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We collect data on the size distribution of all U.S. corporate businesses for 100 years. We document that corporate concentration (e.g., asset share or sales share of the top 1%) has increased persistently over the past century. Rising concentration was stronger in manufacturing and mining before the 1970s, and stronger in services, retail, and wholesale after the 1970s. Furthermore, rising concentration in an industry aligns closely with investment intensity in research and development and information technology. Industries with higher increases in concentration also exhibit higher output growth. The long-run trends of rising corporate concentration indicate increasingly stronger economies of scale.
Consumers purchase energy in many forms. Sometimes energy goods are consumed directly, for instance, in the form of gasoline used to operate a vehicle, electricity to light a home, or natural gas to heat a home. At other times, the cost of energy is embodied in the prices of goods and services that consumers buy, say when purchasing an airline ticket or when buying online garden furniture made from plastic to be delivered by mail. Previous research has focused on quantifying the pass-through of the price of crude oil or the price of motor gasoline to U.S. inflation. Neither approach accounts for the fact that percent changes in refined product prices need not be proportionate to the percent change in the price of oil, that not all energy is derived from oil, and that the correlation of price shocks across energy markets is far from one. This paper develops a vector autoregressive model that quantifies the joint impact of shocks to several energy prices on headline and core CPI inflation. Our analysis confirms that focusing on gasoline price shocks alone will underestimate the inflationary pressures emanating from the energy sector, but not enough to overturn the conclusion that much of the observed increase in headline inflation in 2021 and 2022 reflected non-energy price shocks.
This paper examines optimal enviromental policy when external financing is costly for firms. We introduce emission externalities and industry equilibrium in the Holmström and Tirole (1997) model of corporate finance. While a cap-and- trading system optimally governs both firms` abatement activities (internal emission margin) and industry size (external emission margin) when firms have sufficient internal funds, external financing constraints introduce a wedge between these two objectives. When a sector is financially constrained in the aggregate, the optimal cap is strictly above the Pigouvian benchmark and emission allowances should be allocated below market prices. When a sector is not financially constrained in the aggregate, a cap that is below the Pigiouvian benchmark optimally shifts market share to less polluting firms and, moreover, there should be no "grandfathering" of emission allowances. With financial constraints and heterogeneity across firms or sectors, a uniform policy, such as a single cap-and-trade system, is typically not optimal.
Enabling cybersecurity and protecting personal data are crucial challenges in the development and provision of digital service chains. Data and information are the key ingredients in the creation process of new digital services and products. While legal and technical problems are frequently discussed in academia, ethical issues of digital service chains and the commercialization of data are seldom investigated. Thus, based on outcomes of the Horizon2020 PANELFIT project, this work discusses current ethical issues related to cybersecurity. Utilizing expert workshops and encounters as well as a scientific literature review, ethical issues are mapped on individual steps of digital service chains. Not surprisingly, the results demonstrate that ethical challenges cannot be resolved in a general way, but need to be discussed individually and with respect to the ethical principles that are violated in the specific step of the service chain. Nevertheless, our results support practitioners by providing and discussing a list of ethical challenges to enable legally compliant as well as ethically acceptable solutions in the future.
Central banks have faced a succession of crises over the past years as well as a number of structural factors such as a transition to a greener economy, demographic developments, digitalisation and possibly increased onshoring. These suggest that the future inflation environment will be different from the one we know. Thus uncertainty about important macroeconomic variables and, in particular, inflation dynamics will likely remain high.
This note argues that in a situation of an inelastic natural gas supply a restrictive monetary policy in the euro zone could reduce the energy bill and therefore has additional merits. A more hawkish monetary policy may be able to indirectly use monopsony power on the gas market. The welfare benefits of such a policy are diluted to the extent that some of the supply (approximately 10 percent) comes from within the euro zone, which may give rise to distributional concerns.
The Russian war of aggression against Ukraine since 24 February 2022 has intensified the discussion of Europe’s reliance on energy imports from Russia. A ban on Russian imports of oil, natural gas and coal has already been imposed by the United States, while the United Kingdom plans to cease imports of oil and coal from Russia by the end of 2022. The German Federal Government is currently opposing an energy embargo against Russia. However, the Federal Ministry for Economic Affairs and Climate Action is working on a strategy to reduce energy imports from Russia. In this paper, the authors give an overview of the German and European reliance on energy imports from Russia with a focus on gas imports and discuss price effects, alternative suppliers of natural gas, and the potential for saving and replacing natural gas. They also provide an overview of estimates of the consequences on the economic outlook if the conflict intensifies.
High-frequency changes in interest rates around FOMC announcements are an important tool for identifying the effects of monetary policy on asset prices and the macroeconomy. However, some recent studies have questioned both the exogeneity and the relevance of these monetary policy surprises as instruments, especially for estimating the macroeconomic effects of monetary policy shocks. For example, monetary policy surprises are correlated with macroeconomic and financial data that is publicly available prior to the FOMC announcement. The authors address these concerns in two ways: First, they expand the set of monetary policy announcements to include speeches by the Fed Chair, which essentially doubles the number and importance of announcements in our dataset. Second, they explain the predictability of the monetary policy surprises in terms of the “Fed response to news” channel of Bauer and Swanson (2021) and account for it by orthogonalizing the surprises with respect to macroeconomic and financial data. Their subsequent reassessment of the effects of monetary policy yields two key results: First, estimates of the high-frequency effects on financial markets are largely unchanged. Second, estimates of the macroeconomic effects of monetary policy are substantially larger and more significant than what most previous empirical studies have found.
This study examines the recent literature on the expectations, beliefs and perceptions of investors who incorporate Environmental, Social, Governance (ESG) considerations in investment decisions with the aim to generate superior performance and also make a societal impact. Through the lens of equilibrium models of agents with heterogeneous tastes for ESG investments, green assets are expected to generate lower returns in the long run than their non- ESG counterparts. However, at the short run, ESG investment can outperform non-ESG investment through various channels. Empirically, results of ESG outperformance are mixed. We find consensus in the literature that some investors have ESG preference and that their actions can generate positive social impact. The shift towards more sustainable policies in firms is motivated by the increased market values and the lower cost of capital of green firms driven by investors’ choices.
The importance of agile methods has increased in recent years, not only to manage IT projects but also to establish flexible and adaptive organisational structures, which are essential to deal with disruptive changes and build successful digital business strategies. This paper takes an industry-specific perspective by analysing the dissemination, objectives and relative popularity of agile frameworks in the German banking sector. The data provides insights into expectations and experiences associated with agile methods and indicates possible implementation hurdles and success factors. Our research provides the first comprehensive analysis of agile methods in the German banking sector. The comparison with a selected number of fintechs has revealed some differences between banks and fintechs. We found that almost all banks and fintechs apply agile methods in IT projects. However, fintechs have relatively more experience with agile methods than banks and use them more intensively. Scrum is the most relevant framework used in practice. Scaled agile frameworks are so far negligible in the German banking sector. Acceleration of projects is apparently the most important objective of deploying agile methods. In addition, agile methods can contribute to cost savings and lead to improved quality and innovation performance, though for banks it is evidently more challenging to reach their respective targets than for fintechs. Overall our findings suggest that German banks are still in a maturing process of becoming more agile and that there is room for an accelerated adoption of agile methods in general and scaled agile frameworks in particular.
Are sanctions sustainable?
(2022)
Employing the art-collection records of Burton and Emily Hall Tremaine, we consider whether early-stage art investors can be understood as venture capitalists. Because the Tremaines bought artists’ work very close to an artwork’s creation, with 69% of works in our study purchased within one year of the year when they were made, their collecting practice can best be framed as venture-capital investment in art. The Tremaines also illustrate art collecting as social-impact investment, owing to their combined strategy of art sales and museum donations for which the collectors received a tax credit under US rules. Because the Tremaines’ museum donations took place at a time that U.S. marginal tax rates from 70% to 91%, the near “donation parity” with markets, creating a parallel to ESG investment in the management of multiple forms of value.
With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual’s decisions on the algorithm. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one’s and others’ fate.
In more and more situations, artificially intelligent algorithms have to model humans’ (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do individuals adjust the selfishness or prosociality of their behavior when it is common knowledge that their actions produce various externalities through the training of an algorithm? In an online experiment, we let participants’ choices in dictator games train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We show that individuals who are aware of the consequences of their training on the pay- offs of a future generation behave more prosocially, but only when they bear the risk of being harmed themselves by future algorithmic choices. In that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present.
Since the 2008 financial crisis, European largest banks’ size and business models have largely remained unchallenged. Is that because of banks’ continued structural power over States? This paper challenges the view that States are sheer hostages of banks’ capacity to provide credit to the real economy – which is the conventional definition of structural power. Instead, it sheds light on the geo-economic dimension of banks’ power: key public officials conceive the position of “their own” market-based banks in global financial markets as a crucial dimension of State power. State priority towards banking thus result from political choices over what structurally matters the most for the State. Based on a discourse analysis of parliamentary debates in France, Germany and Spain between 2010 and 2020 as well as on a comparative analysis of the implementation of a special tax on banks in the early 2010s, this paper shows that State’s Finance ministries tend to prioritize geo-economic considerations over credit to firms. By contrast, Parliaments tend to prioritize investment. Power dynamics within the State thus largely shape political priorities towards banking at the domestic and international levels.
Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
Lack of privacy due to surveillance of personal data, which is becoming ubiquitous around the world, induces persistent conformity to the norms prevalent under the surveillance regime. We document this channel in a unique laboratory---the widespread surveillance of private citizens in East Germany. Exploiting localized variation in the intensity of surveillance before the fall of the Berlin Wall, we show that, at the present day, individuals who lived in high-surveillance counties are more likely to recall they were spied upon, display more conformist beliefs about society and individual interactions, and are hesitant about institutional and social change. Social conformity is accompanied by conformist economic choices: individuals in high-surveillance counties save more and are less likely to take out credit, consistent with norms of frugality. The lack of differences in risk aversion and binding financial constraints by exposure to surveillance helps to support a beliefs channel.
Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.
We employ a proprietary transaction-level dataset in Germany to examine how capital requirements affect the liquidity of corporate bonds. Using the 2011 European Banking Authority capital exercise that mandated certain banks to increase regulatory capital, we find that affected banks reduce their inventory holdings, pre-arrange more trades, and have smaller average trade size. While non-bank affiliated dealers increase their market-making activity, they are unable to bridge this gap - aggregate liquidity declines. Our results are stronger for banks with a higher capital shortfall, for non-investment grade bonds, and for bonds where the affected banks were the dominant market-maker.
We develop a two-sector incomplete markets integrated assessment model to analyze the effectiveness of green quantitative easing (QE) in complementing fiscal policies for climate change mitigation. We model green QE through an outstanding stock of private assets held by a monetary authority and its portfolio allocation between a clean and a dirty sector of production. Green QE leads to a partial crowding out of private capital in the green sector and to a modest reduction of the global temperature by 0.04 degrees of Celsius until 2100. A moderate global carbon tax of 50 USD per tonne of carbon is 4 times more effective.
The great financial crisis and the euro area crisis led to a substantial reform of financial safety nets across Europe and – critically – to the introduction of supranational elements. Specifically, a supranational supervisor was established for the euro area, with discrete arrangements for supervisory competences and tasks depending on the systemic relevance of supervised credit institutions. A resolution mechanism was created to allow the frictionless resolution of large financial institutions. This resolution mechanism has been now complemented with a funding instrument.
While much more progress has been achieved than most observers could imagine 12 years ago, the banking union remains unfinished with important gaps and deficiencies. The experience over the past years, especially in the area of crisis management and resolution, has provided impetus for reform discussions, as reflected most lately in the Eurogroup statement of 16 June 2022.
This Policy Insight looks primarily at the current and the desired state of the banking union project. The key underlying question, and the focus here, is the level of ambition and how it is matched with effective legal and regulatory tools. Specifically, two questions will structure the discussions:
What would be a reasonable definition and rationale for a ‘complete’ banking union? And what legal reforms would be required to achieve it?
Banking union is a case of a new remit of EU-level policy that so far has been established on the basis of long pre-existing treaty stipulations, namely, Article 127(6) TFEU (for banking supervision) and Article 114 TFEU (for crisis management and deposit insurance). Could its completion be similarly carried out through secondary law? Or would a more comprehensive overhaul of the legal architecture be required to ensure legal certainty and legitimacy?
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
Large companies are increasingly on trial. Over the last decade, many of the world’s biggest firms have been embroiled in legal disputes over corruption charges, financial fraud, environmental damage, taxation issues or sanction violations, ending in convictions or settlements of record-breaking fines, well above the billion-dollar mark. For critics of globalization, this turn towards corporate accountability is a welcome sea-change showing that multinational companies are no longer above the law. For legal experts, the trend is noteworthy because of the extraterritorial dimensions of law enforcement, as companies are increasingly held accountable for activities independent of their nationality or the place of the activities. Indeed, the global trend required understanding the evolution of corporate criminal law enforcement in the United States in particular, where authorities have skillfully expanded its effective jurisdiction beyond its territory. This paper traces the evolution of corporate prosecutions in the United States. Analyzing federal prosecution data, it then shows that foreign firms are more likely to pay a fine, which is on average 6,6 times larger.
Energy efficiency represents one of the key planned actions aiming at reducing greenhouse emissions and the consumption of fossil fuel to mitigate the impact of climate change. In this paper, we investigate the relationship between energy efficiency and the borrower’s solvency risk in the Italian market. Specifically, we analyze a residential mortgage portfolio of four financial institutions which includes about 70,000 loans matched with the energy performance certificate of the associated buildings. Our findings show that there is a negative relationship between a building’s energy efficiency and the owner’s probability of default. Findings survive after we account for dwelling, household, mortgage, market control variables, and regional and year fixed effect. Additionally, a ROC analysis shows that there is an improvement in the estimation of the mortgage default probability when the energy efficiency characteristic is included as a risk predictor in the model.
Aus hundertseitigen, oftmals verstaubten Büchern zieht er mittels computerlinguistischer Methoden hochspannende Datensätze: Prof. Dr. Alexander Hillert koordiniert als Professor für Finance und Data Science das SAFE-Forschungsdatenzentrum. Jungen Forscherinnen und Forschern eine fundierte Methoden- und Datenkompetenz zu vermitteln, ist ihm ein großes Anliegen.
This policy note summarizes our assessment of financial sanctions against Russia. We see an increase in sanctions severity starting from (1) the widely discussed SWIFT exclusions, followed by (2) blocking of correspondent banking relationships with Russian banks, including the Central Bank, alongside secondary sanctions, and (3) a full blacklisting of the ‘real’ export-import flows underlying the financial transactions. We assess option (1) as being less impactful than often believed yet sending a strong signal of EU unity; option (2) as an effective way to isolate the Russian banking system, particularly if secondary sanctions are in place, to avoid workarounds. Option (3) represents possibly the most effective way to apply economic and financial pressure, interrupting trade relationships.
Der Koalitionsvertrag 2021 sieht eine generationengerechte Absicherung des Rentenniveaus durch eine teilweise aus Haushaltsmitteln finanzierte Kapitaldeckung vor. Um dieses Ziel zu verwirklichen, wird hier die Einführung einer Generationenrente ab Geburt vorgeschlagen. Dabei wird aus Haushaltsmitteln ein Betrag von € 5.000 für jedes Neugeborene nach Grundsätzen des professionellen Anlagemanagements am globalen Kapitalmarkt angelegt. Konzeptionell soll sich diese Generationenrente am Modell der Basisrente(§10 Abs. 1 Nr. 2 b EStG) orientieren, d.h. die akkumulierten Gelder sind weder beleihbar, vererbbar noch übertragbar und können frühestens ab Alter 63 zugunsten einer lebenslangen Monatsrente verwendet werden. Unsere Berechnungen zeigen, dass durch die hier vorgeschlagene Generationenrente unabhängig vom Verlauf der individuellen Erwerbsbiographie, Altersarmut für die vom demographischen Wandel besonders betroffenen zukünftigen Generationen vermieden wird.
Financial literacy affects wealth accumulation, and pension planning plays a key role in this relationship. In a large field experiment, we employ a digital pension aggregation tool to confront a treatment group with a simplified overview of their current pension claims across all pillars of the pension system. We combine survey and administrative bank data to measure the effects on actual saving behavior. Access to the tool decreases pension uncertainty for treated individuals. Average savings increase - especially for the financially less literate. We conclude that simplification of pension information can potentially reduce disparities in pension planning and savings behavior.
Financial ties between drug companies and medical researchers are thought to bias results published in medical journals. To enable readers to account for such bias, most medical journals require authors to disclose potential conflicts of interest. For such policies to be effective, conflict disclosure must modify readers’ beliefs. We therefore examine whether disclosure of financial ties with industry reduces article citations, indicating a discount. A challenge to estimating this effect is selection as drug companies may seek out higher quality authors as consultants or fund their studies, generating a positive correlation between disclosed conflicts and citations. Our analysis confirms this positive association. Including observable controls for article and author quality attenuates but does not eliminate this relation. To tease out whether other researchers discount articles with conflicts, we perform three tests. First, we show that the positive association is weaker for review articles, which are more susceptible to bias. Second, we examine article recommendations to family physicians by medical experts, who choose from articles that are a priori more homogenous in quality. Here, we find a significantly negative association between disclosure and expert recommendations, consistent with discounting. Third, we conduct an analysis within author and article, exploiting journal policy changes that result in conflict disclosure by an author. We examine the effect of this disclosure on citations to a previously published article by the same author. This analysis reveals a negative citation effect. Overall, we find evidence that disclosures negatively affect citations, consistent with the notion that other researchers discount articles with disclosed conflicts.
Previous studies document a relationship between gambling activity at the aggregate level and investments in securities with lottery-like features. We combine data on individual gambling consumption with portfolio holdings and trading records to examine whether gambling and trading act as substitutes or complements. We find that gamblers are more likely than the average investor to hold lottery stocks, but significantly less likely than active traders who do not gamble. Our results suggest that gambling behavior across domains is less relevant compared to other portfolio characteristics that predict investing in high-risk and high-skew securities, and that gambling on and off the stock market act as substitutes to satisfy the same need, e.g., sensation seeking.
Venture capital (VC) funds backed by large multi-fund families tend to perform substantially better due to cross-fund cash flows (CFCFs), a liquidity support mechanism provided by matching distributions and capital calls within a VC fund family. The dynamics of this mechanism coincide with the sensitivity of different stage projects owing to market liquidity conditions. We find that the early-stage funds demand relatively more intra-family CFCFs than later-stage funds during liquidity stress periods. We show that the liquidity improvement based on the timing of CFCF allocation reflects how fund families arrange internal liquidity provision and explains a large part of their outperformance.
We analyze how market fragmentation affects market quality of SME and other less actively traded stocks. Compared to large stocks, they are less likely to be traded on multiple venues and show, if at all, low levels of fragmentation. Concerning the impact of fragmentation on market quality, we find evidence for a hockey stick effect: Fragmentation has no effect for infrequently traded stocks, a negative effect on liquidity of slightly more active stocks, and increasing benefits for liquidity of large and actively traded stocks. Consequently, being traded on multiple venues is not necessarily harmful for SME stock market quality.
In the aftermath of the Wirecard scandal the German lead stock market index DAX has undergone a series of reforms, including the introduction of a profitability criterion based on EBITDA for new DAX members and enhanced financial reporting requirements with specified sanctions for non-compliance. Furthermore, DAX members need to adhere to certain provisions in the German Corporate Governance Code relating to audit committees. The final step of the reform was implemented in September 2021: the extension of the DAX from 30 to 40 constituents, with the ranking based solely on the free float market capitalisation. After one year of experience with the new design of the DAX, this paper concludes that the reform has strengthened the DAX in terms of diversification, quality and adaptability. However, there is still room for further improvement by introducing a minimum ESG score for DAX companies and thus making sustainability a relevant factor in the selection process. In addition, full compliance with the recommendations of the German Corporate Governance Code should be a condition for DAX companies. Furthermore, the profitability criterion should be applied on a continuous basis to ensure that loss-making companies can be excluded from the DAX after a grace period.
The author proposes a Differential-Independence Mixture Ensemble (DIME) sampler for the Bayesian estimation of macroeconomic models.It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may also be computationally expensive to evaluate. DIME is a “Swiss Army knife”, combining the advantages of a broad class of gradient-free global multi-start optimizers with the properties of a Monte Carlo Markov chain (MCMC). This includes fast burn-in and convergence absent any prior numerical optimization or initial guesses, good performance for multimodal distributions, a large number of chains (the “ensemble”) running in parallel, an endogenous proposal density generated from the state of the full ensemble, which respects the bounds of the prior distribution. The author shows that the number of parallel chains scales well with the number of necessary ensemble iterations.
DIME is used to estimate the medium-scale heterogeneous agent New Keynesian (“HANK”) model with liquid and illiquid assets, thereby for the first time allowing to also include the households’ preference parameters. The results mildly point towards a less accentuated role of household heterogeneity for the empirical macroeconomic dynamics.
The authors propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting. The second method transforms a quarterly state space into a monthly frequency. Their algorithm combines the advantages of these two existing approaches.They compare the new method with the existing methods using simulated data and real-world data. With simulated data, the new method outperforms all other methods, including forecasts from the standard quarterly model. With real world data, incorporating auxiliary variables as in their method substantially decreases forecasting errors for recessions, but casting the model in a monthly frequency delivers better forecasts in normal times.
For the academic audience, this paper presents the outcome of a well-identified, large change in the monetary policy rule from the lens of a standard New Keynesian model and asks whether the model properly captures the effects. For policymakers, it presents a cautionary tale of the dismal effects of ignoring basic macroeconomics. The Turkish monetary policy experiment of the past decade, stemming from a belief of the government that higher interest rates cause higher inflation, provides an unfortunately clean exogenous variance in the policy rule. The mandate to keep rates low, and the frequent policymaker turnover orchestrated by the government to enforce this, led to the Taylor principle not being satisfied and eventually a negative coeffcient on inflation in the policy rule. In such an environment, was the exchange rate still a random walk? Was inflation anchored? Does the “standard model”” suffice to explain the broad contours of macroeconomic outcomes in an emerging economy with large identifying variance in the policy rule? There are no surprises for students of open-economy macroeconomics; the answers are no, no, and yes.
In a parsimonious regime switching model, we find strong evidence that expected consumption growth varies over time. Adding inflation as a second variable, we uncover two states in which expected consumption growth is low, one with high and one with negative expected inflation. Embedded in a general equilibrium asset pricing model with learning, these dynamics replicate the observed time variation in stock return volatilities and stock- bond return correlations. They also provide an alternative derivation for a measure of time-varying disaster risk suggested by Wachter (2013), implying that both the disaster and the long-run risk paradigm can be extended towards explaining movements in the stock-bond correlation.
Are we in a new “Polanyian moment”? If we are, it is essential to examine how “spontaneous” and punctual expressions of discontent at the individual level may give rise to collective discourses driving social and political change. It is also important to examine whether and how the framing of these discourses may vary across political economies. This paper contributes to this endeavor with the analysis of anti-finance discourses on Twitter in France, Germany, Italy, Spain and the UK between 2019 and 2020. This paper presents three main findings. First, the analysis shows that, more than ten years after the financial crisis, finance is still a strong catalyzer of political discontent. Second, it shows that there are important variations in the dominant framing of public anti-finance discourses on social media across European political economies. If the antagonistic “us versus them” is prominent in all the cases, the identification of who “us” and “them” are, vary significantly. Third, it shows that the presence of far-right tropes in the critique of finance varies greatly from virtually inexistent to a solid minority of statements.
We study liquidity provision by competitive high-frequency trading firms (HFTs) in a dynamic trading model with private information. Liquidity providers face adverse selection risk from trading with privately informed investors and from trading with other HFTs that engage in latency arbitrage upon public information. The impact of the two different sources of risk depends on the details of the market design. We determine equilibrium transaction costs in continuous limit order book (CLOB) markets and under frequent batch auctions (FBA). In the absence of informed trading, FBA dominates CLOB just as in Budish et al. (2015). Surprisingly, this result does no longer hold with privately informed investors. We show that FBA allows liquidity providers to charge markups and earn profits – even under risk neutrality and perfect competition. A slight variation of the FBA design removes the inefficiency by allowing traders to submit orders conditional on auction excess demand.
Peer effects can lead to better financial outcomes or help propagate financial mistakes across social networks. Using unique data on peer relationships and portfolio composition, we show considerable overlap in investment portfolios when an investor recommends their brokerage to a peer. We argue that this is strong evidence of peer effects and show that peer effects lead to better portfolio quality. Peers become more likely to invest in funds when their recommenders also invest, improving portfolio diversification compared to the average investor and various placebo counterfactuals. Our evidence suggests that social networks can provide good advice in settings where individuals are personally connected.
Veronika Grimm, Lukas Nöh, and Volker Wieland assess the possible development of government interest expenditures as a share of GDP for Germany, France, Italy and Spain. Until 2021, these and other member states could anticipate a further reduction of interest expenditure in the future. This outlook has changed considerably with the recent surge in inflation and government bond rates. Nevertheless, under reasonable assumptions current yield curves still imply that interest expenditure relative to GDP can be stabilized at the current level. The authors also review the implications of a further upward shift in the yield curves of 1 or 2 percentage points. These implications suggest significant medium-term risks for highly indebted member states with interest expenditure approaching or exceeding levels last observed on the eve of the euro area debt crisis. In light of these risks, governments of euro area member states should take substantive action to achieve a sustained decline in debt-to-GDP ratios towards safer levels. They bear the responsibility for making sure that government finances can weather the higher interest rates which are required to achieve price stability in the euro area.
The financial sector plays an important role in financing the green transformation of the European economy. A critical assessment of the current regulatory framework for sustainable finance in Europe leads to ambiguous results. Although the level of transparency on ESG aspects of financial products has been significantly improved, it is questionable whether the complex, mainly disclosure-oriented architecture is sufficient to mobilise more private capital into sustainable investments. It should be discussed whether a minimum Taxonomy ratio or Green Asset Ratio has to be fulfilled to market a financial product as “green”. Furthermore, because of the high complexity of the regulation, it could be helpful for the understanding of private investors to establish a simplified green rating, based on the Taxonomy ratio, to facilitate the selection of green financial products.
The financial sector plays an important role in supporting the green transformation of the European economy. A critical assessment of the current regulatory framework for sustainable finance in Europe leads to ambiguous results. Although the level of transparency on environmental, social and governance aspects of financial products has improved significantly, it is questionable whether the complex, mainly disclosure-oriented architecture is sufficient to mobilise more private capital into sustainable investments. It should be discussed whether a minimum taxonomy ratio or Green Asset Ratio has to be fulfilled to market a financial product as “green”. Furthermore, because of the high complexity of the regulation, it could be helpful for private investors to establish a simplified green rating, based on the taxonomy ratio, to facilitate the selection of green financial products.
Despite the impressive success of deep neural networks in many application areas, neural network models have so far not been widely adopted in the context of volatility forecasting. In this work, we aim to bridge the conceptual gap between established time series approaches, such as the Heterogeneous Autoregressive (HAR) model (Corsi, 2009), and state-of-the-art deep neural network models. The newly introduced HARNet is based on a hierarchy of dilated convolutional layers, which facilitates an exponential growth of the receptive field of the model in the number of model parameters. HARNets allow for an explicit initialization scheme such that before optimization, a HARNet yields identical predictions as the respective baseline HAR model. Particularly when considering the QLIKE error as a loss function, we find that this approach significantly stabilizes the optimization of HARNets. We evaluate the performance of HARNets with respect to three different stock market indexes. Based on this evaluation, we formulate clear guidelines for the optimization of HARNets and show that HARNets can substantially improve upon the forecasting accuracy of their respective HAR baseline models. In a qualitative analysis of the filter weights learnt by a HARNet, we report clear patterns regarding the predictive power of past information. Among information from the previous week, yesterday and the day before, yesterday's volatility makes by far the most contribution to today's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly when moving further into the past.
We propose a new instrument for estimating the price elasticity of gasoline demand that exploits systematic differences across U.S. states in the pass-through of oil price shocks to retail gasoline prices. These differences, which are primarily driven by variation in the cost of producing and distributing gasoline, create cross-sectional dispersion in gasoline price growth in response to an aggregate oil price shock. We find that the elasticity was stable near -0.3 until the end of 2014, but subsequently rose to about -0.2. Our estimates inform the recent debate about gasoline-tax holidays and policies to reduce carbon emissions.
The authors study the impact of dissent in the ECB‘s Governing Council on uncertainty surrounding households‘ inflation expectations. They conduct a randomized controlled trial using the Bundesbank Online Panel Households. Participants are provided with alternative information treatments concerning the vote in the Council, e.g. unanimity and dissent, and are asked to submit probabilistic inflation expectations. The results show that the vote is informative.
Households revise their subjective inflation forecast after receiving information about the vote. Dissenting votes cause a wider individual distribution of future inflation. Hence, dissent increases households‘ uncertainty about inflation. This effect is statistically significant once the authors allow for the interaction between the treatments and individual characteristics of respondents.
The results are robust with respect to alternative measures of forecast uncertainty and hold for different model specifications. The findings suggest that providing information about dissenting votes without additional information about the nature of dissent is detrimental to coordinating household expectations.
he ECB is independent, but it is also accountable to the European parliament (EP). Yet, how the EP has held the ECB accountable has largely been overlooked. This paper starts addressing this gap by providing descriptive statistics of three accountability modalities. The paper highlights three findings. First, topics of accountability have changed. Climate-related accountability has increased quickly and dramatically since 2017. Second, if the relationship between price stability and climate change remains an object of conflict among MEPs, a majority within the EP has emerged to put pressure for the ECB to take a more active stance against climate change, precisely on behalf of its price stability mandate. Third, MEPs engage with the climate topic in very specific ways. There is a gender divide between the climate and the price stability topics. Women engage more actively with climate-related topics. While the Greens heavily dominate the climate topic, parties from the Right dominate the topic of Price stability. Finally, MEPs adopt a more united strategy and a particularly low confrontational tone in their climate-related interventions.
Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws.
Ad blockers allow users to browse websites without viewing ads. Online news publishers that rely on advertising income tend to perceive users’ adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads—which users presumably dislike—may affect users’ online news consumption behavior in positive ways. Using 3.1 million visits from 79,856 registered users on a news website, this research finds that ad blocker adoption has robust positive effects on the quantity and variety of articles users consume. Specifically, ad blocker adoption increases the number of articles that users read by 21.0%–43.2%, and it increases the number of content categories that users consume by 13.4%–29.1%. These effects are stronger for less-experienced users of the website. The increase in news consumption stems from increases in repeat visits to the news website, rather than in the number of page impressions per visit. These postadoption visits tend to start from direct navigation to the news website, rather than from referral sources. The authors discuss how news publishers could benefit from these findings, including exploring revenue models that consider users’ desire to avoid ads.
The authors study the effects of forward looking communication in an environment of rising inflation rates on German consumers‘ inflation expectations using a randomized control trial. They show that information about rising inflation increases short- and long-term inflation expectations. This initial increase in expectations can be mitigated using forward looking information about inflation. Among these information treatments, professional forecasters‘ projections seem to reduce inflation expectations by more than policymakers‘ characterization of inflation as a temporary phenomenon.
Many nations incentivize retirement saving by letting workers defer taxes on pension contributions, imposing them when retirees withdraw their funds. Using a dynamic life cycle model, we show how ‘Rothification’ – that is, taxing 401(k) contributions rather than payouts – alters saving, investment, consumption, and Social Security claiming patterns. We find that taxing pension contributions instead of withdrawals leads to delayed retirement, somewhat lower lifetime tax payments, and relatively small reductions in consumption. Indeed, the two tax regimes generate quite similar relative inequality metrics: the relative consumption inequality ratio under TEE is only four percent higher than in the EET case. Moreover, results indicate that the Gini measures are also strikingly similar under the EET and the TEE regimes for lifetime consumption, cash on hand, and 401(k) assets, differing by only 1-4 percent. While tax payments are higher early in life under the TEE regime, they are slightly lower in the long run. Moreover, higher EET tax payments are also accompanied by higher volatility. We therefore find few reasons for policymakers to favor either tax approach on egalitarian or revenue-enhancing grounds.
In the communication of the European Central Bank (ECB), the statement that „we act within our mandate“ is often referred to. Also among practitioners of the Eurosystem the term „mandate“ has become popular. In his Working Paper, Helmut Siekmann analyzes the legal foundation of the tasks and objectives of the Eurosysstem and price stability as a legal term. He finds that the primary law of the EU only very sparsely employs the term „mandate“. It is never used in the context of monetary policy and its institutions. Moreover, he comes to the conclusion that inflation targeting as a task, competence, or objective of the Eurosystem is legally highly questionable according to the common standards of interpretation.
This briefing paper describes and evaluates the law and economics of institution(al) protection schemes. Throughout our analysis, we use Europe’s largest such scheme, that of German savings banks, as paradigm. We find strengths and weaknesses: Strong network-internal monitoring and early warning seems to be an important contributor to IPS network success. Similarly, the geographical quasi-cartel encourages banks to build a strong client base, including SME, in all regions. Third, the growth of the IPS member institutions may have benefitted from the strictly unlimited protection offered, in terms of euro amounts per account holder. The counterweighing weaknesses encompass the conditionality of the protection pledge and the underinvestment risk it entails, sometimes referred to as blackmailing the government, as well as the limited diversification potential of the deposit insurance within the network, and the near-incompatibility of the IPS model with the provisions of the BRRD, particularly relating to bail-in and resolution. Consequently, we suggest, as policy guidance, to treat large IPS networks similar to large banking groups, and put them as such under the direct supervision of the ECB within the SSM. Moreover, we suggest strengthening the seriousness of a deposit insurance that offers unlimited protection. Finally, to improve financial stability, we suggest embedding the IPS model into a multi-tier deposit re-insurance scheme, with a national and a European layer. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee.
To ensure the credibility of market discipline induced by bail-in, neither retail investors nor peer banks should appear prominently among the investor base of banks’ loss absorbing capital. Empirical evidence on bank-level data provided by the German Federal Financial Supervisory Authority raises a few red flags. Our list of policy recommendations encompasses disclosure policy, data sharing among supervisors, information transparency on holdings of bail-inable debt for all stakeholders, threshold values, and a well-defined upper limit for any bail-in activity. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee.
This study analyzes information production and trading behavior of banks with lending relationships. We combine trade-by-trade supervisory data and credit-registry data to examine banks' proprietary trading in borrower stocks around a large number of corporate events. We find that relationship banks build up positive (negative) trading positions in the two weeks before events with positive (negative) news, even when these events are unscheduled, and unwind positions shortly after the event. This trading pattern is more pronounced in situations when banks are likely to possess private information about their borrowers, and cannot be explained by specialized expertise in certain industries or certain firms. The results suggest that banks' lending relationships inform their trading and underscore the potential for conflicts of interest in universal banking, which have been a prominent concern in the regulatory debate for a long time. Our analysis illustrates how combining large data sets can uncover unusual trading patterns and enhance the supervision of financial institutions.
The loan impairment rules recently introduced by IFRS 9 require banks to estimate their future credit losses by using forward-looking information. We use supervisory loan-level data from Germany to investigate how banks apply their reporting discretion and adjust their lending upon the announcement of the new rules. Our identification strategy exploits a cut-off for the level of provisions at the investment grade threshold based on banks’ internal rating of a borrower. We find that banks required to adopt the new rules assign better internal ratings to exactly the same borrowers compared to banks that do not apply IFRS 9 around this cut-off. This pattern is consistent with a strategic use of the increased reporting discretion that is inherent to rules requiring forward-looking loss estimation. At the same time, banks also reduce their lending exposure to exactly those borrowers at the highest risk of experiencing a rating downgrade below the cutoff. These loans would be associated with additional provisions in future periods, both in the intensive and extensive margin. The lending change thus mitigates some of the negative effects of increased reporting opportunism on banks’ crisis resilience. However, when these firms with internal ratings around the investment grade cut-off obtain less external funding through banks, the introduction of IFRS 9 will likely also be associated with real economic effects
Linear rational-expectations models (LREMs) are conventionally "forwardly" estimated as follows. Structural coefficients are restricted by economic restrictions in terms of deep parameters. For given deep parameters, structural equations are solved for "rational-expectations solution" (RES) equations that determine endogenous variables. For given vector autoregressive (VAR) equations that determine exogenous variables, RES equations reduce to reduced-form VAR equations for endogenous variables with exogenous variables (VARX). The combined endogenous-VARX and exogenous-VAR equations comprise the reduced-form overall VAR (OVAR) equations of all variables in a LREM. The sequence of specified, solved, and combined equations defines a mapping from deep parameters to OVAR coefficients that is used to forwardly estimate a LREM in terms of deep parameters. Forwardly-estimated deep parameters determine forwardly-estimated RES equations that Lucas (1976) advocated for making policy predictions in his critique of policy predictions made with reduced-form equations.
Sims (1980) called economic identifying restrictions on deep parameters of forwardly-estimated LREMs "incredible", because he considered in-sample fits of forwardly-estimated OVAR equations inadequate and out-of-sample policy predictions of forwardly-estimated RES equations inaccurate. Sims (1980, 1986) instead advocated directly estimating OVAR equations restricted by statistical shrinkage restrictions and directly using the directly-estimated OVAR equations to make policy predictions. However, if assumed or predicted out-of-sample policy variables in directly-made policy predictions differ significantly from in-sample values, then, the out-of-sample policy predictions won't satisfy Lucas's critique.
If directly-estimated OVAR equations are reduced-form equations of underlying RES and LREM-structural equations, then, identification 2 derived in the paper can linearly "inversely" estimate the underlying RES equations from the directly-estimated OVAR equations and the inversely-estimated RES equations can be used to make policy predictions that satisfy Lucas's critique. If Sims considered directly-estimated OVAR equations to fit in-sample data adequately (credibly) and their inversely-estimated RES equations to make accurate (credible) out-of-sample policy predictions, then, he should consider the inversely-estimated RES equations to be credible. Thus, inversely-estimated RES equations by identification 2 can reconcile Lucas's advocacy for making policy predictions with RES equations and Sims's advocacy for directly estimating OVAR equations.
The paper also derives identification 1 of structural coefficients from RES coefficients that contributes mainly by showing that directly estimated reduced-form OVAR equations can have underlying LREM-structural equations.
Liquidity derivatives
(2022)
It is well established that investors price market liquidity risk. Yet, there exists no financial claim contingent on liquidity. We propose a contract to hedge uncertainty over future transaction costs, detailing potential buyers and sellers. Introducing liquidity derivatives in Brunnermeier and Pedersen (2009) improves financial stability by mitigating liquidity spirals. We simulate liquidity option prices for a panel of NYSE stocks spanning 2000 to 2020 by fitting a stochastic process to their bid-ask spreads. These contracts reduce the exposure to liquidity factors. Their prices provide a novel illiquidity measure refllecting cross-sectional commonalities. Finally, stock returns significantly spread along simulated prices.
A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autogressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on economic activity. We prove by counterexample that this practice is invalid in general, whether the data generating process is a structural VAR model or a dynamic stochastic general equilibrium model.
A common element of market structure analysis is the spatial representation of firms’ competitive positions on maps. Such maps typically capture static snapshots in time. Yet, competitive positions tend to change. Embedded in such changes are firms’ trajectories, that is, the series of changes in firms’ positions over time relative to all other firms in a market. Identifying these trajectories contributes to market structure analysis by providing a forward-looking perspective on competition, revealing firms’ (re)positioning strategies and indicating strategy effectiveness. To unlock these insights, we propose EvoMap, a novel dynamic mapping framework that identifies firms’ trajectories from high-frequency and potentially noisy data. We validate EvoMap via extensive simulations and apply it empirically to study the trajectories of more than 1,000 publicly listed firms over 20 years. We find substantial changes in several firms’ positioning strategies, including Apple, Walmart, and Capital One. Because EvoMap accommodates a wide range of mapping methods, analysts can easily apply it in other empirical settings and to data from various sources.
European banks have substantial investments in assets that are
measured without directly observable market prices (mark-to-
model). Financial disclosures of these value estimates lack
standardization and are hard to compare across banks. These
comparability concerns are concentrated in large European
banks that extensively rely on level 3 estimates with the most
unobservable inputs. Although the relevant balance sheet
positions only represent a small fraction of these large banks’
total assets (2.9%), their value equals a significant fraction of core
equity tier 1 (48.9%). Incorrect valuations thus have a potential to
impact financial stability. 85% of these bank assets are under
direct ECB supervision. Prudential regulation requires value
adjustments that are apt to shield capital against valuation risk.
Yet, stringent enforcement is critical for achieving this objective.
This document was provided by the Economic Governance
Support Unit at the request of the ECON Committee.
Die notwendige ökologische Transformation aber auch darüberhinausgehend die zunehmenden Erwartungen, die Gesellschaft und Politik an die Wirtschaft stellen, erfordern eine Prüfung des Wettbewerbsrechts und seiner Durchsetzung, insbesondere auch der dabei verwendeten (ökonomischen) Konzepte und Methoden, dahingehend, ob die aktuelle Praxis nicht einer stärkeren Berücksichtigung von Nachhaltigkeitszielen in unbegründeter Weise im Wege steht. Auf europäischer Ebene hat der Diskurs darüber im Jahr 2021 erheblich an Fahrt gewonnen. Wir stellen wesentliche Initiativen dar. Dabei zeigt sich unseres Erachtens allerdings auch, dass für eine konstruktive Weiterentwicklung noch die nötigen konzeptionellen und methodischen Grundlagen fehlen.
We investigate whether the bank crisis management framework of the European banking union can effectively bar the detrimental influence of national interests in cross-border bank failures. We find that both the internal governance structure and decision making procedure of the Single Resolution Board (SRB) and the interplay between the SRB and national resolution authorities in the implementation of supranationally devised resolution schemes provide inroads that allow opposing national interests to obstruct supranational resolution. We also show that the Single Resolution Fund (SRG), even after the ratification of the reform of the European Stability Mechanism (ESM) and the introduction of the SRF backstop facility, is inapt to overcome these frictions. We propose a full supranationalization of resolution decision making. This would allow European authorities in charge of bank crisis management to operate autonomously and achieve socially optimal outcomes beyond national borders.
This paper characterizes the stationary equilibrium of a continuous-time neoclassical production economy with capital accumulation in which households can insure against idiosyncratic income risk through long-term insurance contracts. Insurance companies operating in perfectly competitive markets can commit to future contractual obligations, whereas households cannot. For the case in which household labor productivity takes two values, one of which is zero, and where households have logutility we provide a complete analytical characterization of the optimal consumption insurance contract, the stationary consumption distribution and the equilibrium aggregate capital stock and interest rate. Under parameter restrictions, there is a unique stationary equilibrium with partial consumption insurance and a stationary consumption distribution that takes a truncated Pareto form. The unique equilibrium interest rate (capital stock) is strictly decreasing (increasing) in income risk. The paper provides an analytically tractable alternative to the standard incomplete markets general equilibrium model developed in Aiyagari (1994) by retaining its physical structure, but substituting the assumed incomplete asset markets structure with one in which limits to consumption insurance emerge endogenously, as in Krueger and Uhlig (2006).