under the provisions of Annex 2 to Basel II. Contrary to earlier research this study does not find that the highest level of split ratings arise for banks. The level of consensus between agencies appears to correspond to the average credit quality of the industry in question. Bank credit ratings are modelled from financial ratios and variables using ordinal logistic regression. Sample sizes exceeded 1,100 banks for the largest agencies.

This thesis focuses on one instance of housing financialisation, mortgagedebt and political subjectivation through considering the mortgaging anddemortgaging of life in the Barcelona metropolitan region from 1997 to 2014.My original contributions to knowledge are illustrating how the financialisationof housing equates to the financialisation of life; operationalising a biopoliticalreading of mortgaged homeownership and showing how politicalsubjectivation is not an act or event but an accumulation of learned practices‘from below’. A heterodox, Marxist-inspired political economic perspectiveand ethnographic engagement with (formerly) mortgaged homeowners in thehousing rights movement the Platform for Mortgage Affected People (PAH) inthe Barcelona metropolitan region are used to explore the mortgaging anddemortgaging of life. To consider the former, I connect the political economicprocesses driving the financialisation of housing during Spain’s 1997-2007housing boom to the lived experience of people unable to pay theirmortgage, facing foreclosure and eviction, in the Barcelona metropolitanregion. In other words, I weave together the macro processes and microrealities underlying the mortgaging of life. To understand the demortgaging oflife, I consider the processes of political subjectivation of mortgage-affectedpeople through their collective struggles with the PAH to get their mortgagedebt forgiven, to block evictions and to occupy empty bank-owned housing,among others. The thesis sheds light onto how life becomes a keycomponent of (urban) capital accumulation strategies, and thus thedevelopment of urban futures, and how financialised and biopoliticaltechnologies of power related to (mortgage) debt can be disrupted.

This thesis is based on three essays in corporate credit risk and economic performance analysis. Corporate bankruptcy prediction using past financial information is well established in the literature. Early studies of corporate bankruptcy prediction mainly applied statistical techniques such as discriminant analysis, logit and probit. Although, some of these models such as logit is still widely popular amongst the academics and practitioners due to its simplicity, the shortcomings of such models for bankruptcy prediction have been noticed and criticised in the literature. One of the main shortcomings is that these models as linear classification approach can not explain the possible non-linear relationship between some accounting ratios and the probability of default (PD). This issue has been addressed in the literature by introducing non-linear statistical techniques such as support vector machines (SVM). The first essay of this thesis, presented in Chapter 2, investigates the performance of SVM in corporate bankruptcy prediction and compares its performance with that of logit. This essay analyses bankruptcy risk for firms in the Asian and Pacific region using a list of financial ratios which covers different aspects of a firm's performance. The financial and credit event information for this analysis is provided by the Risk Management Institute of National University of Singapore (RMI NUS). With respect to forecasting accuracy, the findings of this analysis reveal that on average the SVM displays a higher forecasting accuracy and a more robust performance than the logit. Among several financial ratios suggested as predictors of default, leverage ratios and firm size display a higher discriminating power compared to others. Additionally, an analysis of the relationship between PD and financial ratios is provided. The accounting based models in bankruptcy analysis are mostly based on a set of measures which represents current financial position of the firms. However, these models have no indication of the status of the technology competency of a firm amongst its peers, which could be a more significant factor in the survival of a firm. Therefore, a new measure about level of firm's technological knowledge is required for a more precise assessment of firms future financial performance. Considering the rise in the technological competition and patenting activities since the 1990s and also the important role of accurate credit rating modeling in the financial stability, second essay of this thesis examined in Chapter 3 focuses on the relationship between patent applications, as an output of a firm's technological development, and financial survival. Applying a survival analysis model, this relationship is empirically tested on a longitudinal firm-level data set for the listed firms in the US which matches the patent application data from European Patent Offi ce (EPO) against a set of financial variables provided by RMI NUS. The results of this analysis reveal that patent applications are strong identifiers of low default risk companies. In a further analysis, third essay of this thesis presented in Chapter 4 focuses on the impact of patent applications on firm's economic performance. In contrast to the studies which study the overall patent portfolio indicators as proxy for innovation, on a few aspects of firm performance this essay provides a comprehensive analysis of the effect of individual patent applications on several aspects of firm performance including including profitability, leverage, liquidity, size, credit rating quality and stock return. Using the matched data set of patent application data and economic variables for the US listed firms explained earlier, this essay examines whether changing from non-patenting to patenting status when a firm files its first and subsequent applications is associated with significant changes in its firm's performance and stability. The empirical findings of this essay indicates a higher capitalisation, increased liquidity, a lower leverage and an improve in credit quality for the patenting firms.

This research investigates the role of efficiency in attracting long-term finance to the mortgage sector. Within the framework of the traditional economic theory, the new institutional theory and the theory of mortgage collateral, the study investigates the efficiency of primary mortgage banks and the perceived efficiency of the larger system within which they operate using quantitative and qualitative techniques. Quantitative data were extracted from the financials of 27 mortgage banks in Nigeria, which constitute about 90% of the size of the entire industry in Nigeria, as measured by banks’ total assets. These were analyzed using data envelopment analysis (DEA) and stochastic cost frontier (SCF) analysis to determine the efficiency of mortgage banks in Nigeria. In-depth interviews and focus group discussions were conducted among 40 CEOs of mortgage banks in Nigeria to investigate the perceived efficiency of both the banks and the entire mortgage sector. This sample constitutes about 54.2% of the CEOs in the industry and represents all geopolitical zones and ethnic groups where mortgage banks exist in the country. A review of housing finance policies, systems and sources of funds in thriving emerging economies was also conducted with the aim of drawing lessons from them that are applicable to improving the efficiency of the Nigerian mortgage sector. The findings from the review formed the basis of a mixed method questionnaire survey to investigate the existing and potential sources of funds for housing finance, to assess the acceptability and suitability of lessons drawn from other countries in Nigeria and to make policy recommendations for improving the efficiency of the Nigerian mortgage sector. The findings reveal that on average, mortgage banks in Nigeria are 33% - 49% efficient compared to best practice firms within the sector. Ownership structure and bank size influence the efficiency of these banks. Banks owned by private organizations and commercial banks are more efficient than those owned by the government or religious organizations. Banks with average total assets in excess of ₦5 Billion are more technically efficient than those with total asset less than ₦5 Billion. Practitioners perceive the mortgage banks and the larger system within which they operate as only about 10% efficient. This perceived efficiency is much lower than the technical efficiency measured in the quantitative assessment. Through the lens of institutional theory, this low rating is attributed to the negative perception of the institutional structures of the mortgage sector by mortgage finance practitioners. The findings also reveal that two categories – external and internal factors – impair the efficiency of the sector. The regulative constraints account for 55% of challenges to efficiency, normative constraints account for 24%, while cultural cognitive constraints account for 21%. The study identified accumulated deposits in pension funds, unclaimed dividends, funds in dormant accounts of commercial banks and other financial institutions, and funds from insurance companies, as possible sources of long-term funds for housing finance, while a concerted effort is being made to set up a secondary mortgage facility. The findings also reveal that effective government policies, regulation and amendment of existing laws would help improve the efficiency of the mortgage banking sector and attract investors to this sector.

No description available.

This thesis is divided into three parts; an intensity based network model of firm default, an agent based network model of firm default, and an agent based model of feedback effects from dynamic hedging. The common theme among all three parts is the application of ideas from both physics and mathematics to the solution of problems motivated by the financial markets. Less broadly, in the first two parts, the common themes are credit markets, networks, and dependent defaults. Part one tackles the problem of default dependence from a probabilistic perspective, modeling the default of companies as generalised Poisson processes, with the default dependence structure given by a network. We present a mathematical framework to solve a generalised version of the Jarrow Yu model of looping defaults [27] and study the relationship between network structure and the resilience of a network of firms to default events. Using this model we then show how to price simple multi-name credit products such as kth to default baskets. Part two again considers dependent defaults, but here the network is dynamic and firms are modelled as simple agents, defined by strategies, whose interactions determine a network of trading links. Using our agent based network model of firm default we study network structure and their degree distributions, firm lifetimes, and look for evidence of agent learning and default clustering. We then study the effect of default on a network of firms and the response of remaining firms to that default event. Part three considers a relatively more established agent based framework, called the Minority Game. We first describe in detail the Minority Game and discuss its suitability as a market model. We then show how it may be applied to modelling the actions of traders delta hedging a short option position. We show that for a variety of option positions, in a sufficiently illiquid market feedback effects arise from the actions of the traders as their trades impact upon the underlying market.

This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.

Στην παρούσα εργασία γίνεται μια εκτενής αναφορά στα εργαλεία χρηματοδότησης που παρέχουν οι εμπορικές τράπεζες στον ελληνικό χώρο, καθώς και στους τρόπους και τις μεθόδους που χρησιμοποιούνται για την αξιολόγηση και διαχείριση των χρηματοοικονομικών κινδύνων που αντιμετωπίζουν τα πιστωτικά ιδρύματα όταν παρέχουν πιστωτικές διευκολύνσεις. Επιπλέον πραγματοποιείται αναφορά στις πρόσφατες χρηματοοικονομικές κρίσεις που έπληξαν τις διεθνείς οικονομίες καθώς των συνεπειών αυτών. / This paper provides an extensive presentation of the financial tools which are provided by the Greek commercial banks. It also describes the methods which are used in order to evaluate, measure and manage financial risks which Greek banks have to deal with when they finance the markets. There is also an extensive report which present and describes the consquencies of the latest economic crises.

Η εργασία αυτή εξετάζει τη μεταβολή των επιχειρηματικών πιστώσεων από τις ελληνικές τράπεζες πριν και κατά τη διάρκεια της πρόσφατης οικονομικής κρίσης. Εντοπίζει πιστωτική συρρίκνωση από το 2009Μ9. Μελετά και αναλύει τις μεταβολές της επίδρασης ειδικών δανειακών χαρακτηριστικών στη διαμόρφωση του συνολικού επιτοκίου, τόσο για τις νέες χρηματοδοτήσεις όσο και τις υφιστάμενες. Όσον αφορά την παραγωγή νέων δανείων κατά τη διάρκεια της κρίσης, βρέθηκε οτι τα μεγάλα δάνεια τιμολογήθηκαν χαμηλότερα από τα μικρά. Τα οικονομετρικά αποτελέσματα έδειξαν τη μεγαλύτερη και θετική συνεισφορά των μικρών δανείων στον καθορισμό του επιτοκίου. Επιπλέον, βρέθηκε ότι η διαπραγματευτική δύναμη των μεγάλων δανειοληπτών, κατά τη διάρκεια της κρίσης, προκαλεί αρνητική επίδραση των μεγάλων δανείων στο συνολικό επιτόκιο. Τέλος, δείχνεται ότι το υφιστάμενο δανειακό χαρτοφυλάκιο μειώνεται σημαντικά η επίδραση των βραχυχρόνιων δανείων στο συνολικό επιτόκιο ενώ, αντίθετα, σχεδόν διπλασιάζεται η θετική επίδραση των μεσομακροπρόθεσμων δανείων. / --

Orientador : Prof. Dr. José Guilherme Silva Vieira / Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Sociais Aplicadas, Programa de Pós-Graduação em Desenvolvimento Ecônomico. Defesa : Curitiba, 15/04/2016 / Inclui referências : f. 54-55 / Resumo: Em um contexto de ampla disponibilidade de informações, a aplicação das metodológias de Data Mining provocou, nas últimas décadas, mudanças radicais nos processos de tomada de decisão em diversos campos do conhecimento, dentre eles o das finanças. Uma das beneficiárias desses avanços é a Agência de Fomento do Estado do Paraná - Fomento Paraná, instituição financeira que tem como sua principal atividade a concessão de crédito, orientado a impulsionar o desenvolvimento econômico regional. Tendo como seu acionista majoritário o Governo do Estado do Paraná, a instituição possui amplo potencial de utilização das metodologias de Data Mining, uma vez que o mesmo possui em suas bases uma grande quantidade de dados sobre as pessoas físicas e jurídicas do Paraná, sendo que uma considerável parte desses dados contém informações sensíveis ao processo decisório de crédito. Nesse contexto, através da aplicação empírica das metodologias de Data Mining, este trabalho teve como objetivo estimar um modelo estatístico básico de análise e suporte a decisão de crédito à instituição financeira de desenvolvimento Fomento Paraná, com foco na utilização de variáveis regressoras cujos valores posteriormente pudessem ser obtidos junto às demais bases de dados administrativas do Governo do Estado do Paraná. Para tanto, foram catalogadas as informações relevantes ao processo decisório de crédito com base na bibliografia acadêmica, e posteriormente foram identificadas bases de dados existentes no Estado que possuem dados e informações dessa natureza. Na sequência, com base no histórico de operações da carteira de Microcrédito da Fomento Paraná, através da aplicação da metodologia de Regressão Logística foi identificado um modelo estatístico básico de analise de crédito, que apresentou graus de Acurácia de até 82%, e que possui um conjunto de variáveis regressoras cujos valores poderão ser acessadas junto as bases de informações administrativas do Governo do Estado do Paraná. Os resultados obtidos permitem que seja estruturado um modelo inicial de análise capaz de agilizar a identificação de empresas com mérito de crédito e dar suporte a tomada de decisão, antes mesmo da instituição ser demandada pelos empreendedores, permitindo que políticas de desenvolvimento regional sejam executadas com maior precisão, agilidade e com a otimização de recursos. Palavras Chave: Análise de Crédito, Regressão Logística, Data Mining, Desenvolvimento Econômico, Bancos Públicos / Abstract: In a context of wide availability of information, where the collection and storage costs are becoming smaller, the process of analysis of credit now has a powerful set of statistical and technological tools - referred as data mining - that radically influenced the agility of decision making process. One of the beneficiaries of this process is the financial institution Agência de Fomento do Paraná - Fomento Paraná, which has as its main activity the concession of credit, aimed to boost regional economic development. However, there is an even greater potential for efficiency gains for the company since it has as its main shareholder the State of Paraná. This occurs because the State Government of Paraná, in the process of providing services to the population, must maintain a large amount of data on individuals and companies of Paraná, and a considerable part of this data contains sensitive information to the credit decision-making process. In this context, by utilizing Data Mining methods, this study aimed to estimate of a statistical model of analysis and credit decision to Fomento Paraná, focusing on the use of variables which the value could later be gathered from the databases of the State Government of Paraná. To this end, sensitive information to the credit decision-making process based on academic literature was cataloged, and existing databases that have information of this nature were later identified in the State. Further, based on the history of the Fomento Paraná Microcredit portfolio, by applying the Logistic Regression method, a basic statistical model analysis of credit that has showed 82% of Accuracy, was identified. The results allow the development of an initial analysis model that permits the identification of companies with credit merit and that can provide support decision-making process, even before the institution is asked by the entrepreneurs, enabling regional development policies to be implemented with greater accuracy, flexibility and resource optimization. Key words: Credit Analysis, Logistic Regression, Data Mining, Economic Development, Public Banking