Issuers’ credit ratings change by one or more notches when credit rating agencies provide new ratings. Unique to the literature, I study the influences affecting multi notch versus single notch rating upgrades and downgrades. For Standard & Poors data, I show that rating changes with multiple notches provide more information to the market than single notch rating changes. Consistent with prior literature on the business cycle, I show that investors value good news rating changes (upgrades) more in bad times (recession) and that investors value bad news rating changes (downgrades) more in good times (expansion). I model and test probit models using variables capturing the characteristics of the previous issuer’s credit rating, liquidity, solvency, profitability, and growth opportunity to determine the classification of single notch versus multi notch rating changes. The determinants of multi notch versus single notch rating changes for upgrades and downgrades differ. Business cycle influences are evident. Firms that have multi notch rating upgrades and downgrades have significantly different probit variables vis-à-vis firms that have single notch rating upgrades and downgrades. The important characteristics for determining multiple notch upgrades are a firm’s prior rating change, prior rating, cash flow, total assets and market value. The important characteristics for determining multiple notch downgrades are a firm’s prior rating change, prior rating, current ratio, interest coverage, total debt, operating margin, market to book ratio, capital expenditure, total assets, market value, and market beta. The variables that differ for multi notch upgrades in recessions are cash flow, net income, operating margin, market to book ratio, total assets, and retained earnings. The variables that differ for multi notch downgrades in expansions are a firm’s prior rating change, current ratio, interest coverage ratio, debt ratio, total debt, capital expenditure and market beta. The power of the explanatory tests improves when the stage of the business cycle is considered. Results are robust to consideration of rating changes across rating categories, changes from probit to logit, alternative specifications of accounting variables, lags and leads of recessions and expansions timing, Fama and French industry adjustments, and winsorization levels of variables.

Kiekvieno banko viena iš pagrindinių veiklos sričių – paskolų išdavimas. Su kiekviena išduodama paskola bankas prisiima vieną svarbiausių savo veikloje rizikų – kredito riziką, kurios nuostoliai bankui gali būti labai dideli. To geriausias įrodymas – 2007 metais JAV ištikusi kredito rizikos krizė, nusidriekusi per visą pasaulį ir atnešusi milžiniškus nuostolius. Todėl kredito rizikos tikslus įvertinimas ir tinkamas valdymas yra ypatingai svarbus tiek komerciniams bankams, tiek bankus prižiūrinčioms institucijoms, kurios privalo užtikrinti stabilų finansinio sektoriaus vystymąsi. Tinkamų kredito rizikos vertinimo modelių naudojimas bankuose leidžia sumažinti kredito riziką, padidinti banko veiklos stabilumą ir patikimumą. Todėl yra aktualu išanalizuoti kredito rizikos vertinimo ir reitingavimo metodus, ir atlikus atitinkamas korekcijas pritaikyti juos Lietuvos įmonių kredito rizikai vertinti. Darbo objektas – kredito rizikos vertinimo ir kredito reitingų nustatymo modeliai. Mokslinė problema: nors kredito rizikos vertinimo ir kredito reitingų nustatymo modeliai ir metodai yra plačiai išanalizuoti ir taikomi praktikoje, tačiau visi jie yra labiau pritaikyti stambioms užsienio rinkoms, kurios reikšmingai skiriasi nuo Lietuvos rinkos, todėl egzistuoja modelio, pritaikyto konkrečiai Lietuvos rinkai, problema. Darbo tikslas – įmonių kredito rizikos vertinimo modelio, pritaikyto Lietuvos rinkai, suformulavimas. Darbą sudaro trys skyriai: teorinė, analitinė ir rezultatų. Teorinėje... [toliau žr. visą tekstą] / Issuing credits is one of the main bank’s activities. Each bank takes a credit risk by giving the credits. Credit risk is the most important risk of all and requires exceptional consideration, because potential losses caused by credit risk can be huge. If correct and accurate credit risk evaluation models are used to evaluate the credit risk, it helps to reduce the credit risk and increase the stability and reliability of the bank. That is why it is so important and topical to perform the analysis of the credit risk evaluation and credit ratings models and to make corrections for adoption these models for credit risk evaluation of Lithuanian companies. The object of this working paper – credit risk evaluation and assigning credit ratings models. The scientific problem: although credit risk evaluation and credit ratings methods and models are broadly analyzed and applied in practice, but these models are more designed for large foreign markets, which significantly differs from Lithuania’s market and because of that the problem of an adopted credit risk evaluation model for Lithuanian companies exists. The purpose of this working paper – to formulate the credit risk evaluation model adopted for Lithuanian companies. This working paper consists of three chapters: theoretical, analytical and results. In theoretical chapter risk, risk kinds, risk measurement models allowing to measure creditworthiness and assigning credit ratings models are analyzed. In analytical chapter the... [to full text]

Readjusting Historical Credit Ratings using Ordered Logistic Re-gression and Principal Component Analysis The introduction of the Basel II Accord as a regulatory document for creditrisk presented new concepts of credit risk management and credit risk mea-surements, such as enabling international banks to use internal estimates ofprobability of default (PD), exposure at default (EAD) and loss given default(LGD). These three measurements is the foundation of the regulatory capitalcalculations and are all in turn based on the bank’s internal credit ratings. Ithas hence been of increasing importance to build sound credit rating modelsthat possess the capability to provide accurate measurements of the credit riskof borrowers. These statistical models are usually based on empirical data andthe goodness-of-fit of the model is mainly depending on the quality and sta-tistical significance of the data. Therefore, one of the most important aspectsof credit rating modeling is to have a sufficient number of observations to bestatistically reliable, making the success of a rating model heavily dependenton the data collection and development state.The main purpose of this project is to, in a simple but efficient way, createa longer time series of homogeneous data by readjusting the historical creditrating data of one of Svenska Handelsbanken AB’s credit portfolios. Thisreadjustment is done by developing ordered logistic regression models thatare using independent variables consisting of macro economic data in separateways. One model uses macro economic variables compiled into principal com-ponents, generated through a Principal Component Analysis while all othermodels uses the same macro economic variables separately in different com-binations. The models will be tested to evaluate their ability to readjust theportfolio as well as their predictive capabilities. / Justering av historiska kreditbetyg med hjälp av ordinal logistiskregression och principialkomponentsanalys När Basel II implementerades introducerades även nya riktlinjer för finan-siella instituts riskhantering och beräkning av kreditrisk, så som möjlighetenför banker att använda interna beräkningar av Probability of Default (PD),Exposure at Default (EAD) och Loss Given Default (LGD), som tillsammansgrundar sig i varje låntagares sannoliket för fallissemang. Dessa tre mått ut-gör grunden för beräkningen av de kapitaltäckningskrav som banker förväntasuppfylla och baseras i sin tur på bankernas interna kreditratingsystem. Detär därmed av stor vikt för banker att bygga stabila kreditratingmodeller medkapacitet att generera pålitliga beräkningar av motparternas kreditrisk. Dessamodeller är vanligtvis baserade på empirisk data och modellens goodness-of-fit,eller passning till datat, beror till stor del på kvalitén och den statistiska sig-nifikansen hos det data som står till förfogande. Därför är en av de viktigasteaspekterna för kreditratingsmodeller att ha tillräckligt många observationeratt träna modellen på, vilket gör modellens utvecklingsskede samt mängdendata avgörande för modellens framgång.Huvudsyftet med detta projekt är att, på ett enkelt och effektivt sätt, skapaen längre, homogen tidsserie genom att justera historisk kreditratingdata i enportfölj med företagslån tillhandahållen av Svenska Handelsbanken AB. Jus-teringen görs genom att utveckla olika ordinala logistiska regressionsmodellermed beroende variabler bestående av makroekonomiska variabler, på olikasätt. En av modellerna använder makroekonomiska variabler i form av princi-palkomponenter skapade med hjälp av en principialkomponentsanalys, medande andra modelelrna använder de makroekonomiska variablerna enskilt i olikakombinationer. Modellerna testas för att utvärdera både deras förmåga attjustera portföljens historiska kreditratings samt för att göra prediktioner.

Bibliografia: p. 46-48 / Dissertação (mestrado) - Faculdade de Economia e Finanças Ibmec, Rio de Janeiro, 2014. / Devido à crescente importância dos mercados financeiros nas últimas décadas, o risco de crédito tem se tornado um tema fundamental na tomada de decisões acerca de investimentos, taxas de financiamento, solvência corporativa, tendência e perspectivas etc. Os modelos de avaliação de risco de crédito, em geral, podem ser classificados em duas categorias: quantitativo e qualitativo. Modelos quantitativos buscam analisar informações de demonstrativos financeiros e seus indicadores, enquanto modelos qualitativos focam na análise de variáveis intangíveis que afetam os negócios globais. Estes modelos normalmente seguem uma estrutura "top-down" de análise setorial, competitividade e comparação de pares e gestão. O objetivo desta dissertação é apresentar um modelo de classificação de risco setorial com base em métodos de análise multicritério que possam mensurar a importância das variáveis que afetam os setores da economia brasileira, bem como a influência entre estas. O modelo é baseado, principalmente, no método Weighted Influence Non-Linear Gauge System. Acerca dos julgamentos sobre as variáveis, o modelo baseia-se na utilização do método Analytic Hierarchy Process. O resultado do modelo é apresentado através de níveis de risco, aplicado a quatorze setores da economia brasileira. A dissertação se encerra com uma discussão sobre os resultados, bem como com um esboço do direcionamento para futuras pesquisas. / Due to the increasing importance of the financial market over the past decades, credit risk has become a paramount issue in investment, loan spreads, corporate solvency, trends and prospetcs, etc. Credit risk evaluation models may be classified in two broad categories: quantitative and qualitative. Quantitative models seek to analyze information from financial statement and indexes, while qualitative models focus on the analysis of intangible variables that affect global business. These models typically follow a top-down approach by analyzing the industry risk, competitiveness and peer comparison and management. The aim of this thesis is to present an industry risk assessment model based on multicriteria analysis methods that can measure the strengh of variables that affect the industries of Brazilian economy, as well as the influence between them. The model is based primarily on the Weighted Influence Non-Linear Gauge System method. Concerning human judgements about the variables, the model is founded on the use of the Analytic Hierarchy Process method. The result from the model is presented through risk levels, applied to fourteen industries in the Brazilian economy. The thesis closes with a discussion of results, as well as with an outline to future research directions.

Cette thèse met en avant l’analyse du lien entre le marché de la dette de gouvernement, le risque souverain, la stabilité du système financier et le développement des marchés de la dette locale dans les pays émergents. Afin de remplir l’espace vide dans la littérature académique sur les flux obligataires j’ai construit une nouvelle base des données sur les détentions des obligations souveraines par les investisseurs domestiques et étrangers et, par conséquence, j’ai effectué une analyse empirique des déterminants des flux entrants et sortants par type d’investisseur et pays. Ainsi la thèse projette une image complète de la globalisation des marchés de la dette souveraine. / This PhD dissertation presents the analysis of the link between the government debt market, sovereign risk, financial stability and development of the local currency debt in emerging economies. The reserch contribution to the academic literature lies in the empirical analysis of capital flows in bond markets and, for this purpose, I constructed a novel database covering domestic and foreign holdings of government bonds in developed and emerging economies. As a result, this disertation projects a complete and coherent image of the globalisation of sovereign bond markets.

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Lietuvoje, kaip ir daugelyje kitų Europos šalių, kuriose vyksta dinamiški ekonomikos pertvarkymo procesai, nuolat susiduriama su lėšų šiems procesams finansuoti trūkumu. Dėl objektyvių priežasčių, kai yra riboti finansavimo šaltinių ištekliai šių valstybių viduje, labai svarbią įtaką, stiprinant jų finansines sistemas, aprūpinant jas būtinomis lėšomis, turi valstybės skolinimasis užsienyje. Valstybės skolos valdymo politika neatskiriama šalies ekonomikos dalis. Kalbant apie valstybės skolą, automatiškai susiduriama su biudžeto deficitu. Susidariusį biudžeto deficitą valstybė būna priversta finansuoti skolintomis lėšomis. Visoms pereinamosios ekonomikos šalims modernizuojant ūkį, reikia didelių investicijų. Nacionalinių santaupų santykis su BVP tokiu laikotarpiu yra nedidelis, taigi valstybė neturi pakankamai savų lėšų investicijoms. Todėl tokios valstybės, tarp jų ir Lietuva, turi skolintis, ir tai yra normalus reiškinys. Valstybė kreditiniuose santykiuose dalyvauja kaip jų subjektas. Paprastai valstybė skolinasi ir garantuoja didesnėmis sumomis, negu skolina. Svarbiausia tokios padėties priežastis – per valstybinį kreditą pritrauktos lėšos pakeičia mokesčius ir kitais būdais surinktas lėšas. Tai ir yra pagrindinė susidariusios valstybės skolos priežastis. Vietiniai finansavimo šaltiniai yra tinkamiausi valstybės finansavimo šaltiniai, ypač jeigu tie šaltiniai denominuoti nacionaline valiuta. Valstybės vidaus skola yra labai svarbus rodiklis, rodantis valstybės ekonominį... [toliau žr. visą tekstą] / In Lithuania, like in other European countries where dynamic processes of economy reorganization are proceeding, there is a constant impact with lack of funds to finance these processes. Objectively, when limited resources of finance funds are in countries, lending abroad has a huge influence on strengthening their financial systems and providing them with necessary funds. Policy of national debt management is inseparable part from national economy. Taking about national debt, one constantly confronts to budget deficit. The country is forced to finance formed budget deficit with loans. All countries of transitional economy need huge investments to modernize economy. The proportion of national stockpiling and gross domestic product (GDP) at that time is not high; therefore, the country does not have enough own resources for investments. Therefore, such countries, including Lithuania, have to borrow, and that is very normal. The country in credit relations participates like their subject. Usually the country takes a loan and guarantees for bigger sums than lends. The most important reason of this condition is that though national credit raised funds change taxes and funds collected in other ways. That is the main reason of formed national debt. Local finance resources are the most suitable country’s finance resources, especially if there resources are denominated by national currency. Inland debt is a very important factor that presents the level of country’s economical... [to full text]

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.

This study examined the determinants of commercial bank liquidity in South Africa. The panel regression approach was used, applying panel data from twelve commercial banks over the period 2006 to 2016. A quantitative research method was used to investigate the relationship between bank liquidity and some microeconomic and bank-specific factors and between bank liquidity and selected macro-economic factors. The regression analysis for four liquidity ratios was conducted using the pooled ordinary least squares regression, fixed effects, random effects and the generalised methods of moments. However, the system generalised methods of moments approach was preferred over the other methods because it eliminated the problem of endogeneity. Results show that capital adequacy, size and gross domestic product have a positive and significant effect on liquidity. Loan growth and non-performing loans had a negative and significant effect on liquidity. Inflation had both a positive and a negative but an insignificant effect on liquidity. The study concluded that South African banks could enhance their liquidity positions by tightening their loan-underwriting criteria and credit policies. Banks should improve their credit risk management frameworks to be more prudent in their lending practices to improve the quality of the loan book to enhance liquidity. They also need to grow their capital levels by embarking on efficient revenue enhancements activities. Banks may also to look at their clients on an overall basis and not on transaction bases, and they need to improve non-interest revenue by introducing innovated products. The South African Reserve Bank could push for policies that might enhance capitalisation by ensuring that the sector is consolidated and thus merging smaller banks to create banks with stronger balance sheets and stronger capital base. This study contributes to the empirical research repository on the determinants of liquidity and more specifically, it identified the significant factors that affect South African commercial bank liquidity. Identifying the determinants of South African commercial bank liquidity will provide the South African Reserve Bank with insight into ways of enhancing liquidity management reforms, to improve the sector’s liquidity management practices and help to maintain a sound and liquid banking sector. / Business Management / M. Com. (Business Management)