Contributions published at Department of Banking and Finance (Pablo Koch Medina)

The recent surge in inflation represents the first time many individuals experience inflation considerably above central banks’ targets. Despite limited inflation experience, inflation expectations of many households have been upward biased relative to ex-post realizations and target rates. This column proposes inflation shocks in the more distant past as an explanation for elevated inflation expectations. Consistent with this conjecture, German households living in areas with higher local inflation during the hyperinflation of the 1920s expect higher inflation today. Long-lasting effects of inflation shocks on attitudes toward inflation have important implications for monetary and fiscal policy as managing inflation expectations becomes more difficult.

We consider dynamic stochastic economies with heterogeneous agents and introduce the concept of uniformly self-justified equilibria (USJE)-temporary equilibria for which expectations satisfy the following rationality requirements: i) individuals' forecasting functions for the next period's endogenous variables are assumed to lie in a compact, finite-dimensional set of functions, and ii) the forecasts constitute the best uniform approximation to a selection of the equilibrium correspondence. We show that in contrast to rational expectations equilibria, USJE always exist, and we develop a simple algorithm to compute them. As an application, we discuss a stochastic overlapping generations exchange economy. We give an example where recursive (rational expectations) equilibria fail to exist and explain how to construct USJE for that example. In addition, we provide numerical examples to illustrate our computational method.

In this paper, we address the problem of analyzing and computing all steady states of an overlapping generation (OLG) model with production and many generations. The characterization of steady states coincides with a geometrical representation of the algebraic variety of a polynomial ideal, and, in principle, one can apply computational algebraic geometry methods to solve the problem. However, it is infeasible for standard methods to solve problems with a large number of variables and parameters. Instead, we use the specific structure of the economic problem to develop a new algorithm that does not employ the usual steps for the computation of Gröbner basis such as the computation of successive S-polynomial and expensive division.

Comparing banks to non-bank lenders, we investigate whether the geographical distance between lenders, borrowers, and their properties is reflected in the pricing of US mortgages that were included in US commercial mortgage-backed security (CMBS) pools during the 2000 to 2017 period. The difference in loan spreads when the bank-borrower distance increases from zero to the median of about 700 miles is 10 basis points, and this effect is more pronounced if the loan is collateralized by a riskier property. On the contrary, geographical distance does not seem to have any effect on the loan spread of mortgages granted by non-bank lenders. The difference in loan pricing across originator types (even after controlling for key mortgage and property characteristics) suggests that banks and non-bank lenders have different incentives, lending technologies, and/or different types of borrowers. Our results contribute to the emerging literature on non-bank lender behavior.

The flows to equity method is commonly used in leveraged buyouts and other highly levered transactions. These flows are hybrid flows, mixing expected operating cash flows with promised debt payments under a planned debt schedule. Because of this, it is difficult to accurately estimate the appropriate discount rate, a difficulty that is compounded by the typically changing leverage over time under the planned debt schedule. We show how the flows to equity approach works and discuss its benefits and drawbacks as compared with other, ‘more standard’ methods.

We analyze the predictive power of technical analysis with a novel data set based on news sentiment that allows to systematically examine a set of technical analysis indicators over an extensive time period. We do not find much statistically significant relationships with the examined indicators and future asset returns, and we almost do not find any alphas in trading strategies based on technical analysis sentiment. We find evidence for a contrarian-based hypothesis: past market returns and technical analysis sentiment are able to predict future technical analysis sentiment with a negative relationship.

This study examines the stock price reactions of environmentally responsible stocks during the onset of the 2023 banking crisis, triggered by the collapse of Silicon Valley Bank (SVB). Our findings indicate that stocks poised to benefit from the shift to a low-carbon economy underperformed during the 2023 crisis. This suggests that investors anticipate a slowdown in climate tech development due to distress in the banking sector. Our results underscore the significance of considering not only the influence of the climate crisis on financial stability, but also the pivotal role that financial stability plays in ensuring a successful energy transition.

Moral sensitivity is one of the most important educational goals in the ethical domain. This paper offers a first review of attempts to train moral sensitivity from 1979 to 2018, including 45 studies. The studies were reviewed using a combination of qualitative and quantitative analyses. Our results show that longer courses are generally more successful in fostering moral sensitivity. However, this effect is relatively small. What ultimately appears to have mattered most, was whether learners received personal feedback on their awareness to ethical issues. Training methods that appeared to be of little value in promoting moral sensitivity including problem-based learning, the discussion of papers and texts (seminars) and a strong focus on critical thinking and challenging existing perspectives. A further major implication of our results is that reviews of ethics training ought to differentiate between learning outcomes (e.g., moral sensitivity vs. reasoning).

Team of researchers wants to use tech to expose greenwashing.

Investors perceive stocks of companies with fluent names as more profitable. This perception may result from two different channels: a direct, non-deliberate affect toward fluent names or a deliberate interpretation of fluent names as a signal for company quality. We use preregistered experiments to disentangle these channels and test their limitations. Our results indicate the existence of a significant non-deliberate fluency effect, while the deliberate fluency effect can be activated and deactivated in boundary cases. Both effects are consistent across different groups of participants. However, whereas the fluency effect is strong in isolation, it has limitations when investors are confronted with additional information about the stock.

Central-bank money provides utility by serving as means of exchange for virtually all transactions in the economy. New reserves (money) are issued to banks in exchange for collateral such as government bonds. An asset's degree of direct convertibility into fresh reserves may affect its utility and, consequently, its market price. We show the existence of a government-bond reserves convertibility premium, which tapers off at longer maturities. Essentially, there is a pure monetary component to some asset prices. Our findings have implications for our understanding of liquidity premia, the term structure of interest rates, and the impact of central-bank collateral policy.

We investigate the impact of three non-climate environmental criteria: biodiversity, water, and pollution prevention, on infrastructure firms' credit risk term structure from the perspective of double materiality. Our findings show that firms that effectively manage these three environmental risks to which they are materially exposed have up to 93bps better long-term refinancing conditions compared to the worst-performing firms. While the results are less significant for the firm's material impact on the environment, investors still reward the management of these criteria beyond climate with improved long-term financing conditions for infrastructure investments. Overall, we find that financial markets respond positively to the prospect of more stringent regulations related to these criteria, which are currently used by the EU Taxonomy to assess the sustainability of investments.

German households living in areas with higher local inflation during the hyperinflation of the 1920s expect higher inflation today, suggesting that inflationary shocks have a long-lasting impact on attitudes toward inflation.

We show that banks cross-sell future deposits and loans to existing household depositors. A bank is 20-percentage-points more likely to sell a loan to an existing depositor than to an otherwise comparable household. Existing depositors pay a premium when borrowing, and we find no indication that banks obtain an informational advantage on such borrowers, suggesting that the cross-selling is driven more by demand than by supply complementarities. These demand complementarities are in turn driven more by stickiness rather than by unobserved persistent preferences. Finally, banks internalize future cross-selling potential when setting deposit rates.

Der Fokus auf Stock Picking, das Timing des Ein- und Ausstiegs sowie Erzielen einer Überrendite führt in die Irre. Entscheidend ist die richtige Anlagestrategie.

We assess how investors’ willingness-to-pay (WTP) for sustainable investments responds to the impact of those investments, using a framed field experiment. While investors have a substantial WTP for sustainable investments, they do not pay more for more impact. This also holds for dedicated impact investors. When investors compare several sustainable investments, their WTP responds to differences in impact but not to the absolute level of impact. Investors experience positive emotions when choosing sustainable investments, irrespective of investments’ impact. Our findings suggest that the WTP for sustainable investments is driven by an emotional rather than a calculative valuation of impact.

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Value-at-Risk (VaR) is a widely used statistical measure of financial risk. It provides an estimate of the maximum expected loss at a given confidence level over a specified time horizon. VaR models can be based on different statistical approaches, including Parametric models (Standard Normal, Weighted Standard Normal Increasing and Decreasing) and NonParametric models (Historical, Weighted Historical Increasing and Decreasing). However, there is no consensus on which approach is the best for predicting financial risk, and the choice of model can have a significant impact on the accuracy and robustness of VaR estimates. The goal of this study is to compare the performance of two different VaR models in forecasting the Value at Risk of different indices from the three asset classes of Equities, Commodities, and Fixed Income. The models to be tested are the Standard Normal and Historical, and for both we will analyze the three cases of Equally Weighted, Weighted Increasing and Weighted Decreasing. The primary objective of this study is to identify the model that provides the most accurate and robust predictions of risk for the three different indices. To achieve this objective, we will calculate VaR using the two different models for three different indices from three different asset classes. For each of the two models, we will apply different weights to the sample observations, equal, increasing and decreasing, so that each time different importance will be attributed to more dated or recent data. We will then backtest the VaR estimates using the Traffic Light Approach from the Basel II regulation, which is a supervisory tool used by regulators to assess the operational risk management practices of banks. We will classify the VaR estimates into three categories based on their performance: green, yellow, and red. The green category represents VaR estimates that perform well, the yellow category represents VaR estimates that need improvement, and the red category represents VaR estimates that are not acceptable. We will compare the performance of the VaR models based on the number of green lights achieved during the backtesting process. We will also analyze the results to determine which VaR model is the most robust and accurate for predicting the risk of different indices. Moreover, we will compare the results obtained utilizing 2-years data with the ones obtained utilizing 10-years data in order to add robustness to the findings. The expected findings of this study are that one of the VaR models will perform better than the others in terms of accuracy and robustness. We also expect to observe that for different asset classes the best performing model will vary, showing how one or another model best suits the different characteristics of each. The findings of this study will contribute to the existing literature on VaR modeling and model selection. In conclusion, this study aims to provide insights into the performance of different VaR models and their suitability for predicting financial risk. The findings of this study will be of interest to risk managers, investors, and regulators who use VaR as a tool for measuring and managing financial risk.

Die Förderung von ethischem Verhalten hat im Zusammenhang mit Fehlverhalten und Unternehmens- skandalen immer mehr an Bedeutung gewonnen. Diese Arbeit hat das Ziel, in diesem Rahmen die Forschungsfrage zu beantworten, ob Contemplation Questions und Regulatory Focus die Entscheidungs- fähigkeit unterstützen können. Dazu wurde eine Literaturrecherche zur Vertiefung des Verständnisses durchgeführt und darauf aufbauend wurden Annahmen und Hypothesen gebildet, die mittels Gedankenexperiment untersucht wurden. Die Erkenntnisse führen zum Schluss, dass Contemplation Questions und Regulatory Focus vereinzelt in der Lage sein sollten, die Entscheidungsfähigkeit unter passenden Bedingungen zu unterstützen. Ob dies jedoch in Verbindung der beiden Komponenten möglich ist, kann abschliessend nur durch weiterführende empirische Untersuchungen bestätigt werden.