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In times of crisis, insurance companies may invest into riskier assets to benefit from expected price recoveries. Using daily stock market data for 34 European insurers, I investigate how a stock market contraction, as experienced during the Covid-19 pandemic, affects insurers’ decision on the allocation of their corporate bond portfolio. I find that insurers shift their portfolio holdings pro-cyclically towards lower credit risk assets in the first month of the market contraction. As the crisis progresses, I find evidence for counter-cyclical investment behavior by insurers, which can neither be explained by credit rating downgrades of held bonds nor by hedging with CDS derivatives. The observed counter-cyclical investment behavior of insurers could be beneficial for the financial system in attenuating price declines, but excessive risk-taking by insurance companies over longer periods can also reinforce stress in the system.
Macro-finance theory predicts that financial fragility builds up when volatility is low. This “volatility paradox’” challenges traditional systemic risk measures. I explore a new dimension of systemic risk, spillover persistence, which is the average time horizon at which a firm’s losses increase future risk in the financial system. Using firm-level data covering more than 30 years and 50 countries, I document that persistence declines when fragility builds up: before crises, during stock market booms, and when banks take more risks. In contrast, persistence increases with loss amplification: during crises and fire sales. These findings support key predictions of recent macrofinance models.
This paper investigates systemic risk in the insurance industry. We first analyze the systemic contribution of the insurance industry vis-à-vis other industries by applying 3 measures, namely the linear Granger causality test, conditional value at risk and marginal expected shortfall, on 3 groups, namely banks, insurers and non-financial companies listed in Europe over the last 14 years. We then analyze the determinants of the systemic risk contribution within the insurance industry by using balance sheet level data in a broader sample. Our evidence suggests that i) the insurance industry shows a persistent systemic relevance over time and plays a subordinate role in causing systemic risk compared to banks, and that ii) within the industry, those insurers which engage more in non-insurance-related activities tend to pose more systemic risk. In addition, we are among the first to provide empirical evidence on the role of diversification as potential determinant of systemic risk in the insurance industry. Finally, we confirm that size is also a significant driver of systemic risk, whereas price-to-book ratio and leverage display counterintuitive results.
Does political conflict with another country influence domestic consumers' daily consumption choices? We exploit the volatile US-China relations in 2018 and 2019 to analyze whether US consumers reduce their visits to Chinese restaurants when bilateral relations deteriorate. We measure the degree of political conflict through negativity in media reports and rely on smartphone location data to measure daily visits to over 190,000 US restaurants. A deterioration in US-China relations induces a significant decline in visits not only to Chinese but also to other foreign ethnic restaurants, while visits to typical American restaurants increase. We identify consumers' age, race, and cultural openness to moderate the strength of this ethnocentric effect.
We explore how personality traits are related to household borrowing behavior. Using survey data representative for the Netherlands, we consider the Big Five personality traits (openness, conscientiousness, agreeableness, extraversion and neuroticism), as well as the belief that one is master of one’s fate (locus of control). We hypothesize that personality traits can complement as well as substitute financial knowledge of a household. We present three sets of results. First, we find that personality traits are positively correlated with borrowing expectations. Locus of control, extraversion and agreeableness are correlated with informal borrowing expectations, which is the expectation that one can borrow from family and friends. With respect to expectations on the approval of a formal loan application, it is locus of control and conscientiousness that are positively associated. Effect sizes are large and economically meaningful. Second, we find that personality traits are important for borrowing constraints. A more internal locus of control and higher neuroticism are correlated with being denied for credit, as well as discouraged borrowing. Our third set of results reports findings on personality traits and loan regret, and how traits are correlated with dealing with loan troubles. Many households in our sample express regret (21%), but more open, more agreeable and more neurotic individuals are more likely to express regret. Our results are not driven by financial knowledge, time preferences or risk attitudes. Overall these findings imply that non-cognitive traits are important for borrowing behavior of households.
The Federal Reserve has been publishing federal funds rate prescriptions from Taylor rules in its Monetary Policy Report since 2017. The signals from the rules aligned with Fed action on many occasions, but in some cases the Fed opted for a different route. This paper reviews the implications of the rules during the coronavirus pandemic and the subsequent inflation surge and derives projections for the future.
In 2020, the Fed took the negative prescribed rates, which were far below the effective lower bound on the nominal interest rate, as support for extensive and long-lasting quantitative easing. Yet, the calculations overstate the extent of the constraint, because they neglect the supply side effects of the pandemic.
The paper proposes a simple model-based adjustment to the resource gap used by the rules for 2020. In 2021, the rules clearly signaled the need for tightening because of the rise of inflation, yet the Fed waited until spring 2022 to raise the federal funds rate. With the decline of inflation over the course of 2023, the rules’ prescriptions have also come down. They fall below the actual federal funds rate target range in 2024. Several caveats concerning the projections of the interest rate prescriptions are discussed.
This paper addresses the need for transparent sustainability disclosure in the European Auto Asset-Backed Securities (ABS) market, a crucial element in achieving the EU's climate goals. It proposes the use of existing vehicle identifiers, the Type Approval Number (TAN) and the Type-Variant-Version Code (TVV), to integrate loan-level data with sustainability-related vehicle information from ancillary sources. While acknowledging certain challenges, the combined use of TAN and TVV is the optimal solution to allow all stakeholders to comprehensively assess the environmental characteristics of securitised exposure pools in terms of data protection, matching accuracy, and cost-effectiveness.
External linkages allow nascent ventures to access crucial resources during the process of new product development. Forming external linkages can substantially contribute to a venture’s performance. However, little is known about the paths of external linkage formation, as well as the circumstances that drive the choice to pursue one rather than another path. This gap deserves further investigation, because we do not know whether insights developed for incumbent firms also apply to nascent ventures: To address this gap, we explore a novel dataset of 370 venture creation processes. Using sequence analyses based on optimal matching techniques and cluster analyses, we reveal that nascent ventures pursue one of overall four distinct paths of linkage formation activities during new product development. Contrary to the findings of the strategy literature, we find that if nascent ventures engage in external linkages at all, they do not combine exploration- and exploitation-oriented linkages but form either exploration- or exploitation-oriented linkages. Additional regression analyses highlight the circumstances that lead nascent ventures to pursue one rather than the other pathways. Taken together, our analyses point out that resource scarcity constitutes an important factor shaping the linkage formation activities of nascent ventures. Accordingly, we show that nascent ventures tend not to optimize by adding complementary knowledge to the firm’s knowledge base but rather to extend the existing knowledge base—a strategy which we call bricolage.
The 2011 Arab Spring marked the opening of the Central Mediterranean Route for irregular border crossings between Libya and Italy, which produced heterogeneous reductions of bilateral smuggling distances between country pairs in the Mediterranean region. We exploit this source of spatial and temporal variation in bilateral distance along land and sea routes to estimate the elasticity of irregular migration intentions for African and Near East countries. We estimate an elasticity of migration intentions to smuggling distances exceeding −3, mainly driven by countries with weak rule of law and high internet penetration. Our findings are consistent across irregular migration measures both at the aggregate and individual levels. We show that irregular migration elasticity is higher for youth, relatively skilled individuals and those with an informative advantage (having a social network abroad or a mobile phone).
Nations are imposing unprecedented measures at a large scale to contain the spread of the COVID-19 pandemic. While recent studies show that non-pharmaceutical intervention measures such as lockdowns may have mitigated the spread of COVID-19, those measures also lead to substantial economic and social costs, and might limit exposure to ultraviolet-B radiation (UVB). Emerging observational evidence indicates the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. This observational study empirically outlines the protective roles of lockdown and UVB exposure as measured by the ultraviolet index (UVI). Specifically, we examine whether the severity of lockdown is associated with a reduction in the protective role of UVB exposure. We use a log-linear fixed-effects model on a panel dataset of secondary data of 155 countries from 22 January 2020 until 7 October 2020 (n = 29,327). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors. After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with − 0.85 percentage points (p.p) and − 4.7 p.p decline in COVID-19 deaths growth rate, indicating their respective protective roles. The change of UVI over time is typically large (e.g., on average, UVI in New York City increases up to 6 units between January until June), indicating that the protective role of UVI might be substantial. However, the widely utilized and least severe lockdown (governmental recommendation to not leave the house) is associated with the mitigation of the protective role of UVI by 81% (0.76 p.p), which indicates a downside risk associated with its widespread use. We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find evidence that an increase in lockdown severity is associated with significant mitigation in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have even reduced the number of COVID-19 deaths more strongly than lockdowns alone. For example, we estimate that there would be 11% fewer deaths on average with sufficient UVB exposure during the period people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns, and could inspire further clinical studies that may support policy decision-making in countries imposing such measures.
This research focuses on the cost of financing green projects on the primary bond market and tests for a potential price differential between green bonds issued by government entities and those issued by supranational and private sector issuers. Our findings indicate that government entities benefit from more favorable pricing conditions worldwide. This advantage is growing over time and particularly pronounced for sovereigns and municipal authorities. Our analysis also reveals that country-specific factors, such as strong political commitment to address climate change, low income level and high degree of indebtedness are significant predictors of the pricing spread across bonds.
Contagious stablecoins?
(2023)
Can competing stablecoins produce efficient and stable outcomes? We study competition among stablecoins pegged to a stable currency. They are backed by interest-bearing safe assets and can be redeemed with the issuer or traded in a secondary market. If an issuer sticks to an appropriate investment and redemption rule, its stablecoin is invulnerable to runs. Since an issuer must pay interest on its stablecoin if other issuers also pay interest, competing interest-bearing stablecoins, however, are contagious and can render the economy inefficient and unstable. The efficient allocation is uniquely implemented when regulation prevents interest payments on stablecoins.
In this study, we unpack the ESG ratings of four prominent agencies in Europe and find that (i) each single E, S, G pillar explains the overall ESG score differently,(ii) there is a low co-movement between the three E, S, G pillars and (iii) there are specific ESG Key Performance Indicators (KPIs) that are driving these ratings more than others. We argue that such discrepancies might mislead firms about their actual ESG status, potentially leading to cherry-picking areas for improvement, thus raising questions about the accuracy and effectiveness of ESG evaluations in both explaining sustainability and driving capital toward sustainable companies.
We document the individual willingness to act against climate change and study the role of social norms in a large sample of US adults. Individual beliefs about social norms positively predict pro-climate donations, comparable in strength to universal moral values and economic preferences such as patience and reciprocity. However, we document systematic misperceptions of social norms. Respondents vastly underestimate the prevalence of climate-friendly behaviors and norms. Correcting these misperceptions in an experiment causally raises individual willingness to act against climate change as well as individual support for climate policies. The effects are strongest for individuals who are skeptical about the existence and threat of global warming.
Despite a number of helpful changes, including the adoption of an inflation target, the Fed’s monetary policy strategy proved insufficiently resilient in recent years. While the Fed eased policy appropriately during the pandemic, it fell behind the curve during the post-pandemic recovery. During 2021, the Fed kept easing policy while the inflation outlook was deteriorating and the economy was growing considerably faster than the economy’s natural growth rate—the sum of the Fed’s 2% inflation goal and the growth rate of potential output.
The resilience of the Fed’s monetary policy strategy could be enhanced, and such errors be avoided with guidance from a simple natural growth targeting rule that prescribes that the federal funds rate during each quarter be raised (cut) when projected nominal income growth exceeds (falls short) of the economy’s natural growth rate. An illustration with real-time data and forecasts since the early 1990s shows that Fed policy has not persistently deviated from this simple rule with the notable exception of the period coinciding with the Fed’s post-pandemic policy error.
Highlights
• Pathways for a circular economy towards the EU goals require policy support that, in turn, requires legitimacy.
• Legitimacy is often contested in the public discourse at all phases in the technological innovation system.
• Legitimacy remains poorly understood for ‘in-between’ technologies that struggle to move from the formative to the growth stage.
• The article explores legitimacy for chemical recycling primarily based on evidence from the UK, Germany, and Italy.
Abstract
The European Commission aims to increase the recycling of plastic packaging to 60% by 2025, requiring fundamental changes towards a more circular economy. Pathways for this transition require policy support that largely depends on their legitimacy in the public discourse. These normative aspects remain poorly understood for ‘in-between’ technologies, i.e., technologies that are no longer novel but struggle to move to the growth phase within the technological innovation system. Therefore, we ask: How do discourses shape technology legitimacy for in-between technologies? Drawing on the empirical example of chemical recycling, the analysis renders two principal findings. First, legitimising and delegitimising storylines present contesting views on in-between technologies regarding their technological aspects, environmental and social impacts, and economic and policy implications. Second, how discourses contribute to technology legitimacy depends on the actors and interests that drive the prevalent storylines in particular contexts.
Highlights
• Six Newton methods for solving matrix quadratic equations in linear DSGE models.
• Compared to QZ using 99 different DSGE models including Smets and Wouters (2007).
• Newton methods more accurate than QZ with comparable computation burden.
• Apt for refining solutions from alternative methods or nearby parameterizations.
Abstract
This paper presents and compares Newton-based methods from the applied mathematics literature for solving the matrix quadratic that underlies the recursive solution of linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that Newton-based methods compare favorably in solving DSGE models, providing higher accuracy as measured by the forward error of the solution at a comparable computation burden. The methods, however, suffer from their inability to guarantee convergence to a particular, e.g. unique stable, solution, but their iterative procedures lend themselves to refining solutions either from different methods or parameterizations.
In a unifying framework generalizing established theories we characterize under which conditions Joint Ownership of assets creates the best cooperation incentives in a partnership. We endogenise renegotiation costs and assume that they weakly increase with additional assets. A salient sufficient condition for optimal cooperation incentives among patient partners is if Joint Ownership is a Strict Coasian Institution for which transaction costs impede an efficient asset reallocation after a breakdown. In contrast to Halonen (2002) the logic behind our results is that Joint Ownership maximizes the value of the relationship and the costs of renegotiating ownership after a broken relationship.
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.
In its first ten years (2014-2023), the banking union was successful in its prudential agenda but failed spectacularly in its underlying objective: establishing a single banking market in the euro area. This goal is now more important than ever, and easier to attain than at any time in the last decade. To make progress, cross-border banks should receive a specific treatment within general banking union legislation. Suggestions are made on how to make such regulatory carve-out effective and legally sound.
The Eurosystem and the Deutsche Bundesbank will incur substantial losses in 2023 that are likely to persist for several years. Due to the massive purchases of securities in the last 10 years, especially of government bonds, the banks' excess reserves have risen sharply. The resulting high interest payments to the banks since the turnaround in monetary policy, with little income for the large-scale securities holdings, led to massive criticism. The banks were said to be making "unfair" profits as a result, while the fiscal authorities had to forego the previously customary transfers of central bank profits. Populist demands to limit bank profits by, for example, drastically increasing the minimum reserve ratios in the Eurosystem to reduce excess reserves are creating new severe problems and are neither justified nor helpful. Ultimately, the EU member states have benefited for a very long time from historically low interest rates because of the Eurosystem's extraordinary loose monetary policy and must now bear the flip side consequences of the massive expansion of central bank balance sheets during the necessary period of monetary policy normalisation.
This cumulative dissertation contains four self-contained chapters on stochastic games and learning in intertemporal choice.
Chapter 1 presents an experiment on value learning in a setting where actions have both immediate and delayed consequences. Subjects make a series of choices between abstract options, with values that have to be learned by sampling. Each option is associated with two payoff components: One is revealed immediately after the choice, the other with one round delay. Objectively, both payoff components are equally important, but most subjects systematically underreact to the delayed consequences. The resulting behavior appears impatient or myopic. However, there is no inherent reason to discount: All rewards are paid simultaneously, after the experiment. Elicited beliefs on the value of options are in accordance with choice behavior. These results demonstrate that revealed impatience may arise from frictions in learning, and that discounting does not necessarily reflect deep time preferences. In a treatment variation, subjects first learn passively from the evidence generated by others, before then making a series of own choices. Here, the underweighting of delayed consequences is attenuated, in particular for the earliest own decisions. Active decision making thus seems to play an important role in the emergence of the observed bias.
Chapter 2 introduces and proves existence of Markov quantal response equilibrium (QRE), an application of QRE to finite discounted stochastic games. We then study a specific case, logit Markov QRE, which arises when players react to total discounted payoffs using the logit choice rule with precision parameter λ. We show that the set of logit Markov QRE always contains a smooth path that leads from the unique QRE at λ = 0 to a stationary equilibrium of the game as λ goes to infinity. Following this path allows to solve arbitrary finite discounted stochastic games numerically; an implementation of this algorithm is publicly available as part of the package sgamesolver. We further show that all logit Markov QRE are ε-equilibria, with a bound for ε that is independent of the payoff function of the game and decreases hyperbolically in λ. Finally, we establish a link to reinforcement learning, by characterizing logit Markov QRE as the stationary points of a game dynamic that arises when all players follow the well-established reinforcement learning algorithm expected SARSA.
Chapter 3 introduces the logarithmic stochastic tracing procedure, a homotopy method to compute stationary equilibria for finite and discounted stochastic games. We build on the linear stochastic tracing procedure (Herings and Peeters 2004), but introduce logarithmic penalty terms as a regularization device, which brings two major improvements. First, the scope of the method is extended: it now has a convergence guarantee for all games of this class, rather than just generic ones. Second, by ensuring a smooth and interior solution path, computational performance is increased significantly. A ready-to-use implementation is publicly available. As demonstrated here, its speed compares quite favorable to other available algorithms, and it allows to solve games of considerable size in reasonable times. Because the method involves the gradual transformation of a prior into equilibrium strategies, it is possible to search the prior space and uncover potentially multiple equilibria and their respective basins of attraction. This also connects the method to established theory of equilibrium selection.
Chapter 4 introduces sgamesolver, a python package that uses the homotopy method to compute stationary equilibria of finite discounted stochastic games. A short user guide is complemented with discussion of the homotopy method, the two implemented homotopy functions logit Markov QRE and logarithmic tracing, and the predictor-corrector procedure and its implementation in sgamesolver. Basic and advanced use cases are demonstrated using several example games. Finally, we discuss the topic of symmetries in stochastic games.
Central banks sowing the seeds for a green financial sector? NGFS membership and market reactions
(2024)
In December 2017, during the One Planet Summit in Paris, a group of eight central banks and supervisory authorities launched the “Network for Greening the Financial Sector” (NGFS) to address challenges and risks posed by climate change to the global financial system. Until 06/2023 an additional 69 central banks from all around the world have joined the network. We find that the propensity to join the network can be described as a function in the country’s economic development (e.g., GDP per capita), national institutions (e.g., central bank independence), and performance of the central bank on its mandates (e.g., price stability and output gap). Using an event study design to examine consequences of network expansions in capital markets, we document that a difference portfolio that is long in clean energy stocks and short in fossil fuel stocks benefits from an enlargement of the NGFS. Overall, our results suggest that an increasing number of central banks and supervisory authorities are concerned about climate change and willing to go beyond their traditional objectives, and that the capital market believes they will do so.
By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.
The pricing of digital art
(2023)
The intersection of recent advancements in generative artificial intelligence and blockchain technology has propelled digital art into the spotlight. Digital art pricing recognizes that owners derive utility beyond the artwork’s inherent value. We incorporate the consumption utility associated with digital art and model the stochastic discount factor and risk premiums. Furthermore, we conduct a calibration analysis to analyze the effects of shifts in the real and digital economy. Higher returns are required in a digital market upswing due to increased exposure to systematic risk and digital art prices are especially responsive to fluctuations in business cycles within digital markets.
Using a field study at a German brokerage, we investigate advised individual investors’ behavior and outcomes after self-selecting into a flat-fee scheme (percentage of portfolio value) for mutual funds. In a difference-in-differences setting, we compare 699 switchers to propensity-score-matched advisory clients who remained in the commission-based scheme. Switchers increase their portfolio values, improve portfolio diversification, and increase their portfolio performance. They also demand more financial advice and follow more advisor recommendations. We argue that switchers attribute a higher quality to the unchanged advisory services.
We study the role mutual funds play in the recovery from fast intraday crashes based on data from the National Stock Exchange of India for a single large stock. During normal times, trading activity and liquidity provision by mutual funds is negligible compared to other traders at around 4% of overall activity. Nevertheless, for the two intraday market-wide crashes in our sample, price recovery took place only after mutual funds moved in. Market stability may require the presence of well-capitalized standby liquidity providers for recovery from fast crashes.
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.
Can consumption-based mechanisms generate positive and time-varying real term premia as we see in the data? I show that only models with time-varying risk aversion or models with high consumption risk can independently produce these patterns. The latter explanation has not been analysed before with respect to real term premia, and it relies on a small group of investors exposed to high consumption risk. Additionally, it can give rise to a “consumption-based arbitrageur” story of term premia. In relation to preferences, I consider models with both time-separable and recursive utility functions. Specifically for recursive utility, I introduce a novel perturbation solution method in terms of the intertemporal elasticity of substitution. This approach has not been used before in such models, it is easy to implement, and it allows a wide range of values for the parameter of intertemporal elasticity of substitution.
The complexities of geopolitical events, financial and fiscal crises, and the ebb and flow of personal life circumstances can weigh heavily on individuals’ minds as they make critical economic decisions. To investigate the impact of cognitive load on such decisions, the authors conducted an incentivized online experiment involving a representative sample of 2,000 French households. The results revealed that exposure to a taxing and persistent cognitive load significantly reduced consumption, particularly for individuals under the threat of furlough, while simultaneously increasing their account balances, particularly for those not facing such employment uncertainty. These effects were not driven by supply constraints or a worsening of credit constraints. Instead, cognitive load primarily affected the optimality of the chosen policy rules and impaired the ability of the standard economic model to accurately predict consumption patterns, although this effect was less pronounced among college-educated subjects
We investigate how unconventional monetary policy, via central banks’ purchases of corporate bonds, unfolds in credit-saturated markets. While this policy results in a loosening of credit market conditions as intended by policymakers, we report two unintended side effects. First, the policy impacts the allocation of credit among industries. Affected banks reallocate loans from investment-grade firms active on bond markets almost entirely to real estate asset managers. Other industries do not obtain more loans, particularly real estate developers and construction firms. We document an increase in real estate prices due to this policy, which fuels real estate overvaluation. Second, more loan write-offs arise from lending to these firms, and banks are not compensated for this risk by higher interest rates. We document a drop in bank profitability and, at the same time, a higher reliance on real estate collateral. Our findings suggest that central banks’ quantitative easing has substantial adverse effects in credit-saturated economies.
We conduct a field experiment with clients of a German universal bank to explore the impact of peer information on sustainable retail investments. Our results show that infor-mation about peers’ inclination towards sustainable investing raises the amount allocated to stock funds labeled sustainable, when communicated during a buying decision. This effect is primarily driven by participants initially underestimating peers’ propensity to invest sustainably. Further, treated individuals indicate an increased interest in addi-tional information on sustainable investments, primarily on risk and return expectations. However, when analyzing account-level portfolio holding data over time, we detect no spillover effects of peer information on later sustainable investment decisions.
Many consumers care about climate change and other externalities associated with their purchases. We analyze the behavior and market effects of such “socially responsible consumers” in three parts. First, we develop a flexible theoretical framework to study competitive equilibria with rational consequentialist consumers. In violation of price taking, equilibrium feedback non-trivially dampens a consumer’s mitigation efforts, undermining responsible behavior. This leads to a new type of market failure, where even consumers who fully “internalize the externality” overconsume externality-generating goods. At the same time, socially responsible consumers change the relative effectiveness of taxes, caps, and other policies in lowering the externality. Second, since consumer beliefs about and preferences over dampening play a crucial role in our framework, we investigate them empirically via a tailored survey. Consistent with our model, consumers are predominantly consequentialist, and on average believe in dampening. Inconsistent with our model, however, many consumers fail to anticipate dampening. Third, therefore, we analyze how such “naive” consumers modify our theoretical conclusions. Naive consumers behave more responsibly than rational consumers in a single-good economy, but may behave less responsibly in a multi-good economy with cross-market spillovers. A mix of naive and rational consumers may yield the worst outcomes.
This paper investigates stock market reaction to greenwashing by analyzing a new channel whereby companies change their names to green-related ones (i.e., names that evoke green and sustainable sentiments) to persuade the public that their activities are green. The findings reveal a striking positive stock price reaction to the announcement of corporate name changes to green-related names only for companies not involved in green activities at the time of the announcement. However, over an extended period of time, companies unrelated to green activities experience substantial negative abnormal returns if they fail to align their operational focus with the new name after the change.
How does group identity affect belief formation? To address this question, we conduct a series of online experiments with a representative sample of individuals in the US. Using the setting of the 2020 US presidential election, we find evidence of intergroup preference across three distinct components of the belief formation cycle: a biased prior belief, avoid-ance of outgroup information sources, and a belief-updating process that places greater (less) weight on prior (new) information. We further find that an intervention reducing the salience of information sources decreases outgroup information avoidance by 50%. In a social learn-ing context in wave 2, we find participants place 33% more weight on ingroup than outgroup guesses. Through two waves of interventions, we identify source utility as the mechanism driving group effects in belief formation. Our analyses indicate that our observed effects are driven by groupy participants who exhibit stable and consistent intergroup preferences in both allocation decisions and belief formation across all three waves. These results suggest that policymakers could reduce the salience of group and partisan identity associated with a policy to decrease outgroup information avoidance and increase policy uptake.
This paper applies structure preserving doubling methods to solve the matrix quadratic underlying the recursive solution of linear DSGE models. We present and compare two Structure-Preserving Doubling Algorithms ( SDAs) to other competing methods – the QZ method, a Newton algorithm, and an iterative Bernoulli approach – as well as the related cyclic and logarithmic reduction algorithms. Our comparison is completed using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that both SDAs perform very favorably relative to QZ, with generally more accurate solutions computed in less time. While we collect theoretical convergence results that promise quadratic convergence rates to a unique stable solution, the algorithms may fail to converge when there is a breakdown due to singularity of the coefficient matrices in the recursion. One of the proposed algorithms can overcome this problem by an appropriate (re)initialization. This SDA also performs particular well in refining solutions of different methods or from nearby parameterizations.
Whatever it takes to understand a central banker : embedding their words using neural networks
(2023)
Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. In this paper, the author propose a novel language model that is able to capture subtleties of messages such as one of the most famous sentences in central bank communications when ECB President Mario Draghi stated that "within [its] mandate, the ECB is ready to do whatever it takes to preserve the euro".
The authors utilize a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantication from computational linguistics. This allows them to provide high-quality central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. Their findings indicate that these deviations in communication significantly impact monetary policy actions, substantially reducing the reaction towards inflation deviation in the US.
Christine Laudenbach and Vincent Lindner: To promote financial education among children, young people, and adults in the long term, comprehensive information services must reach the entire population in Germany with the help of cooperation partners. Talking about finances can no longer be a taboo subject.
Standard applications of the consumption-based asset pricing model assume that goods and services within the nondurable consumption bundle are substitutes. We estimate substitution elasticities between different consumption bundles and show that households cannot substitute energy consumption by consumption of other nondurables. As a consequence, energy consumption affects the pricing function as a separate factor. Variation in energy consumption betas explains a large part of the premia related to value, investment, and operating profitability. For example, value stocks are typically more energy-intensive than growth stocks and thus riskier, since they suffer more from the oil supply shocks that also affect households.
We propose a model with mean-variance foreign investors who exhibit a convex disutility associated to brown bond holdings. The model predicts that bond green premia should be smaller in economies with a closer financial account and highly volatile exchange rates. This happens because foreign intermediaries invest relatively less in such economies, and this lowers the marginal disutility of investing in polluting activities. We find strong empirical evidence in favor of this hypothesis using a global bond market dataset. Exchange rate volatility and financial account openness are thus able to explain the higher financing costs of green projects in emerging markets relative to advanced economies, especially when green bonds are denominated in local currency: a disadvantage that we can call the "green sin" of emerging economies.
This study looks at potential windfall profits for the four banking acquisitions in 2023. Based on accounting figures, an FT article states that a total of USD 44bn was left on the table. We see accounting figures as a misleading analysis. By estimating marked-based cumulative abnormal returns (CAR), we find positive abnormal returns in all four cases which when made quantifiable, are around half of the FT’s accounting figures. Furthermore, we argue that transparent auctions with enough bidders should be preferred to negotiated bank sales.
This document was provided/prepared by the Economic Governance and EMU Scrutiny Unit at the request of the ECON Committee.
This paper explores entrepreneurs’ initially intended exit strategies and compares them to their final exit paths using an inductive approach that builds on the grounded theory methodology. Our data shows that initially intended and final exit strategies differ among entrepreneurs. Two groups of entrepreneurs emerged from our data. The first group comprises entrepreneurs who financed their firms through equity investors. The second group is made up of entrepreneurs who financed their businesses solely with their own equities. Our data shows that the first group originally intended a financial harvest exit strategy and settled with this harvest exit strategy. The second group initially intended a stewardship exit strategy but did not succeed. We used the theory of planned behavior and the behavioral agency model to analyze our data. By examining our results from these two theoretical perspectives, our study explains how entrepreneurs’ exit intentions lead to their actual exit strategies.
This paper develops and implements a backward and forward error analysis of and condition numbers for the numerical stability of the solutions of linear dynamic stochastic general equilibrium (DSGE) models. Comparing seven different solution methods from the literature, I demonstrate an economically significant loss of accuracy specifically in standard, generalized Schur (or QZ) decomposition based solutions methods resulting from large backward errors in solving the associated matrix quadratic problem. This is illustrated in the monetary macro model of Smets and Wouters (2007) and two production-based asset pricing models, a simple model of external habits with a readily available symbolic solution and the model of Jermann (1998) that lacks such a symbolic solution - QZ-based numerical solutions miss the equity premium by up to several annualized percentage points for parameterizations that either match the chosen calibration targets or are nearby to the parameterization in the literature. While the numerical solution methods from the literature failed to give any indication of these potential errors, easily implementable backward-error metrics and condition numbers are shown to successfully warn of such potential inaccuracies. The analysis is then performed for a database of roughly 100 DSGE models from the literature and a large set of draws from the model of Smets and Wouters (2007). While economically relevant errors do not appear pervasive from these latter applications, accuracies that differ by several orders of magnitude persist.
Product aesthetics is a powerful means for achieving competitive advantage. Yet most studies to date have focused on the role of aesthetics in shaping pre-purchase preferences and have failed to consider how product aesthetics affects post-purchase processes and consumers' usage behavior. This research focuses on the relationship between aesthetics and usage behavior in the context of durable products. Studies 1A to 1C provide evidence of a positive effect of product aesthetics on usage intensity using market data from the car and the fashion industries. Study 2 corroborates these findings and shows that the more intensive use of highly aesthetic products may lead to the acquisition of product-specific usage skills that form the basis for a cognitive lock-in. Hence, consumers are less likely to switch away from products with appealing designs, an effect that is labeled as the ‘aesthetic fidelity’ effect. Study 3 addresses an alternative explanation for the ‘aesthetic fidelity effect’ based on mood and motivation but finds that the ‘aesthetic fidelity’ effect is indeed determined by usage intensity. Finally, Study 4 identifies a boundary condition of the positive effect of product aesthetics on product usage, showing that it is limited to durable products. In sum, this research demonstrates that the effects of product aesthetics extend beyond the pre-consumption stage and have an enduring impact on people's consumption experiences.