Consumer Defaults and Social Capital
Brian Clark, Iftekhar Hasan, Helen Lai, Feng Li, Akhtar Siddique
Journal of Financial Stability,
April
2021
Abstract
Using account level data from a credit bureau, we study the role that social capital plays in consumer default decisions. We find that borrowers in communities with greater social capital are significantly less likely to default on loans, even after adjusting for different levels of income and other characteristics such as credit scores. The results are strongest for potentially strategic defaults on mortgages; a one standard deviation increase in social capital reduces such defaults by 12.4 %. These results can be generalized to any mortgage default. Our results also indicate that the effect of social capital is most prominent among more creditworthy borrowers, suggesting that when given a choice, the social cost of defaulting is an important factor affecting default decisions. We find a similar impact of social capital on consumer defaults in other datasets with more detailed information on borrowers as well. Our results are robust to modeling and methodology choices, as well as controlling for other drivers of default such as wealth, income and amenities from homeownership. Our results suggest that increasing social capital via measures to build community cohesion such as promotion of owner-occupied home ownership may be one avenue to deter consumer default.
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
2021
Abstract
Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Pricing Sin Stocks: Ethical Preference vs. Risk Aversion
Stefano Colonnello, Giuliano Curatola, Alessandro Gioffré
European Economic Review,
2019
Abstract
We develop an ethical preference-based model that reproduces the average return and volatility spread between sin and non-sin stocks. Our investors do not necessarily boycott sin companies. Rather, they are open to invest in any company while trading off dividends against ethicalness. When dividends and ethicalness are complementary goods and investors are sufficiently risk averse, the model predicts that the dividend share of sin companies exhibits a positive relation with the future return and volatility spreads. An empirical analysis supports the model’s predictions. Taken together, our results point to the importance of ethical preferences for investors’ portfolio choices and asset prices.
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Flight from Safety: How a Change to the Deposit Insurance Limit Affects Households‘ Portfolio Allocation
H. Evren Damar, Reint E. Gropp, Adi Mordel
IWH-Diskussionspapiere,
Nr. 19,
2019
Abstract
We study how an increase to the deposit insurance limit affects households‘ portfolio allocation by exogenously reducing uninsured deposit balances. Using unique data that identifies insured versus uninsured deposits, along with detailed information on Canadian households‘ portfolio holdings, we show that households respond by drawing down deposits and shifting towards mutual funds and stocks. These outflows amount to 2.8% of outstanding bank deposits. The empirical evidence, consistent with a standard portfolio choice model that is modified to accommodate uninsured deposits, indicates that more generous deposit insurance coverage results in nontrivial adjustments to household portfolios.
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IWH-FDI-Mikrodatenbank
IWH-FDI-Mikrodatenbank Die IWH-FDI-Mikrodatenbank (FDI = Foreign Direct Investment)...
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Arbeitspapiere
Fiscal Policy and Fiscal Fragility: Empirical Evidence from the OECD ...
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Publikationen
Does Machine Learning Help us Predict Banking Crises? ...
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Housing Consumption and Macroprudential Policies in Europe: An Ex Ante Evaluation
Antonios Mavropoulos, Qizhou Xiong
IWH Discussion Papers,
Nr. 17,
2018
Abstract
In this paper, we use the panel of the first two waves of the Household Finance and Consumption Survey by the European Central Bank to study housing demand of European households and evaluate potential housing market regulations in the post-crisis era. We provide a comprehensive account of the housing decisions of European households between 2010 and 2014, and structurally estimate the housing preference of a simple life-cycle housing choice model. We then evaluate the effect of a tighter LTV/LTI regulation via counter-factual simulations. We find that those regulations limit homeownership and wealth accumulation, reduces housing consumption but may be welfare improving for the young households.
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