Counterparty credit risk is present in trades offinancial obligations. This master thesis investigates the up and comingtechnology blockchain and how it could be used to mitigate counterparty creditrisk. The study intends to cover essentials of the mathematical model expectedloss, along with an introduction to the blockchain technology. After modellinga simple smart contract and using historical financial data, it was evidentthat there is a possible opportunity to reduce counterparty credit risk withthe use of blockchain. From the market study of this thesis, it is obvious thatthe current financial market needs more education about blockchain technology. / Motpartsrisk är närvarande i finansiella obligationer. Den här uppsatsen un- dersöker den lovande teknologin blockkedjan och hur den kan användas för att reducera motpartsrisk. Studien har för avsikt att täcka det essentiel- la i den matematiska modellen för förväntad förlust, samt en introduktion om blockkedjeteknologi. Efter att ha modellerat ett enkelt smart kontrakt, där historiska finansiella data använts, var det tydligt att det kan finnas en möjlighet att reducera motpartsrisk med hjälp av blockkedjan. Från mark- nadsundersökningen gjord i studien var det uppenbart att den nuvarande finansiella marknaden är i stort behov av mer utbildning om blockkedjan.

Магистерская диссертация посвящена вопросам управления портфельным кредитным риском коммерческих банков в условиях неопределенности и асимметрии информации. Целью исследования является разработка и экономическое обоснование методического инструментария, позволяющего оценивать и регулировать портфельный кредитный риск банка по корпоративным ссудам. Сделан вывод о том, что отраслевая диверсификация кредитов является инструментом, позволяющим снизить концентрацию кредитного риска по портфелю корпоративных кредитов. / Master thesis is devoted to the management of portfolio credit risk of commercial banks in conditions of uncertainty and information asymmetry. The aim of the study is the development and economic justification of methodological instruments to assess and manage portfolio credit risk of corporate loans. It is concluded that industrial diversification of loans is a instrument that allows to reduce concentration of credit risk of the corporate loan portfolios.

Магистерская диссертация посвящена вопросам оценки кредитного риска заемщиков – физических лиц. Целью исследования является разработка методического подхода по оценке кредитоспособности заемщиков как направления совершенствования управления кредитным риском. В работе сделан вывод о том, что повышение качества кредитного портфеля банка способствует повышению конкурентоспособности банка в условиях высокой конкуренции и нормативных требований Банка России. / Master thesis is devoted to the assessment of credit risk of individual borrowers. The aim of the study is the development a methodical approach to assess the creditworthiness of borrowers as a way to improve credit risk management. The work concluded that improving the quality of the credit portfolio of a bank contributes to improving competitiveness of bank in conditions of high competition and regulatory requirements of the Bank of Russia.

This thesis provides an end-to-end picture of the modelling of interest rates and Foreign Exchange (FX) rates. We start by defining the FX rates and the interest rates. After having a good understanding of the basics, we take a deep dive into the approaches commonly used to model interest rates and FX rates respectively. In particular, we present an interest rate model and a FX rate model that I have developed for man- aging Swedbank’s Counterparty Credit Risk (CCR). In addition to the mathematical derivations, we describe the theories underlying the models, discuss the model com- parisons, and explain the model choices made in practical applications. Finally, we provide a prototype of model implementation to illustrate how theory can be put into practice. I had some doubts about the interest rate model and the FX rate model that I have developed for managing Swedbank’s CCR. These doubts have been cleared up through this thesis work. Both the doubts and the clarifications are described in this thesis. / Denna uppsats tillför en helhetsbild av modellering av räntorna och valutakurserna. Vi börjar med att definiera räntorna och valutakurserna. Med en bra uppfattning av grunden, gör vi en djupdykning i de metoder som används för att modellera räntorna och valutakurserna respektive. I synnerhet presenterar vi en räntemodell och en valu- takursmodell, som jag har utvecklat för att hantera Swedbanks motpartsrisk. Förutom de matematiska härledningarna beskriver vi också modellernas underliggande teorier, diskuterar modellerjämförelser, och förtydligar de modellval som gjorts i praktiska tillämpningar. Slutligen använder vi en prototyp för att belysa genomförandet av modellerna. Jag var en smula tveksam till de riskfaktormodeller som jag har utvecklat för att hantera Swedbanks motpartsrisk. Jag har klargjort dessa tvivel genom att arbeta med den här uppsatsen. Både tvivlen och klargörandena beskrivs i denna rapport.

This thesis investigates the robustness and stability of total return series for credit bond index investments. Dueto the challenges which arise for financial institutes and investors in achieving these objectives, we aim to createa forecasting model which matches the statistical properties of historical data, while remaining robust, stable andeasy to calibrate. To reach this goal, we implement autoregressive time-series models for credit spreads, a Vasicekmodel for the interest rate and use transformations to create total return series. We find that our autoregressivemodel performs well in terms of robustness and stability, while being statistically accurate for the Investment GradeIndex. The High Yield model has good statistical accuracy, but is lacking in stability and robustness. / Denna avhandling undersöker robustheten och stabiliteten hos totalavkastningsserier för investeringar ikreditobligationsindex. På grund av de utmaningar som uppstår för finansiella institut och investerare i att uppnådessa mål, syftar vi till att skapa en prognosmodell som matchar de statistiska egenskaperna hos historiska datasamtidigt som den förblir robust, stabil och enkel att kalibrera. För att nå detta mål implementerar vi autoregressivatidsserie-modeller för kreditspridningar, en Vasicek-modell för räntan och använder transformationer för att skapatotalavkastningsserier. Vi finner att vår autoregressiva modell för Investment Grade Indexet presterar bra gällanderobusthet och stabilitet samtidigt som den är statistiskt korrekt. High Yield modellen är statistiskt korrekt, men ärmindre bra gällande stabilitet och robusthet.

Housing prices have risen a lot in recent years, and many who have been outside the market may find it difficult to enter. Mortgage lending is something that can drive home prices. The credit officer grants the mortgage and makes the decision according to models that exist within the credit institution. There are many risks with lending, which the Swedish Financial Supervisory Authority, the Basel Committee and other organizations are trying to minimize by placing demands on banks and households. One risk that seems difficult to assess is the credit officer´s human factor that influences decision-making. Previous international research has pointed out that the human factor in lending exists and that it needs to be eliminated to improve the credit process. The uncertainty about the repayment ability can sometimes be difficult to assess and the creditor is forced to make decisions under uncertainty. Does the individual lender have the opportunity to give out loans if the future borrower does not fit the models and calculations that are set up in the organization or is that opportunity minimized in Swedish commercial banks today? A qualitative interview study has been done to try to map this out with the conclusion that the possibility exists, albeit in a very limited form.

The problem of determining the probability of default or credit risk for companies is crucial when providing financial services. This problem is often modeled based on snapshot data that does not take the time dimension into account. Instead, we approach the problem with enterprise resource planning data in time series. With the added complexity the time series introduce, we pose that deep learning models could be suitable for the task. A comparison of a fully convolutional network and a transformer encoder was made to the current state-of-the-art model for the probability of default problem, XGBoost. The comparison showed that XGBoost generalized very well to the time series domain, even well enough to beat the deep learning models across all evaluation metrics. Furthermore, time series data with monthly, quarterly and yearly timestamps over three years was tested. Also, public features that could be extracted from quarterly and annual financial reports were compared with internal enterprise resource planning data. We found that the introduction of time series to the problem improves the performance and that models based on internal data outperform the ones based on public data. To be more precise, we argue that the dataset being based on small to medium-sized companies lessens the impact of highly granular data, and makes the selection of what features to include more prominent. This is something XGBoost takes advantage of in a very efficient way, especially when extracting features that capture the behavior of the time series, causing it to beat the deep learning competitors even though it does not pick up on the sequential aspect of the data.

International loan is a very important business for Taiwan commercial banks, and China is the largest credit market for Taiwan banks. However, when doing due diligence or credit risk research for Chinese companies, Taiwan commercial banks are facing difficulties and risks such as difficulty to monitor the company, a decelerating Chinese economy growth and other risk factors. Because of these difficulties, the common method used for international loan due diligence in Taiwan is not adequate to cope with the current credit risk situation. There were two famous Chinese default cases in Taiwan during 2014, in which the thesis picks the Ultrasonic AG financial fraud case as an example. The Ultrasonic AG case shows that it is possible to use public information to detect and prevent financial frauds. Based on the case study, the thesis suggests Taiwan commercial banks could use a more comprehensive method of due diligence, to increase the accuracy of credit research. In the end, the thesis also suggests a KL credit scoring system, based on past financial frauds, to assist in credit research. By utilizing a more comprehensive method of due diligence and KL score system with traditional models, Taiwan commercial banks could better detect and prevent international financial frauds, especially financial frauds in China.

This thesis investigates correlations and linkages in credit risk that widely exist in all sectors of the financial markets. The main body of this thesis is constructed around four empirical chapters. I started with extending two main issues focused by earlier empirical studies on credit derivatives markets: the determinants of CDS spreads and the relationship between CDS spreads and bond yield spreads, with a special focus on the effect of the subprime crisis. By having observed that the linear relationship can not fully explain the variation in CDS spreads, the third empirical chapter investigated the dependence structure between CDS spread changes and market variables using a nonlinear copula method. The last chapter investigated the relationship between the CDS spread and another credit spread - the TED spread, in that a MVGARCH model and twelve copulas are set forth including three time varying copulas. The results of this thesis greatly enhanced our understanding about the effect of the subprime crisis on the credit default swap market, upon which a set of useful practical suggestions are made to policy makers and market participants.

可轉換公司債是一種複雜且擁有許多風險的商品，而對於台灣的可轉債市場來說，信用風險佔了評價裡很重要的一部份。本篇論文使用縮減式評價模型，考慮信用風險及股價跳躍。跳躍模型使用Pareto-Beta Jump-Diffusion模型，並且利用信用價差之動態過程，來對可轉換公司債作評價，而為了解決提前轉換的問題，也使用了最小平方蒙地卡羅法來處理。本篇論文分別對宏碁與新光金之可轉債做實證研究，實證結果顯示，加入了股價跳躍之後，的確可以使理論價格更貼近市場真實價格。