Sustainable Architecture for Finance in Europe (SAFE)
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When parties present divergent econometric evidence, the court may view such evidence as contradictory and thus ignore it completely, without conducting closer analysis. We develop a simple method for distinguishing between actual and merely apparent contradiction based on the statistical concept of the “severity” of the furnished evidence. Again using “severity”, we also propose a method for reconciling divergent findings in instances of mere seeming contradiction. Our chosen application is that of damage estimation in follow-on cases.
This paper contributes to the debate on the adequate regulatory treatment of non-bank financial intermediation (NBFI). It proposes an avenue for regulators to keep regulatory arbitrage under control and preserve sufficient space for efficient financial innovation at the same time. We argue for a normative approach to supervision that can overcome the proverbial race between hare and hedgehog in financial regulation and demonstrate how such an approach can be implemented in practice. We first show that regulators should primarily analyse the allocation of tail risk inherent in NBFI. Our paper proposes to apply regulatory burdens equivalent to prudential banking regulation if the respective transactional structures become only viable through indirect or direct access to (ad hoc) public backstops. Second, we use insights from the scholarship on regulatory networks as communities of interpretation to demonstrate how regulators can retrieve the information on transactional innovations and their risk-allocating characteristics that they need to make the pivotal determination. We suggest in particular how supervisors should structure their relationships with semi-public gatekeepers such as lawyers, auditors and consultants to keep abreast of the risk-allocating features of evolving transactional structures. Finally, this paper uses the example of credit funds as non-bank entities economically engaged in credit intermediation to illustrate the merits of the proposed normative framework and to highlight that multipolar regulatory dialogues are needed to shed light on the specific risk-allocating characteristics of recent contractual innovations.
Venture capital-backed firms, unavoidable value-destroying trade sales, and fair value protections
(2020)
This paper investigates the implications of the fair value protections contemplated by the standard corporate contract (i.e., the standard contract form for which corporate law provides) for the entrepreneur–venture capitalist relationship, focusing, in particular, on unavoidable value-destroying trade sales. First, it demonstrates that the typical entrepreneur–venture capitalist contract does institutionalize the venture capitalist’s liquidity needs, allowing, under some circumstances, for counterintuitive instances of contractually-compliant value destruction. Unavoidable value-destroying
trade sales are the most tangible example. Next, it argues that fair value protections can prevent the entrepreneur and venture capitalist from allocating the value that these transactions generate as they would want. Then, it shows that the reality of venture capital-backed firms calls for a process of adaptation of the standard corporate contract that has one major step in the deactivation or re-shaping of fair value protections. Finally, it argues that a standard corporate contract aiming to promote social welfare through venture capital should feature flexible fair value protections
Following the financial crash and the subsequent recession, European policymakers have undertaken major reforms regarding the European Economic and Monetary Union (EMU). Yet, the success rate is mixed. Several reform proposals have either completely failed due to opposition forces or are still pending, sometimes for years. This article provides an overview of reforms in four major policy fields: financial stabilisation, economic governance, fiscal solidarity, and cooperative dissolution. Building on the conceptual foundation of policy analysis, it distinguishes between policy outputs and outcomes. Policy output refers to legislation being adopted or agreement on treaty changes, while policy outcomes depict the result from the implementation process.
Fiscal policies and household consumption during the COVID-19 pandemic: a review of early evidence
(2020)
We review early evidence on how household consumption behavior has evolved over the pandemic and how different groups of households have responded to fiscal stimulus programs. Due to the scarcity of evidence for Europe, our review focuses on evidence from the US. Notwithstanding the institutional and demographic differences, we highlight generalizable findings and challenges to the design of stimulus policies from the pandemic. In conclusion, we identify several open issues for dis cussion.
OTC discount
(2020)
We document a sizable OTC discount in the interdealer market for German sovereign bonds where exchange and over-the-counter trading coexist: the vastmajority of OTC prices are favorable with respect to exchange quotes. This is a challenge for theories of OTC markets centered around search frictions but consistent with models of hybrid markets based on information frictions. We show empiricallythat proxies for both frictions determine variation in the discount, which is largely passed on to customers. Dealers trade on the exchange for immediacy and via brokers for opacity and anonymity, highlighting the complementary roles played by the di↵erent protocols.
We relate time-varying aggregate ambiguity (V-VSTOXX) to individual investor trading. We use the trading records of more than 100,000 individual investors from a large German online brokerage from March 2010 to December 2015. We find that an increase in ambiguity is associated with increased investor activity. It also leads to a reduction in risk-taking which does not reverse over the following days. When ambiguity is high, the effect of sentiment looms larger. Survey evidence reveals that ambiguity averse investors are more prone to ambiguity shocks. Our results are robust to alternative survey-, newspaper- or market-based ambiguity measures.
Supranational rules, national discretion: increasing versus inflating regulatory bank capital?
(2020)
We study how higher capital requirements introduced at the supranational level affect the regulatory capital of banks across countries. Using the 2011 EBA capital exercise as a quasi-natural experiment, we find that treated banks exploit discretion in the calculation of regulatory capital to inflate their capital ratios without a commensurate increase in their book equity and without a reduction in bank risk. Regulatory capital inflation is more pronounced in countries where credit supply is expected to tighten, suggesting that national authorities forbear their domestic banks to meet supranational requirements, with a focus on short-term economic considerations.
In diesem Beitrag wird ein Vorschlag vorgestellt, wie es trotz langfristiger Niedrigzinsen möglich ist, die vor 18 Jahren eingeführte Riester-Rente so umzugestalten, dass alle Beteiligten davon profitieren. Wird die Mindestauszahlung am Ende der Vertragslaufzeit nur für die Eigenbeiträge, nicht aber für die staatlichen Zulagen garantiert, können deutlich höhere Renditen erzielt werden. Unter dem Strich haben dann nicht nur Privatleute mehr Geld aus ihrer Altersvorsorge, sondern der Staat wird mehr Steuern einnehmen und die Anbieter haben mehr Spielraum für bedarfsgerechte Produktgestaltung.
The paper discusses the policy implications of the Wirecard scandal. The study finds that all lines of defense against corporate fraud, including internal control systems, external audits, the oversight bodies for financial reporting and auditing and the market supervisor, contributed to the scandal and are in need of reform. To ensure market integrity and investor protection in the future, the authors make eight suggestions for the market and institutional oversight architecture in Germany and in Europe.
Banks are not immune from COVID-19. The economic downturn may drive some banks to the point of non-viability (PONV). If so, is the resolution regime in the Euro-area ready to respond? No, for banks may not have the right amount of the right kind of liabilities to make bail-in work. That could lead to a banking crisis. The Euro area can avoid this risk, by arranging now for a recap later. This would plug the gap between what the failing bank has and what it would need to make bail-in work. To do so, banks would pay – possibly via the contributions they make to the Single Resolution Fund – a commitment fee to a European backstop authority for a mandatory, system-wide note issuance facility. This would compel each bank, as it approached or reached the PONV, to issue to the backstop, and the backstop to purchase from the bank, the obligations the failing bank needs in order to make bail-in work. Such obligations would take the form of “senior-most” non-preferred debt, and bail-in would stop with such debt. That would allow the SRB to use the bail-in tool to resolve the failed bank, reopen it and run it under a solvent wind-down strategy. That protects counterparties and customers and ensures the continuity of critical economic functions. It also keeps investors at risk and promotes market discipline. Above all, it preserves financial stability.
Inflation ist ein Konstrukt. Sie wird von unterschiedlichen Akteur*innen unterschiedlich wahrgenommen. Zum Teil passiert dies, weil Warenkörbe differieren, zum Teil weil Erwartungen unterschiedlich gebildet werden. Dieser Beitrag diskutiert die Heterogenität der Infl ation und ihrer Wahrnehmung und was dies für die Zielgröße der Zentralbankpolitik bedeutet.
Inflation ist ein Konstrukt. Sie wird von unterschiedlichen Akteuren unterschiedlich wahrgenommen. Zum Teil passiert dies, weil Warenkörbe differieren, zum Teil weil Erwartungen unterschiedlich gebildet werden. Dieser Beitrag diskutiert die Heterogenität der Inflation und ihrer Wahrnehmung und was dies für die Zielgröße der Zentralbankpolitik bedeutet.
The paper compares provision of public infrastructure via public-private partnerships (PPPs) with provision under government management. Due to soft budget constraints of government management, PPPs exert more effort and therefore have a cost advantage in building infrastructure. At the same time, hard budget constraints for PPPs introduce a bankruptcy risk and bankruptcy costs. Consequently, if bankruptcy costs are high, PPPs may be less efficient than public management, although this does not result from PPPs’ higher interest costs.
We develop a novel empirical approach to identify the effectiveness of policies against a pandemic. The essence of our approach is the insight that epidemic dynamics are best tracked over stages, rather than over time. We use a normalization procedure that makes the pre-policy paths of the epidemic identical across regions. The procedure uncovers regional variation in the stage of the epidemic at the time of policy implementation. This variation delivers clean identification of the policy effect based on the epidemic path of a leading region that serves as a counterfactual for other regions. We apply our method to evaluate the effectiveness of the nationwide stay-home policy enacted in Spain against the Covid-19 pandemic. We find that the policy saved 15.9% of lives relative to the number of deaths that would have occurred had it not been for the policy intervention. Its effectiveness evolves with the epidemic and is larger when implemented at earlier stages.
This paper studies a household’s optimal demand for a reverse mortgage. These contracts allow homeowners to tap their home equity to finance consumption needs. In stylized frameworks, we show that the decision to enter a reverse mortgage is mainly driven by the dierential between the aggregate appreciation of the house price and principal limiting factor on the one hand and the funding costs of a household on the other hand. We also study a rich life-cycle model that can explain the low demand for reverse mortgages as observed in US data. In this model, we analyze the optimal response of a household that is confronted with a health shock or financial disaster. If an agent suers from an unexpected health shock, she reduces the risky portfolio share and is more likely to enter a reverse mortgage. On the other hand, if there is a large drop in the stock market, she keeps the risky portfolio share almost constant by buying additional shares of stock. Besides, the probability to take out a reverse mortgage is hardly aected.
The ruling of the German Federal Constitutional Court and its call for conducting and communicating proportionality assessments regarding monetary policy have been the subject of some controversy. However, it can also be understood as a way to strengthen the de-facto independence of the European Central Bank. The authors shows how a regular proportionality check could be integrated in the ECB’s strategy that is currently undergoing a systematic review. In particular, they propose to include quantitative benchmarks for policy rates and the central bank balance sheet. Deviations from such benchmarks can have benefits in terms of the intended path for inflation while involving costs in terms of risks and side effects that need to be balanced. Practical applications to the euro area are provided
In this paper we adapt the Hamiltonian Monte Carlo (HMC) estimator to DSGE models, a method presently used in various fields due to its superior sampling and diagnostic properties. We implement it into a state-of-theart, freely available high-performance software package, STAN. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model using US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition, we find bimodality in the Smets-Wouters model even if we estimate the model using the original tight priors. Finally, we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm to create a powerful tool which permits the estimation of DSGE models with ill-behaved posterior densities.
This paper studies the link between bank recapitalization and welfare in a dynamic production economy. The model features financial frictions because banks benefit of a cost advantage at monitoring firms and face costly equity issuance. The competitive equilibrium outcome is inefficient because agents do not internalize the effects banks’ capitalization over the allocation of capital, its price and, in turn, firms investments. It follows, individual recapitalizations are sub-optimal and bailout policies may benefit social welfare in the long-run. Bailouts improve capital allocation in states where aggregate banks are poorly capitalized, therefore enhancing their market valuation, fostering investments, and stabilizing the economy recovery path.
Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.
Using a structural life-cycle model, we quantify the long-term impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. In the model, public investment through schooling is combined with parental time and resource investments in the production of child human capital at different stages in the children's development process. We quantitatively characterize both the long-term earnings consequences on children from a Covid-19 induced loss of schooling, as well as the associated welfare losses. Due to self-productivity in the human capital production function, skill attainment at a younger stage of the life cycle raises skill attainment at later stages, and thus younger children are hurt more by the school closures than older children. We find that parental reactions reduce the negative impact of the school closures, but do not fully offset it. The negative impact of the crisis on children's welfare is especially severe for those with parents with low educational attainment and low assets. The school closures themselves are primarily responsible for the negative impact of the Covid-19 shock on the long-run welfare of the children, with the pandemic-induced income shock to parents playing a secondary role.
Predictability and the cross-section of expected returns: a challenge for asset pricing models
(2020)
Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive criteria models have to satisfy to produce expected return patterns in line with the data and discuss various examples.
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.