Refine
Year of publication
Document Type
- Working Paper (663) (remove)
Has Fulltext
- yes (663)
Is part of the Bibliography
- no (663) (remove)
Keywords
- Banking Union (14)
- monetary policy (12)
- regulation (11)
- COVID-19 (9)
- Covid-19 (9)
- ESG (9)
- systemic risk (9)
- banks (8)
- corporate governance (8)
- financial stability (8)
Institute
- House of Finance (HoF) (663) (remove)
Non-bank (-balance sheet) based financial intermediation has become considerably more important over the last couple of decades. For the U.S., this trend has been discussed ever since the mid-1990s. As a consequence, traditional monetary transmission mechanisms, mainly operating through bank balance sheets, have apparently become less relevant. This in particular applies to the bank lending channel. Concurrently, recent theoretical and empirical work uncovered a "risk-taking channel" of monetary policy. This mechanism is not confined to traditional banks but has been found to operate also across the spectrum of financial intermediaries and intermediation devices, including securitization and collateralized lending/borrowing. In addition, recent empirical evidence suggests that the increasing importance of shadow-banking activities might have given rise to a so-called "waterbed effect". This is a mediating mechanisms, dampening or counteracting typically to be expected reactions to monetary policy impulses. Employing flow-of-funds data, we can document also for the Euro Area that a trend towards non-bank (not necessarily more 'market'-based) intermediation has occurred. This is, however, a fairly recent development, substantially weaker than in the U.S. Nonetheless, analyzing the response of Euro Area bank and nonbank financial intermediaries to monetary policy impulses, we find some notable behavioral differences between mainly deposit-funded and more 'market'-based financial intermediaries. We also detect, inter alia, the existence of a (still) fairly weak, but potentially policyrelevant, "waterbed" effect.
Euro area shadow banking activities in a low-interest-rate environment: a flow-of-funds perspective
(2016)
Very low policy rates as well as the substantial redesign of rules and supervisory institutions have changed background conditions for the Euro Area’s financial intermediary sector substantially. Both policy initiatives have been targeted at improving societal welfare. And their potential side effects (or costs) have been discussed intensively, in academic as well as policy circles. Very low policy rates (and correspondingly low market rates) are likely to whet investors’ risk taking incentives. Concurrently, the tightened regulatory framework, in particular for banks, increases the comparative attractiveness of the less regulated, so-called shadow banking sector. Employing flow-of-funds data for the Euro Area’s non-bank banking sector we take stock of recent developments in this part of the financial sector. In addition, we examine to which extent low interest rates have had an impact on investment behavior. Our results reveal a declining role of banks (and, simultaneously, an increase in non-bank banking). Overall intermediation activity, hence, has remained roughly at the same level. Moreover, our findings also suggest that non-bank banks have tended to take positions in riskier assets (particularly in equities). In line with this observation, balance-sheet based risk measures indicate a rise in sector-specific risks in the non-bank banking sector (when narrowly defined).
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.
Recent regulatory measures such as the European Union’s AI Act re-quire artificial intelligence (AI) systems to be explainable. As such, under-standing how explainability impacts human-AI interaction and pinpoint-ing the specific circumstances and groups affected, is imperative. In this study, we devise a formal framework and conduct an empirical investiga-tion involving real estate agents to explore the complex interplay between explainability of and delegation to AI systems. On an aggregate level, our findings indicate that real estate agents display a higher propensity to delegate apartment evaluations to an AI system when its workings are explainable, thereby surrendering control to the machine. However, at an individual level, we detect considerable heterogeneity. Agents possess-ing extensive domain knowledge are generally more inclined to delegate decisions to AI and minimize their effort when provided with explana-tions. Conversely, agents with limited domain knowledge only exhibit this behavior when explanations correspond with their preconceived no-tions regarding the relationship between apartment features and listing prices. Our results illustrate that the introduction of explainability in AI systems may transfer the decision-making control from humans to AI under the veil of transparency, which has notable implications for policy makers and practitioners that we discuss.
This paper explores the interplay of feature-based explainable AI (XAI) tech- niques, information processing, and human beliefs. Using a novel experimental protocol, we study the impact of providing users with explanations about how an AI system weighs inputted information to produce individual predictions (LIME) on users’ weighting of information and beliefs about the task-relevance of information. On the one hand, we find that feature-based explanations cause users to alter their mental weighting of available information according to observed explanations. On the other hand, explanations lead to asymmetric belief adjustments that we inter- pret as a manifestation of the confirmation bias. Trust in the prediction accuracy plays an important moderating role for XAI-enabled belief adjustments. Our results show that feature-based XAI does not only superficially influence decisions but re- ally change internal cognitive processes, bearing the potential to manipulate human beliefs and reinforce stereotypes. Hence, the current regulatory efforts that aim at enhancing algorithmic transparency may benefit from going hand in hand with measures ensuring the exclusion of sensitive personal information in XAI systems. Overall, our findings put assertions that XAI is the silver bullet solving all of AI systems’ (black box) problems into perspective.
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.
Advances in Machine Learning (ML) led organizations to increasingly implement predictive decision aids intended to improve employees’ decision-making performance. While such systems improve organizational efficiency in many contexts, they might be a double-edged sword when there is the danger of a system discontinuance. Following cognitive theories, the provision of ML-based predictions can adversely affect the development of decision-making skills that come to light when people lose access to the system. The purpose of this study is to put this assertion to the test. Using a novel experiment specifically tailored to deal with organizational obstacles and endogeneity concerns, we show that the initial provision of ML decision aids can latently prevent the development of decision-making skills which later becomes apparent when the system gets discontinued. We also find that the degree to which individuals 'blindly' trust observed predictions determines the ultimate performance drop in the post-discontinuance phase. Our results suggest that making it clear to people that ML decision aids are imperfect can have its benefits especially if there is a reasonable danger of (temporary) system discontinuances.
In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT’s cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT’s behavior isn’t random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our esearch highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.
Incentives, self-selection, and coordination of motivated agents for the production of social goods
(2021)
We study, theoretically and empirically, the effects of incentives on the self-selection and coordination of motivated agents to produce a social good. Agents join teams where they allocate effort to either generate individual monetary rewards (selfish effort) or contribute to the production of a social good with positive effort complementarities (social effort). Agents differ in their motivation to exert social effort. Our model predicts that lowering incentives for selfish effort in one team increases social good production by selectively attracting and coordinating motivated agents. We test this prediction in a lab experiment allowing us to cleanly separate the selection effect from other effects of low incentives. Results show that social good production more than doubles in the low- incentive team, but only if self-selection is possible. Our analysis highlights the important role of incentives in the matching of motivated agents engaged in social good production.
Der Einsatz von Künstliche Intelligenz (KI) – Technologien eröffnet viele Chancen, birgt aber auch viele Risiken – insbesondere in der Finanzbranche. Dieses Whitepaper gibt einen Überblick über den aktuellen Stand der Anwendung und Regulierung von KI-Technologien in der Finanzbranche, und diskutiert Chancen und Risiken von KI. KI findet in der Finanzbranche zahlreiche Anwendungsgebiete. Dazu gehören Chatbots, intelligente Assistenten für Kunden, automatischer Hochfrequenzhandel, automatisierte Betrugserkennung, Überwachung der Compliance, Gesichtserkennungssoftware zur Kundenidentifikation u. v. m. Auch Finanzaufsichtsbehörden setzen zunehmend KI-Anwendungen ein, um große und komplexe Datenmengen (Big Data) automatisiert und skalierbar auf Muster zu untersuchen und ihren Aufsichtspflichten nachzukommen.
Die Regulierung von KI in der Finanzbranche ist ein Balanceakt. Auf der einen Seite gibt es eine Notwendigkeit Flexibilität zu gewährleisten, um Innovationen nicht einzudämmen und im internationalen Wettbewerb nicht abgehängt zu werden. Strenge Auflagen können in diesem Zusammenhang als Barriere für die erfolgreiche Weiter-)Entwicklung von KI-Applikationen in der Finanzbranche wirken. Auf der anderen Seite müssen Persönlichkeitsrechte geschützt und Entscheidungsprozesse nachvollziehbar bleiben. Die fehlende Erklärbarkeit und Interpretierbarkeit von KI-Modellen entsteht in erster Linie durch Intransparenz bei einem Großteil heutiger KI-Anwendungen, bei welchen zwar die Natur der Ein- und Ausgaben beobachtbar und verständlich ist, nicht jedoch die genauen Verarbeitungsschritte dazwischen (Blackbox Prinzip).
Dieses Spannungsfeld zeigt sich auch im aktuellen regulatorischen Ansatz verschiedener Behörden. So werden einerseits die positiven Seiten von KI betont, wie Effizienz- und Effektivitätsgewinne sowie Rentabilitäts- und Qualitätssteigerungen (Bundesregierung, 2019) oder neue Methoden der Gefahrenanalyse in der Finanzmarktregulierung (BaFin, 2018a). Andererseits, wird darauf verwiesen, dass durch KI getroffene Entscheidungen immer von Menschen verantwortet werden müssen (EU Art. 22 DSGVO) und demokratische Rahmenbedingungen des Rechtsstaats zu wahren seien (FinTechRat, 2017).
Für die Zukunft sehen wir die Notwendigkeit internationale Regularien prinzipienbasiert, vereinheitlicht und technologieneutral weiterzuentwickeln, ohne dabei die Entwicklung neuer KIbasierter Geschäftsmodelle zu bremsen. Im globalen Wettstreit sollte Europa bei der Regulierung des KI-Einsatzes eine Vorreiterrolle einnehmen und damit seine demokratischen Werte der digitalen Freiheit, Selbstbestimmung und das Recht auf Information weltweit exportieren. Förderprogramme sollten einen stärkeren Fokus auf die Entwicklung nachhaltiger und verantwortungsvoller KI in Banken legen. Dazu zählt insbesondere die (Weiter-)Entwicklung breit einsetzbarer Methoden, die es erlauben, menschen-interpretierbare Erklärungen für erzeugte Ausgaben bereitzustellen und Problemen wie dem Blackbox Prinzip entgegenzuwirken.
Aus Sicht der Unternehmen in der Finanzbranche könnte eine Kooperation mit BigTech-Unternehmen sinnvoll sein, um gemeinsam das Potential der Technologie bestmöglich ausschöpfen zu können. Nützlich wäre auch ein gemeinsames semantisches Metadatenmodell zur Beschreibung der in der Finanzbranche anfallenden Daten. In Zukunft könnten künstliche Intelligenzen Daten aus sozialen Netzwerken berücksichtigen oder Smart Contracts aushandeln. Eine der größten Herausforderungen der Zukunft wird das Anwerben geeigneten Personals darstellen.
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.
Using a novel experimental design, I test how the exposure to information about a group’s relative performance causally affects the members’ level of identification and thereby their propensity to harm affiliates of comparison groups. I find that both, being informed about a high and poor relative performance of the ingroup similarly fosters identification. Stronger ingroup identification creates increased hostility against the group of comparison. In cases where participants learn about poor relative performance, there appears to be a direct level effect additionally elevating hostile discrimination. My findings shed light on a specific channel through which social media may contribute to intergroup fragmentation and polarization.
This paper aims at an improved understanding of the relationship between monetary policy and racial inequality. We investigate the distributional effects of monetary policy in a unified framework, linking monetary policy shocks both to earnings and wealth differentials between black and white households. Specifically, we show that, although a more accommodative monetary policy increases employment of black households more than white households, the overall effects are small. At the same time, an accommodative monetary policy shock exacerbates the wealth difference between black and white households, because black households own less financial assets that appreciate in value. Over multi-year time horizons, the employment effects are substantially smaller than the countervailing portfolio effects. We conclude that there is little reason to think that accommodative monetary policy plays a significant role in reducing racial inequities in the way often discussed. On the contrary, it may well accentuate inequalities for extended periods.
We examine how a firms' investment behavior affects the investment of a neighboring firm. Economic theory yields ambiguous predictions regarding the direction of firm peer effects and consistent with earlier work, we find that firms display similar investment behavior within an area using OLS analysis. Exploiting time-variation in the rise of U.S. states' corporate income taxes and utilizing heterogeneity in firms' exposure to increases in corporate income tax rates, we identify the causal impact of local firms' investments. Using this as an instrumental variable in a 2SLS estimation, we find that an increases in local firms' investment reduces the investment of a local peer firm. This effect is more pronounced if local competition among firms is stronger and supports theories that firm investments are strategic substitutes due to competition.
What are the aggregate and distributional consequences of the relationship be-tween an individual’s social network and financial decisions? Motivated by several well-documented facts about the influence of social connections on financial decisions, we build and calibrate a model of stock market participation with a social network that emphasizes the interplay between connectivity and network structure. Since connections to informed agents help spread information, there is a pivotal role for factors that determine sorting among agents. An increase in the average number of connections raises the average participation rate, mostly due to richer agents. A higher degree of sorting benefits richer agents by creating clusters where information spreads more efficiently. We show empirical evidence consistent with the importance of connectivity and sorting. We discuss several new avenues for future research into the aggregate impact of peer effects in finance.
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.
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.
Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.
We investigate the relationship between anchoring and the emergence of bubbles in experimental asset markets. We show that setting a visual anchor at the fundamental value (FV) in the first period only is sufficient to eliminate or to significantly reduce bubbles in laboratory asset markets. If no FV-anchor is set, bubble-crash patterns emerge. Our results indicate that bubbles in laboratory environments are primarily sparked in the first period. If prices are initiated around the FV, they stay close to the FV over the entire trading horizon. Our insights can be related to initial public offerings and the interaction between prices set on pre-opening markets and subsequent intra-day price dynamics.
We investigate the relationship between anchoring and the emergence of bubbles in experimental asset markets. We show that setting a visual anchor at the fundamental value (FV) in the first period only is sufficient to eliminate or to significantly reduce bubbles in laboratory asset markets. If no FV-anchor is set, bubble-crash patterns emerge. Our results indicate that bubbles in laboratory environments are primarily sparked in the first period. If prices are initiated around the FV, they stay close to the FV over the entire trading horizon. Our insights can be related to initial public offerings and the interaction between prices set on pre-opening markets and subsequent intra-day price dynamics.
Do markets correct individual behavioral biases? In an experimental asset market, we compare the outcomes of a standard market economy to those of a an island economy that removed market interactions. We observe asset price bubbles in the market economy while prices are stable in the island economy. We also find that subjects took more risk following larger losses, resulting in larger prices and consistent with a gambling for resurrection motive. This motive can translate into bubbles in the market economy because higher prices increase average losses and thus reinforce the desire to resurrect. By contrast, the absence of such a strategic complementarity in island economies can explain the more stable outcome. These results suggest that markets do not correct behavioral biases, rather the contrary.
Previous research has documented strong peer effects in risk taking, but little is known about how such social influences affect market outcomes. The consequences of social interactions are hard to isolate in financial data, and theoretically it is not clear whether peer effects should increase or decrease risk sharing. We design an experimental asset market with multiple risky assets and study how exogenous variation in real-time information about the portfolios of peer group members affects aggregate and individual risk taking. We find that peer information ameliorates under-diversification that occurs in a market without such information. One reason is that peer information increases risk aversion and induces a concern for relative income position that may reduce or amplify risk taking, depending on whether the context highlights the most or least successful trader. Thus, contrary to conventional wisdom, we show that social interactions may help to reduce earnings volatility in financial markets, and we discuss implications for institutional design.
In an experimental setting in which investors can entrust their money to traders, we investigate how compensation schemes affect liquidity provision and asset prices. Investors face a trade-off between risk and return. At the benefit of a potentially higher return, they can entrust their money to a trader. However this investment is risky, as the trader might not be trustworthy. Alternatively, they can opt for a safe but low return. We study how subjects solve this trade-off when traders are either liable for losses or not, and when their bonuses are either capped or not. Limited liability introduces a conflict of interest because it makes traders value the asset more than investors. To limit losses, investors should thus restrict liquidity provision to force traders to trade at a lower price. By contrast, bonus caps make traders value the asset less than investors. This should encourage liquidity provision and decrease prices. In contrast to these predictions, we find that under limited liability investors contribute to asset price bubbles by increasing liquidity provision and that caps fail to tame bubbles. Overall, giving investors skin in the game fosters financial stability.
Flows of funds run by banks or by firms that belong to the same financial group as a bank are less volatile and less sensitive to bad past performance. This enables bank-affiliated funds to better weather distress and to hold lower precautionary cash buffers in comparison with their unaffiliated peers. Banks provide liquidity support to distressed affiliated funds by buying shares of those funds that are experiencing large outflows. This, in turn, diminishes the severity of strategic complementarities in investors’ redemptions. Liquidity support and other benefits of bank affiliation are conditional on the financial health of the parent company. Distress in the banking system spills over to the mutual fund sector via ownership links. Our research high-lights substantial dependencies between the banking system and the asset management industry, and identifies an important channel via which financial stability risks depend on the organisational structure of the financial sector.
This paper analyzes the current implementation status of sustainability and taxonomy-aligned disclosure under the Sustainable Finance Disclosure Regulation (SFDR) as well as the development of the SFDR categorization of funds offered via banks in Germany. Examining data provided by WM Group, which consists of more than 10,000 investment funds and 2,000 index funds between September 2022 and March 2023, we have observed a significant proportion of Article 9 (dark green) funds transitioning to Article 8 (light green) funds, particularly among index funds. As a consequence of this process, the profile of the SFDR classes has sharpened, which reflects an increased share of sustainable investments in the group of Article 9 funds. When differentiating between environmental and social investments, the share of environmental investments increased, but the share of social investments decreased in the group of Article 9 funds at the beginning of 2023. The share of taxonomy-aligned investments is very low, but slightly increasing for Article 9 funds. However, by March 2023 only around 1,000 funds have reported their sustainability proportions and this picture might change due to legal changes which require all funds in the scope of the SFDR to report these proportions in their annual reports being published after 1 January 2023.
We study self- and cross-excitation of shocks in the Eurozone sovereign CDS market. We adopt a multivariate setting with credit default intensities driven by mutually exciting jump processes, to capture the salient features observed in the data, in particular, the clustering of high default probabilities both in time (over days) and in space (across countries). The feedback between jump events and the intensity of these jumps is the key element of the model. We derive closed-form formulae for CDS prices, and estimate the model by matching theoretical prices to their empirical counterparts. We find evidence of self-excitation and asymmetric cross-excitation. Using impulse-response analysis, we assess the impact of shocks and a potential policy intervention not just on a single country under scrutiny but also, through the effect on cross-excitation risk which generates systemic sovereign risk, on other interconnected countries.
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.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible, trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the USD in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC--USD relative to other exchange rate pairs, as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent trading frictions associated with short sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC--USD futures was beneficial to the bitcoin spot market by making the underlying prices more informative.
We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on quantities known only in the true model. We apply our method to a calibrated affine model. Our findings are threefold: Jumps matter more, i.e. our approximation is less accurate, if (i) the expected jump size or (ii) the jump intensity is large. Fixing the average impact of jumps, we find that (iii) rare, but severe jumps matter more than frequent, but small jumps.
We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on results from the true model. We apply our method to a calibrated affine model and fine that relative wealth equivalent losses are below 1.16% if the jump size is stochastic and below 1% if the jump size is constant and γ ≥ 5. We perform robustness checks for various levels of risk-aversion, expected jump size, and jump intensity.
We present new statistical indicators of the structure and performance of US banks from 1990 to today, geographically disaggregated at the level of individual counties. The constructed data set (20 indicators for some 3150 counties over 31 years, for a total of about 2 million data points) conveys a detailed picture of how the geography of US banking has evolved in the last three decades. We consider the data as a stepping stone to understand the role banks and banking policies may have played in mitigating, or exacerbating, the rise of poverty and inequality in certain US regions.
Exit strategies
(2014)
We study alternative scenarios for exiting the post-crisis fiscal and monetary accommodation using a macromodel where banks choose their capital structure and are subject to runs. Under a Taylor rule, the post-crisis interest rate hits the zero lower bound (ZLB) and remains there for several years. In that condition, pre-announced and fast fiscal consolidations dominate - based on output and inflation performance and bank stability - alternative strategies incorporating various degrees of gradualism and surprise. We also examine an alternative monetary strategy in which the interest rate does not reach the ZLB; the benefits from fiscal consolidation persist, but are more nuanced.
We assess the effects of monetary policy on bank risk to verify the existence of a risk-taking channel - monetary expansions inducing banks to assume more risk. We first present VAR evidence confirming that this channel exists and tends to concentrate on the bank funding side. Then, to rationalize this evidence we build a macro model where banks subject to runs endogenously choose their funding structure (deposits vs. capital) and risk level. A monetary expansion increases bank leverage and risk. In turn, higher bank risk in steady state increases asset price volatility and reduces equilibrium output.
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.
Discussions about the banking union have restarted. Its success so far is limited: national banking sectors are still overwhelmingly exposed to their own countries’ economies, cross border banking has not increased and capital and liquidity remain locked within national boundaries. The policy letter highlights that the current debate, centered on sovereign exposures and deposit insurance, misses critical underlying problems in the supervision and resolution frameworks. The ECB supervisors’ efforts to facilitate cross-border banking have been hampered by national ringfencing. The resolution framework is not up to its task: limited powers of the SRB, prohibitive access conditions and limited size of the Single Resolution Fund limit its effectiveness. A lack of a coherent European framework for insolvency unlevels the regulatory field and creates incentives to bypass European rules. The new Commission and European Parliament, with the new ECB leadership, provide a unique opportunity to address these shortcomings and make the banking union work.
There is much discussion today about a possible digital euro (PDE). Is this attention exaggerated? Are “central bank digital currencies” (CBDCs) “a solution in search of a problem”, as some have argued? This article summarizes the main facts about the PDE and concludes that, if the decision on adoption had to be taken today, the arguments against would outweigh those in favor. However, there may be future circumstances in which having a CBDC ready for use can indeed be useful. Therefore, preparing is a good thing, even if the odds of its usefulness in normal conditions are slim.
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.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
Investors' return expectations are pivotal in stock markets, but the reasoning behind these expectations remains a black box for economists. This paper sheds light on economic agents' mental models -- their subjective understanding -- of the stock market, drawing on surveys with the US general population, US retail investors, US financial professionals, and academic experts. Respondents make return forecasts in scenarios describing stale news about the future earnings streams of companies, and we collect rich data on respondents' reasoning. We document three main results. First, inference from stale news is rare among academic experts but common among households and financial professionals, who believe that stale good news lead to persistently higher expected returns in the future. Second, while experts refer to the notion of market efficiency to explain their forecasts, households and financial professionals reveal a neglect of equilibrium forces. They naively equate higher future earnings with higher future returns, neglecting the offsetting effect of endogenous price adjustments. Third, a series of experimental interventions demonstrate that these naive forecasts do not result from inattention to trading or price responses but reflect a gap in respondents' mental models -- a fundamental unfamiliarity with the concept of equilibrium.
This paper shows that support for climate action is high across survey participants from all EU countries in three dimensions: (1) Participants are willing to contribute personally to combating climate change, (2) they approve of pro-climate social norms, and (3) they demand government action. In addition, there is a significant perception gap where individuals underestimate others' willingness to contribute to climate action by over 10 percentage points, influencing their own willingness to act. Policymakers should recognize the broad support for climate action among European citizens and communicate this effectively to counteract the vocal minority opposed to it.
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.
Shallow meritocracy
(2023)
Meritocracies aspire to reward hard work and promise not to judge individuals by the circumstances into which they were born. However, circumstances often shape the choice to work hard. I show that people's merit judgments are "shallow" and insensitive to this effect. They hold others responsible for their choices, even if these choices have been shaped by unequal circumstances. In an experiment, US participants judge how much money workers deserve for the effort they exert. Unequal circumstances disadvantage some workers and discourage them from working hard. Nonetheless, participants reward the effort of disadvantaged and advantaged workers identically, regardless of the circumstances under which choices are made. For some participants, this reflects their fundamental view regarding fair rewards. For others, the neglect results from the uncertain counterfactual. They understand that circumstances shape choices but do not correct for this because the counterfactual—what would have happened under equal circumstances—remains uncertain.
In this paper we propose a way forward towards increased financial resilience in times of growing disagreement concerning open borders, free trade and global regulatory standards. In light of these concerns, financial resilience remains a highly valued policy objective. We wish to contribute by suggesting an agenda of concrete, do-able steps supporting an enhanced level of resilience, combined with a deeper understanding of its relevance in the public domain.
First, remove inconsistencies across regulatory rules and territorial regimes, and ensure their credibility concerning implementation. Second, discourage the use of financial regulatory standards as means of international competition. Third, give more weight to pedagogically explaining the established regulatory standards in public, to strengthen their societal backing.
We show that the correct experiment to evaluate the effects of a fiscal adjustment is the simulation of a multi year fiscal plan rather than of individual fiscal shocks. Simulation of fiscal plans adopted by 16 OECD countries over a 30-year period supports the hypothesis that the effects of consolidations depend on their design. Fiscal adjustments based upon spending cuts are much less costly, in terms of output losses, than tax-based ones and have especially low output costs when they consist of permanent rather than stop and go changes in taxes and spending. The difference between tax-based and spending-based adjustments appears not to be explained by accompanying policies, including monetary policy. It is mainly due to the different response of business confidence and private investment.
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.
Since the outbreak of the financial crisis, the macro-prudential policy paradigm has gained increasing prominence (Bank of England, 2009; Bernanke, 2011). The dynamics of this shift in the economic discourse, and the reasons this shift has not taken place prior to the crisis have not been addressed systemically. This paper investigates the evolution of the economic discourse on systemic risk and banking regulation to better understand these changes and their timing. Further, we use our sample to inquire whether, and if so, why the economic regulatory studies failed to recommend a reliable banking regulation prior to the crisis. By following a discourse analysis, we establish that the economic discourse on banking regulation has not been suitable for providing the knowledge basis required for a dynamically reliable banking regulation, and we identify the underlying reasons for such failure. These reasons include the obsession of economic discourse with optimization and particular forms of formalism, particularly, partial equilibrium analysis. Further, the economic discourse on banking regulation excludes historical and practitioners’ discourses and ignores weak signals. We point out that post-crisis, these epistemological failures of the economic discourse on banking regulation were not sufficiently recognized and that recent attempts to conceptualize systemic risk as a negative externality and to thus price it point to the persistence of formalism, equilibrium thinking and optimization, with their attending dangers.
The implications of delegating fiscal decision making power to sub-national governments has become an area of significant interest over the past two decades, in the expectation that these reforms will lead to better and more efficient provision of public goods and services. The move towards decentralization has, however, not been homogeneously implemented on the revenue and expenditure side: decentralization has materialized more substantially on the latter than on the former, creating "vertical fiscal imbalances". These imbalances measure the extent to which sub-national governments’ expenditures are financed through their own revenues. This mismatch between own revenues and expenditures may have negative consequences for public finances performance, for example by softening the budget constraint of sub-national governments. Using a large sample of countries covering a long time period from the IMF’s Government Finance Statistics Yearbook, this paper is the first to examine the effects of vertical fiscal imbalances on fiscal performance through the accumulation of government debt. Our findings suggest that vertical fiscal imbalances are indeed relevant in explaining government debt accumulation, and call for a degree of caution when promoting fiscal decentralization.
In many cases, the dire situation of public finances calls into question the very soundness of sovereigns and prompts corrective actions with far-reaching consequences. In this context, European authorities responded with several measures on different fronts, for instance by passing the "Fiscal Compact", which entered into force on January 1, 2013. Of critical importance in this framework is the assessment of a country’s situation by way of statistical measures, in order to take corrective actions when called for according to the letter of the law. If these statistics are not correct, there is a risk of imposing draconian measures on countries that do not really need it.
We present a network model of the interbank market in which optimizing risk averse banks lend to each other and invest in non-liquid assets. Market clearing takes place through a tâtonnement process which yields the equilibrium price, while traded quantities are determined by means of a matching algorithm. We compare three alternative matching algorithms: maximum entropy, closest matching and random matching. Contagion occurs through liquidity hoarding, interbank interlinkages and fire sale externalities. The resulting network configurations exhibits a core-periphery structure, dis-assortative behavior and low clustering coefficient. We measure systemic importance by means of network centrality and input-output metrics and the contribution of systemic risk by means of Shapley values. Within this framework we analyze the effects of prudential policies on the stability/efficiency trade-off. Liquidity requirements unequivocally decrease systemic risk but at the cost of lower efficiency (measured by aggregate investment in non-liquid assets); equity requirements tend to reduce risk (hence increase stability) without reducing significantly overall investment.
We uncover a new channel for spillovers of funding dry-ups. The 2016 US money market fund (MMF) reform exogenously reduced unsecured MMF funding for some banks. We use novel data to trace those banks to a platform for corporate deposit funding. We show that intensified competition for corporate deposits spilled the funding squeeze over to other banks with no MMF exposure. These banks paid more for deposits, and their pool of funding providers deteriorated. Moreover, their lending volumes and margins declined, and their stocks underperformed. Our results suggest that banks' competitiveness in funding markets affect their competitiveness in lending markets.
The analyses of intersectoral linkages of Leontief (1941) and Hirschman (1958) provide a natural way to study the transmission of risk among interconnected banks and to measure their systemic importance. In this paper we show how classic input-output analysis can be applied to banking and how to derive six indicators that capture different aspects of systemic importance, using a simple numerical example for illustration. We also discuss the relationship with other approaches, most notably network centrality measures, both formally and by means of a simulated network.
Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks balance sheets is more complex than the single-layer-of-exposure paradigm. We use data on exposures between large European banks broken down by both maturity and instrument type to characterise the main features of the multiplex structure of the network of large European banks. This multiplex network presents positive correlated multiplexity and a high similarity between layers, stemming both from standard similarity analyses as well as a core-periphery analyses of the different layers. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a useful tool for banking regulators and supervisors. We use the dataset of exposures between large European banks to illustrate the proposed measures.
A common prediction of macroeconomic models of credit market frictions is that the tightness of financial constraints is countercyclical. As a result, theory implies a negative collateralizability premium; that is, capital that can be used as collateral to relax financial constraints provides insurance against aggregate shocks and commands a lower risk compensation compared with non-collateralizable assets. We show that a longshort portfolio constructed using a novel measure of asset collateralizability generates an average excess return of around 8% per year. We develop a general equilibrium model with heterogeneous firms and financial constraints to quantitatively account for the collateralizability premium.
Credit boom detection methodologies (such as threshold method) lack robustness as they are based on univariate detrending analysis and resort to ratios of credit to real activity. I propose a quantitative indicator to detect atypical behavior of credit from a multivariate system - a monetary VAR. This methodology explicitly accounts for endogenous interactions between credit, asset prices and real activity and detects atypical credit expansions and contractions in the Euro Area, Japan and the U.S. robustly and timely. The analysis also proves useful in real time.
In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). Within a system of quantile regressions for four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions. Using daily data, we can trace out the spillover effects over time in a set of impulse response functions and find that they reach their peak after 10 to 15 days.
Zur Reform der Einlagensicherung: Elemente einer anreizkompatiblen Europäischen Rückversicherung
(2020)
Bankeinlagen bis 100.000 Euro sind de jure überall im Euroraum gleichermaßen vor Verlusten geschützt. De facto hängt der Wert dieser gesetzlichen Haftungszusage unter anderem von der Ausstattung des nationalen Sicherungsfonds und der relativen Größe des Bankensektors in einer Volkswirtschaft ab. Um die Homogenität des Einlagenschutzes zu gewährleisten und die Bankenunion zu vollenden, bedarf es einer einheitlichen europäischen Einlagensicherung. Die bestehende implizite Risikoteilung im Euroraum ist ordnungspolitisch nicht wünschenswert. Ferner kann eine explizite und glaubwürdige Zweitsicherung Fehlanreize zur Übernahme exzessiver Risiken verhindern, bevor es zum Schadensfall kommt. Daher plädiert dieser Beitrag für ein zweistufiges, streng subsidiär organisiertes Rückversicherungsmodell: Nationale Erstversicherungen würden einen festgeschriebenen Teil, die europäische Rückversicherung nachrangig den Rest der Deckungssumme besichern. Die Rückversicherung gewährt diese Liquiditätshilfen in Form von Kassenkrediten. Weil die Haftung auf nationaler Ebene verbleibt, werden Risiken geteilt aber nicht vergemeinschaftet. Marktgerechte Prämien müssen nicht nur das individuelle Risikogewicht einer Bank sondern auch länderspezifische Risikofaktoren berücksichtigen. Zuletzt braucht der Rückversicherer umfangreiche Aufsichtsrechte, um die Zahlungsfähigkeit der Erstversicherer mit Hinblick auf die nationalen Haftungspflichten jederzeit sicherzustellen.
The European Central Bank (ECB) has finalized its comprehensive assessment of the solvency of the largest banks in the euro area and on October 26 disclosed the results of this assessment. In the present paper, Acharya and Steffen compare the outcomes of the ECB's assessment to their own benchmark stress tests conducted for 39 publically listed financial institutions that are also included in the ECB's regulatory review. The authors identify a negative correlation between their benchmark estimates for capital shortfalls and the regulatory capital shortfall, but a positive correlation between their benchmark estimates for losses under stress both in the banking book and in the trading book. They conclude that the regulatory stress test outcomes are potentially heavily affected by discretion of national regulators in measuring what is capital, and especially the use of risk-weighted assets in calculating the prudential capital requirement.
We investigate the transmission of central bank liquidity to bank deposits and loan spreads in Europe over the January 2006 to June 2010 period. We find evidence consistent with an impaired transmission channel due to bank risk. Central bank liquidity does not translate into lower loan spreads for high-risk banks, even as it lowers deposit rates for both high-risk and low-risk banks. This adversely affects the balance sheets of high-risk bank borrowers, leading to lower payouts, lower capital expenditures, and lower employment. Overall, our results suggest that banks’ capital constraints at the time of an easing of monetary policy pose a challenge to the effectiveness of the bank lending channel and the effectiveness of the central bank as a lender of last resort.
The ECB’s Outright Monetary Transactions (OMT) program, launched in summer 2012, indirectly recapitalized periphery country banks through its positive impact on the value of sovereign bonds. However, the regained stability of the European banking sector has not fully transferred into economic growth. We show that zombie lending behavior of banks that still remained undercapitalized after the OMT announcement is an important reason for this development. As a result, there was no positive impact on real economic activity like employment or investment. Instead, firms mainly used the newly acquired funds to build up cash reserves. Finally, we document that creditworthy firms in industries with a high prevalence of zombie firms suffered significantly from the credit misallocation, which slowed down the economic recovery.
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.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
Against the background of the European debt crisis, the Research Center SAFE, in the fall of 2013, had issued a call for papers on the topic “Austerity and Economic Growth: Concepts for Europe”, with the objective of soliciting research proposals focusing on the nature of the relationship between austerity, debt sustainability and growth. Each of the five funded projects brought forth an academic paper and a shortened, non-technical policy brief. These policy papers are presented in the present collection of policy letters, edited by Alfons Weichenrieder.
The first paper by Alberto Alesina, Carlo Favero and Francesco Giavazzi looks into the question of how fiscal consolidations influence the real economy. Harris Dellas and Dirk Niepelt emphasize that fiscal austerity is a signal that investors use to tell apart governments with high and low default costs that accordingly will have a high or low probability of repayment.The paper by Benjamin Born, Gernot Müller and Johannes Pfeiffer,looks at the impact of austerity measures on government bond spreads. Oscar Jorda and Alan M. Taylor, in the fourth contribution, put into question whether the narrative records of fiscal consolidation plans are really exogenous. The final study by Enrique Mendoza, Linda Tesar and Jing Zhang suggests that fiscal consolidation should largely depend on expenditure cuts, rather than tax increases that may fail, when fiscal space is exhausted.
Nach der Bundestagswahl am 26. September 2021 wird sich die künftige Bundesregierung mit einer Reihe drängender Herausforderungen befassen müssen. Aus Sicht des Leibniz-Instituts für Finanzmarktforschung SAFE haben die folgenden, miteinander verbundenen Einzelpunkte dabei Priorität:
1. Schaffung eines ordnungspolitischen Pakets zur Sicherung globaler Gemeinschaftsgüter, wie etwa des Klimas
2. Initiative zum Aufbau notwendiger Datensätze und Standards für eine zielgenaue Nachhaltigkeitsgestaltung an den Finanzmärkten
3. regulatorischer Fahrplan zur Erfassung, Ermöglichung und Einhegung einer digitalen Transformation des Finanzsystems
4. Vollendung der Bankenunion, insbesondere durch einen „europäischen Schlussstein“: der Schaffung einer einheitlichen Aufsicht und Letztabsicherung
5. Durchbrechung des „Doom-Loop“ zwischen Staaten und Banken in Europa, insbesondere durch Begrenzung des Umfangs, in dem eigene Staatsanleihen im Portfolio von Banken liegen dürfen
6. ernsthafter Versuch zur Schaffung eines einheitlichen und integren europäischen Kapitalmarkts mit einer Aufsicht nach US-Vorbild
7. Banken- und Kapitalmarktunion als wesentliche Bausteine für eine grundlegende Reform der Altersversorgung mit mehr Teilhabe aller Bürger:innen an der Leistungsentwicklung der Volkswirtschaft