Network based scoring models to improve credit risk management in peer to peer lending platforms

Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models

Production and financial linkages in inter-firm networks: structural variety, risk-sharing and resilience

The paper analyzes how (production and financial) inter-firm networks can affect firms’ default probabilities and observed default rates: an issue the recent crisis has brought to the front of the debate. A simple theoretical model of shock transfer is built up to investigate some stylized facts on how firm-idiosyncratic shocks tend to be allocated in the network, and how this allocation changes firms’ default probability. The model shows that the network works as a perfect “risk-pooling” mechanism, when it is both strongly connected and symmetric. But the resort to “risk-sharing” does not necessarily reduce default rates in the network, unless the shock they face is lower on average than their financial capacity. Conceived as cases of symmetric inter-firm networks, industrial districts might have a comparative disadvantage in front of “heavy” financial crises such as the current one.Firm clusters, industrial districts, interlinking transactions,resilience, systemic risk

Production and financial linkages in inter-firm networks: structural variety, risk-sharing and resilience

The paper analyzes how (production and financial) inter-firm networks can affect firms' default probabilities and observed default rates: an issue the recent crisis has brought to the front of the debate. A simple theoretical model of shock transfer is built up to investigate some stylized facts on how firm-idiosyncratic shocks tend to be allocated in the network, and how this allocation changes firms' default probability. The model shows that the network works as a perfect "risk-pooling" mechanism, when it is both strongly connected and symmetric. But the resort to "risk-sharing" does not necessarily reduce default rates in the network, unless the shock they face is lower on average than their financial capacity. Conceived as cases of symmetric inter-firm networks, industrial districts might have a comparative disadvantage in front of "heavy" financial crises such as the current one.Firm clusters, industrial districts, interlinking transactions, resilience, systemic risk

Essays on risk preferences, time preferences, and credit risk contagion

This cumulative dissertation comprises two contributions on behavioral finance and one contribution on credit risk management. The first contribution examines the impact of investors’ probability distortion on the stock market and future economic growth. The empirical challenge is to quantify the optimality of today’s decisions in order to study its impact on future economic growth. Risk preferences can be estimated using stock prices. We use a time series of monthly aggregated stock prices from 1926 to 2015 and estimate risk preferences via an asset pricing model using cumulative prospect theory agents and compute a recently proposed probability distortion index. This index negatively fore- casts future GDP growth, both in-sample and out-of-sample, with stronger and more reliable predictability as the time increases. Our research results suggest that distorted stock prices can lead to significant welfare losses. The second contribution establishes empirical relation- ships of risk and time preferences on academic success. Subjects of our experiment are fourth-semester undergraduate economics students at Leibniz University Hannover. We measure academic success via the points achieved in a business exam in the 4th semester as well as the grade point average of the academic progress so far. Our methodology is based on Tanaka et al. (2010), who use a multiple price list to estimate time preferences and lotteries for the preference parameters of cumulative prospect theory. We find empirical evidence for quasi-hyperbolic discounting and a relationship between higher academic success and lower time discounting. No empirical evidence is observed for a link between risk preferences and academic performance. In the final contribution, we examine contagion effects in credit default risk defined as co-movement in the distances-to-default of U.S. firms, which we estimate from the model of Campbell et al. (2008). We quantify financial, inter-industry, and intra-industry contagion effects based on Fama and French’s 12 sectors and document significant co-movement across sectors during times of crises. We also find that a firm’s size and average share of total sales in each sector are significantly related to intra-industry contagion. Our results are robust to different crisis definitions and index weighting methodologies. Moreover, our results suggest that the probability of default increases in times of crisis due to contagion effects, which may lead to an underestimation of the risk measures of individual loans or portfolios and ultimately of economic capital

Systemic risk diagnostics: coincident indicators and early warning signals

We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures (‘thermometers’) and a forward looking indicator for the likelihood of simultaneous failure of a large number of financial intermediaries. The indicators are based on latent macro-financial and credit risk components for a large data set comprising the U.S., the EU-27 area, and the respective rest of the world. Credit risk conditions can significantly and persistently de-couple from macro-financial fundamentals. Such decoupling can serve as an early warning signal for macro-prudential policy. JEL Classification: G21, C33credit portfolio models, financial crisis, frailty-correlated defaults, state space methods, systemic risk