The deregulation of the financial markets that started over two decades ago in the developed countries has led to increased house prices and loan to value ratios. Home owners in western countries have over the last two decades steadily decreased their savings and at the same time increased the size of their mortgages and the amount of leverage used to purchase their homes. This development has increased the financial risk for homeowners which recently became clear in the United States when prices on homes started to fall rapidly in 2007. Due to this development Finansinspektionen in Sweden has enforced new regulation on mortgage lending making it more expensive for home owners to use high leverage ratios. Finansinspektionen is responsible for consumer protection in terms of financial products and the new regulation aims to protect mortgage borrowers. Finansinspektionen suggests that an insurance that protects the borrower from loss could be used as an alternative to the regulation restricting the amount of leverage. Finansinspektionen also mentions the Canadian mortgage market as an example where compulsory mortgage insurances are enforced today. In Canada the borrower must take out a mortgage insurance when the mortgage exceeds 80 percent of the house value. However, we find that the Canadian mortgage insurance system would not fulfil the aim of Finansinspektionen’s regulation. The Canadian mortgage insurances are constructed to protect the lender against default and there purpose was initially to increase lending. When examining the basic structure of mortgage and home value insurance products we find that such products and systems are complicated to construct to match the Finansinspektionen requirements and purpose due to issues such as moral hazard, adverse selection, price, willingness to pay and systemic risk.

The aim of the thesis is to bring new insights into banks' internal credit risk estimates and their application in estimation of credit transition matrices, which are an important part of credit risk modelling with limited publicly available sources. The doctoral thesis consists of three essays that jointly analyse features of bank- sourced credit risk data and practicalities of transition matrices estimation. In the first essay, I empirically test two assumptions widely used for estimation of transition matrices: Markovian property and time homogeneity. The results indicate that internal credit risk estimates do not satisfy the two assumptions, showing evidence of both path-dependency and time heterogeneity even within a period of economic expansion. Contradicting previous findings based on data from credit rating agencies, banks tend to revert their past rating actions. The second essay analyses the extent to which transition matrices depend on the characteristics of the underlying overlapping bank-sourced credit risk datasets and the aggregation method. It outlines that the choice of aggregation approach has a substantial effect on credit risk model results. I also show that bank-sourced transition matrices are more dynamic than those produced by credit rating agencies and introduce industry-specific...

We develop a framework to quantify credit risks of non-traditional mortgage products (NMPs). Ex ante probabilities of default are caused by willingness-to-pay and ability-to-pay problems and the high default rates for NMPs confirm that payment shock is a critical default risk indicator. Monte Carlo simulations are conducted using three correlated stochastic variables (mortgage interest rate, home price, and household income) under normal and stressed economies. Results confirm that the default risk of 2/28 and option ARM contracts requiring a minimum monthly interest payment have a greater probability of default than other mortgage products in all economic scenarios. Additionally, the credit risk of NMPs is primarily systematic risk, suggesting that these products should require higher risk-based capital. Due to the non-linear distribution of credit risk, even the advanced internal-based rating approach of the Basle II framework can understate the risk involved in these NMPs.

We develop a framework to quantify credit risks of non-traditional mortgage products (NMPs). Ex ante probabilities of default are caused by willingness-to-pay and ability-to-pay problems and the high default rates for NMPs confirm that payment shock is a critical default risk indicator. Monte Carlo simulations are conducted using three correlated stochastic variables (mortgage interest rate, home price, and household income) under normal and stressed economies. Results confirm that the default risk of 2/28 and option ARM contracts requiring a minimum monthly interest payment have a greater probability of default than other mortgage products in all economic scenarios. Additionally, the credit risk of NMPs is primarily systematic risk, suggesting that these products should require higher risk-based capital. Due to the non-linear distribution of credit risk, even the advanced internal-based rating approach of the Basle II framework can understate the risk involved in these NMPs.

The use of machine learning methods in credit risk modelling has been proven to yield good results in terms of increasing the accuracy of the risk score as- signed to customers. In this thesis, the aim is to examine the performance of the machine learning boosting algorithms XGBoost and CatBoost, with logis- tic regression as a benchmark model, in terms of assessing credit risk. These methods were applied to two different data sets where grid search was used for hyperparameter optimization of XGBoost and CatBoost. The evaluation metrics used to examine the classification accuracy of the methods were model accuracy, ROC curves, AUC and cross validation. According to our results, the machine learning boosting methods outperformed logistic regression on the test data for both data sets and CatBoost yield the highest results in terms of both accuracy and AUC.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第21530号 / 経博第598号 / 新制||経||289(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 江上 雅彦, 教授 西山 慶彦, 准教授 砂川 伸幸 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

The imminent consequences of the deteriorating state of the socio-ecological systems pose significant challenges to the well-being of society and societal functioning. The financial sector, specifically banks, plays a crucial role in the transition toward sustainable development because they hold the financial resources and the power to allocate these resources. For banks to contribute to this transition, credit risk assessment (CRA) can serve as an impactful process for sustainability integration. However, as CRA is a well-incorporated process within banks, it is unclear if it is strategic enough to support a transition toward sustainability. This research, therefore, aims to analyse the key gaps, opportunities, and limitations for integrating sustainability considerations into the credit risk assessment process using a strategic sustainable development (SSD) lens. The Framework for Strategic Sustainable Development (FSSD) was used as a conceptual framework to give a better understanding of the sustainability challenge and to analyse how credit risk assessment can contribute to sustainable development. Data were collected through document review and semi-structured interviews with practitioners from Nordic banks who have relevant experience to explain how banks incorporate sustainability into their credit risk assessment practices within corporate lending and credit. A semi-systematic literature review was also done to determine the views and methods of integrating sustainability considerations into the credit risk assessment process according to academic literature. The findings were structured using the FSSD’s 5-Level Model (5LM) to identify the key gaps, limitations, and opportunities in literature and practice. The results suggest that the key gaps in integrating sustainability into the credit risk assessment process are sustainability data inadequacy, inaccessibility, incomparability, trustworthiness, and storage, and the qualitative manner of sustainability data. Further to these limitations is a competence gap where crucial skillsets needed include systems and sector-specific sustainability knowledge.

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