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Agricultural leases issued to forestry, dairy and cash crops operators from 1980-1992 are reviewed to determine factors statistically significant in predicting risk level (probability of default and/or probability of late payment) of the lessee for each industry. From a previous study of Telmark, 1990, literature review and the Recommendations of the Farm Financial Standards Task Force financial, operator/lessee and farmer/operator variables are selected for analysis. Data obtained from Telmark,Inc. are used to develop a model to explain lease risk level of the forestry, dairy, and crops industries. Results show that for forestry the following financial, lessee/operator, and farmer/operator variables are useful in determining riskiness: operating expense to revenue, cash flow coverage, capital turnover, years in business, gross revenue, and owner's equity. The dairy results indicate that the following variables are important: current ratio, cash flow coverage, return on assets, capital turnover, operating expense to revenue, FHA loan secured, owner's equity, and gross revenue. The crop results indicate percent equity, current ratio, cash flow coverage ratio, return on assets, capital turnover, operating expense to revenue, interest to income, real estate owned, years in business, FHA loan-secured, and owner's equity are significant variables for determining lease risk. Using the results from these models, a weighted average cost of misclassifying a lease is calculated. This is used to develop a profit maximizing criterion for determining whether a lease is high or low risk. The need for future work is discussed. In the area of weighted average cost of misclassifying a lease, additional information on the costs of leasing and riskiness of the population would aid in reducing the misclassified leases in the portfolio. Further study exploring some of the unexpected results in this study would be beneficial to both the lessee and the lessor. / Master of Science

The financial crisis of 2007-2008 was a severe global crisis causing a worldwide recession. One of the main contributing factors of the crisis was the excessive risk appetite of banks and financial institutions. Since then, regulatory authorities and financial institutions have directed focus towards risk management with the main objective to avert a similar crisis from occurring in the future. The aim of this thesis is to investigate how an adverse macroeconomic scenario would affect the migrations between risk classes of an SME portfolio, referred to as stress test. This thesis utilises two frameworks, one by Belkin and Suchower and one by Carlehed and Petrov, for creating a single systematic indicator describing the credit class migrations of the portfolio. Four different regression model setups (Ordinary Least Squares, Additive Model, XGBoost and SVM) are then used to describe the relationship between macroeconomic indicators and this systematic indicator. The four models are evaluated in terms of interpretability and ability to predict in order to find the main drivers for the systematic indicator. Their corresponding prediction errors are compared to find the best model. The portfolio is stress tested by using the regression models to predict the corresponding systematic indicator given an adverse macroeconomic scenario. The probability of default, estimated from the indicator using each of the frameworks, are then compared and analysed with regards to the systematic indicator. The results show that unemployment is the main driver of the risk class migrations for an SME portfolio, both from a statistical and economical perspective. The most appropriate regression model is the additive model because of its performance and interpretability and is therefore advised to use for this problem. From the PD estimations, it is concluded that the framework by Belkin and Suchower gives a more volatile estimate than that of Carlehed and Petrov.

Yes / Following the global financial crisis, the measurement of counterparty credit risk has become an essential part of the Basel III accord with credit value adjustment being one of the most prominent components of this concept. In this study, we extend the Merton structural credit risk model for counterparty credit risk calculation in the context of calculating the credit value adjustment mainly by estimating the probability of default. We improve the Merton model in a variance-convoluted-gamma environment to include default dependence between counterparties through a linear factor decomposition framework. This allows one to tackle dependence through a systematic common component. Our set-up allows for easier, faster and more accurate fitting for the credit spread. Results confirm that use of the variance-gamma-convolution clearly solves the vanishing credit spread problem for short time-to-maturity or low leverage cases compared to a Brownian motion environment and its modifications. / Ahmet Sensoy gratefully acknowledges support from Turkish Academy of Sciences under its Outstanding Young Scientist Award Programme (TUBA-GEBIP). Frank J. Fabozzi acknowledges the financial support from EDHEC Business School.

Yes / So far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models.

The master's thesis is devoted to the development of methodological tools for analyzing and modeling the risk parameters of the loan portfolio of individuals. As a scientific novelty, a diagnostic matrix of risk parameters of borrowers was constructed based on the Bayesian method, which allows categorizing borrowers by risk levels, taking into account the multiplicity of analyzed parameters. The result of modeling the risk parameters of bank's loan portfolio based on optimality criteria (risk-return) is presented. The practical significance of the study lies in fact that the management of a commercial bank can use the results obtained to improve the quality of credit management. / Магистерская диссертация посвящена разработке методического инструментария для анализа и моделирования рисковых параметров кредитного портфеля физических лиц. В качестве научной новизны построена диагностическая матрица риск-параметров заемщиков на основе метода Байеса, что позволяет категорировать заемщиков по уровням риска с учетом множественности анализируемых параметров. Также представлен результат моделирования риск-параметров кредитного портфеля банка на основе критериев оптимальности (риск-доходность). Практическая значимость исследования заключается в том, что руководство коммерческого банка может использовать полученные результаты в целях повышения качества кредитного менеджмента.

本文建立了當違約機率及回收率為隨機變動，同時信用等級移動有記憶性，且回收率和無風險利率期間結構相關之信用風險價差期間結構模型。並評價信用價差選擇權及有對手違約風險普通選擇權之價值。 此模型產生的信用價差有更多的變化性，將可描述：信用價差的隨機波動行為，且即使信用等級沒變，價差仍可能發生改變；信用價差與無風險利率期間結構有相關性；公司特定或證券特定的價差及其變動行為；處於等級上升或下降趨勢公司債券之殖利率曲線，能更準確配適有風險債券的價格等實際現象。 並可應用至有對手違約風險之商品及多種信用衍生性商品等的評價與避險，且可進行風險管理方面的應用。 關鍵詞：信用風險；信用風險價差；馬可夫模型；信用衍生性商品 / In this thesis we develop a credit migration model with memory for the term structure of credit risk spreads. Our model incorporates stochastic default probability, stochastic recovery rate, and the correlation between the recovery rate and the term structure of risk-free interest rates. We derive valuation formulae for a credit spread option and a plain vanilla option with counterparty risk. This model provides greater variability in credit spreads, and it has properties in line with what have been observed in practice: (1) credit spreads show diffusion-like behavior even though the credit rating of the firm has not changed; (2) the model injects correlation between spreads and the term structure of interest rates; (3) the model enables firm-specific and security-specific variability of spreads to be accommodated; and (4) the model enables us to estimate the yield curves corresponding to the positive and negative trends of credit ratings and match the observed risky bond prices more precisely. This model is useful for pricing and hedging OTC derivatives with counterparty risk, for pricing and hedging credit derivatives, and for risk management. Key Words: Credit Risk, Credit Risk Spread, Markov Model, Credit Derivative.

The field of bankruptcy prediction has experienced a notable increase of interest in recent years. Machine Learning (ML) models have been an essential component of developing more sophisticated models. Previous studies within bankruptcy prediction have not evaluated how well ML techniques adopt for data sets of companies with higher credit risks. This study introduces a binary decision rule for identifying companies with higher credit risks (abnormal companies). Two categories of abnormal companies are explored based on the activity of: (1) abnormal credit risk analysis (”AC”, herein) and (2) abnormal payment remarks (”AP”, herein) among small Swedish limited companies. Companies not fulfilling the abnormality criteria are considered normal (”NL”, herein). The abnormal companies showed a significantly higher risk for future payment defaults than NL companies. Previous studies have mainly used financial features for bankruptcy prediction. This study evaluates the contribution of different feature categories: (1) financial, (2) qualitative, (3) performed credit risk analysis, and (4) payment remarks. Implementing a Light Gradient Boosting Machine (LightGBM), the study shows that bankruptcies are easiest to anticipate among abnormal companies compared to NL and all companies (full data set). LightGBM predicted bankruptcies with an average Area Under the Precision Recall Curve (AUCPR) of 45.92% and 61.97% for the AC and AP data sets, respectively. This performance is 6.13 - 27.65 percentage units higher compared to the AUCPR achieved on the NL and full data set. The SHapley Additive exPlanations (SHAP)-values indicate that financial features are the most critical category. However, qualitative features highly contribute to anticipating bankruptcies on the NL companies and the full data set. The features of performed credit risk analysis and payment remarks are primarily useful for the AC and AP data sets. Finally, the field of bankruptcy prediction is introduced to: (1) evaluate if bankruptcies among companies with other forms of credit risk can be anticipated with even higher predictive performance and (2) test if other qualitative features bring even better predictive performance to bankruptcy prediction. / Konkursklassificering har upplevt en anmärkningsvärd ökning av intresse de senaste åren. I denna utveckling har maskininlärningsmodeller utgjort en nyckelkompentent i utvecklingen mot mer sofistikerade modeller. Tidigare studier har inte utvärderat hur väl maskininlärningsmodeller kan appliceras för att förutspå konkurser bland företag med högre kreditrisk. Denna studie introducerar en teknik för att definiera företag med högre kreditrisk, det vill säga avvikande företag. Två olika kategorier av avvikande företag introduceras baserat på företagets aktivitet av: (1) kreditrisksanalyser på företaget (”AK”, hädanefter), samt (2) betalningsanmärkningar (”AM”, hädanefter) för små svenska aktiebolag. Företag som inte uppfyller kraven för att vara ett avvikande företag klassas som normala (”NL”, hädanefter). Studien utvärderar sedan hur väl konkurser kan förutspås för avvikande företag i relation till NL och alla företag. Tidigare studier har primärt utvärdera finansiella variabler för konkursförutsägelse. Denna studie utvärderar ett bredare spektrum av variabler: (1) finansiella, (2) kvalitativa, (3) kreditrisks analyser, samt (4) betalningsanmärkningar för konkursförutsägelse. Genom att implementera LightGBM finner studien att konkurser förutspås med högst noggrannhet bland AM företag. Modellen presenterar bättre för samtliga avvikande företag i jämförelse med både NL företag och för hela datasetet. LightGBM uppnår ett genomsnittligt AUC-PR om 45.92% och 61.97% för AK och AM dataseten. Dessa resultat är 6.13-27.65 procentenheter högre i jämförelse med det AUC-PR som uppnås för NL och hela datasetet. Genom att analysera modellens variabler med SHAP-värden visar studien att finansiella variabler är mest betydelsefulla för modells prestation. Kvalitativa variabler har däremot en stor betydelse för hur väl konkurser kan förutspås för NL företag samt alla företag. Variabelkategorierna som indikerar företagets historik av genomförda kreditrisksanalyser samt betalningsanmärkningar är primärt betydelsefulla för konkursklassificering av AK samt AM företag. Detta introducerar området av konkursförutsägelse till att: (1) undersöka om konkurser bland företag med andra kreditrisker kan förutspås med högre noggrannhet och (2) test om andra kvalitativa variabler ger bättre prediktive prestandard för konkursförutsägelse.

This thesis consists of an introduction and five articles in the field of financial mathematics. The main topics of the papers comprise credit risk modelling, optimal stopping theory, and Dynkin games. An underlying theme in all of the articles is valuation of various financial instruments. Namely, Paper I deals with valuation of a game version of a perpetual American option where the parties disagree about the distributional properties of the underlying process, Papers II and III investigate pricing of default-sensitive contingent claims, Paper IV treats CVA (credit value adjustment) modelling for a portfolio consisting of American options, and Paper V studies a problem motivated by model calibration in pricing of corporate bonds. In each of the articles, we deal with an underlying stochastic process that is continuous in time and defined on some probability space. Namely, Papers I-IV treat stochastic processes with continuous paths, whereas Paper V assumes that the underlying process is a jump-diffusion with finite jump intensity. The information level in Paper I is the filtration generated by the stock value. In articles III and IV, we consider investors whose information flow is designed as a progressive enlargement with default time of the filtration generated by the stock price, whereas in Paper II the information flow is an initial enlargement. Paper V assumes that the default is a hitting time of the firm's value and thus the underlying filtration is the one generated by the process modelling this value. Moreover, in all of the papers the risk-free bonds are assumed for simplicity to have deterministic prices so that the focus is on the uncertainty coming from the stock price and default risk.