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As part of the Next Generation EU (NGEU) program, the European Commission has pledged to issue up to EUR 250 billion of the NGEU bonds as green bonds, in order to confirm their commitment to sustainable finance and to support the transition towards a greener Europe. Thereby, the EU is not only entering the green bond market, but also set to become one of the biggest green bond issuers. Consequently, financial market participants are eager to know what to expect from the EU as a new green bond issuer and whether a negative green bond premium, a so-called Greenium, can be expected for the NGEU green bonds. This research paper formulates an expectation in regards to a potential Greenium for the NGEU green bonds, by conducting an interview with 15 sustainable finance experts and analyzing the public green bond market from September 2014 until June 2021, with respect to a potential green bond premium and its underlying drivers. The regression results confirm the existence of a significant Greenium (-0.7 bps) in the public green bond market and that the Greenium increases for supranational issuers with AAA rating, such as the EU. Moreover, the green bond premium is influenced by issuer sector and credit rating, but issue size and modified duration have no significant effect. Overall, the evaluated expert interviews and regression analysis lead to an expected Greenium for the NGEU green bonds of up to -4 bps, with the potential to further increase in the secondary market.
Target date funds in corporate retirement plans grew from $5B in 2000 to $734B in 2018, partly because federal regulation sanctioned these as default investments in automatic enrollment plans. We show that adopters delegated pension investment decisions to fund managers selected by plan sponsors. Including these funds in retirement saving menus raised equity shares, boosted bond exposures, curtailed cash/company stock holdings, and reduced idiosyncratic risk. The adoption of low-cost target date funds may enhance retirement wealth by as much as 50 percent over a 30-year horizon.
A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different econometric approach. Their main aim has been to revise the consensus in the literature that oil demand shocks are a more important determinant of oil price fluctuations than oil supply shocks. Substantial progress has been made in recent years in sorting out the pros and cons of the underlying econometric methodologies and data in this debate, and in separating claims that are supported by empirical evidence from claims that are not. The purpose of this paper is to take stock of the VAR literature on global oil markets and to synthesize what we have learned. Combining this evidence with new data and analysis, I make the case that the concerns regarding the existing VAR oil market literature have been overstated and that the results from these models are quite robust to changes in the model specification.
This paper sets up an experimental asset market in the laboratory to investigate the effects of ambiguity on price formation and trading behavior in financial markets. The obtained trading data is used to analyze the effect of ambiguity on various market outcomes (the price level, volatility, trading activity, market liquidity, and the degree of speculative trading) and to test the quality of popular empirical market-based measures for the degree of ambiguity. We find that ambiguity decreases market prices and trading activity; ambiguity leads to lower market liquidity through wider bid-ask spreads; and ambiguity leads to less speculative trading. We also find that popular market-based measures of ambiguity used in the empirical literature do not seem to correctly capture the true degree of ambiguity.
Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identi.ed VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have been used in the literature to illustrate this potential problem are invalid. Speci.cally, we show that it does not make sense from a Bayesian point of view to characterize the impulse response prior based on the distribution of the impulse responses conditional on the maximum likelihood estimator of the reduced-form parameters, since the the prior does not, in general, depend on the data. We illustrate that this approach tends to produce highly misleading estimates of the impulse response priors. We formally derive the correct impulse response prior distribution and show that there is no evidence that typical sign-identi.ed VAR models estimated using conventional priors tend to imply unintentionally informative priors for the impulse response vector or that the corre- sponding posterior is dominated by the prior. Our evidence suggests that concerns about the Haar prior for the rotation matrix have been greatly overstated and that alternative estimation methods are not required in typical applications. Finally, we demonstrate that the alternative Bayesian approach to estimating sign-identi.ed VAR models proposed by Baumeister and Hamilton (2015) su¤ers from exactly the same conceptual shortcoming as the conventional approach. We illustrate that this alternative approach may imply highly economically implausible impulse response priors.
Since the 1970s, exports and imports of manufactured goods have been the engine of international trade and much of that trade relies on container shipping. This paper introduces a new monthly index of the volume of container trade to and from North America. Incorporating this index into a structural macroeconomic VAR model facilitates the identification of shocks to domestic U.S. demand as well as foreign demand for U.S. manufactured goods. We show that, unlike in the Great Recession, the primary determinant of the U.S. economic contraction in early 2020 was a sharp drop in domestic demand. Although detrended data for personal consumption expenditures and manufacturing output suggest that the U.S. economy has recovered to near 90% of pre-pandemic levels as of March 2021, our structural VAR model shows that the component of manufacturing output driven by domestic demand had only recovered to 59% of pre-pandemic levels and that of real personal consumption only to 76%. The difference is mainly accounted for by unexpected reductions in frictions in the container shipping market.
Using the exact wording of the ECB’s definition of price-stability, we started a representative online survey of German citizens in January 2019 that is designed to measure long-term inflation expectations and the credibility of the inflation target. Our results indicate that credibility has decreased in our sample period, particularly in the course of the deep recession implied by the COVID-19 pandemic. Interestingly, even though inflation rates in Germany have been clearly below 2% for several years, credibility has declined mainly because Germans increasingly expect that inflation will be much higher than 2% over the medium term. We investigate how inflation expectations and the impact of the pandemic depend on personal characteristics including age, gender, education, income, and political attitude.
We raise some critical points against a naïve interpretation of “green finance” products and strategies. These critical insights are the background against which we take a closer look at instruments and policies that might allow green finance to become more impactful. In particular, we focus on the role of a taxonomy and investor activism. We also describe the interaction of government policies with green finance practice – an aspect, which has been mostly neglected in policy debates but needs to be taken into account. Finally, the special case of green government bonds is discussed.
Climate change is one of the highest-ranking issues on the political and social agenda. Vulnerabilities of the world ecosystem laid bare by the COVID-19 pandemic and the potential damage for the human and business life made the need for urgent action clear once again. Corporations are one of the main actors that will play a major role in the decarbonisation of the economy. They need to put forward a net zero strategy and targets, transitioning to net-zero by 2050. Yet, an important but rather overlooked stakeholder group in the sustainability debates can pose a significant stumbling block in this transition: employees. Although climate action has huge benefits by ameliorating adverse environmental events and is expected to have overall positive impact on employment, net zero transition in companies, especially in certain sectors and regions, will cause substantial adverse employment effects for the workforce. This has the potential to slow down or even derail the necessary climate action in companies. In this regard, just transition is a promising concept, which calls for a swift and decisive climate action in corporations while taking account of and mitigating adverse effects for their workforce. If well implemented, it can accelerate net zero transition in companies. This potential clash of environmental (E) and social (S) aspects of ESG agenda, materialised in the companies’ net zero transition, and its potential remedy, just transition, have important implications for corporate governance and finance, especially for directors’ duties & executive remuneration, sustainability disclosures, institutional investors’ engagement and green finance.
Over the course of the last financial crises, retail investors have been identified to bear a major share of the invoked financial losses. As a consequence, financial market regulators put major effort on retail investor protection, especially following the Great Financial Crisis of 2007-2009. The major legislative initiatives, such as in the Dodd-Frank Act in the United States, seemingly manifest retail investors’ overly fragile role among the variety of professional investors in the financial market by establishing additional protection requirements for retail investors. A vast majority of related international academic literature is supporting those steps. However, considering the most recent developments that occurred in the US financial markets, the dogma of the lamb-like retail investor seems to be crumbling: In 2021, under the synonym “WallStreetBets” retail investors systematically colluded in investment bets which eventually disrupted not only financial markets by distorting stock price formation of single firms but also systematically squeezed sizeable positions of institutional investors. The key question arises, how retail investors have changed, such that they not only became a source of price distortions and market turmoil but also endanger professional institutional investors. In this thesis, I study this changing role and investment behavior of retail investors, taking into account the retail investor’s wellestablished and researched behavioral characteristics to the changing environmental aspects such as regulation and the adaption and usage of technology for information gathering and collaboration. Based on the combination of those different research streams, I am able to deduct the sequential consequences of these developments for financial markets.
The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data.
For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.
We raise some critical points against a naïve interpretation of “green finance” products and strategies. These critical insights are the background against which we take a closer look at instruments and policies that might allow green finance to become more impactful. In particular, we focus on the role of a taxonomy and investor activism. We also describe the interaction of government policies with green finance practice – an aspect, which has been mostly neglected in policy debates but needs to be taken into account. Finally, the special case of green government bonds is discussed.
Analysing causality among oil prices and, in general, among financial and economic variables is of central relevance in applied economics studies. The recent contribution of Lu et al. (2014) proposes a novel test for causality— the DCC-MGARCH Hong test. We show that the critical values of the test statistic must be evaluated through simulations, thereby challenging the evidence in papers adopting the DCC-MGARCH Hong test. We also note that rolling Hong tests represent a more viable solution in the presence of short-lived causality periods.
This paper examines how the transmission of government portfolio risk arising from maturity operations depends on the stance of monetary/fiscal policy. Accounting for risk premia in the fiscal theory allows the government portfolio to affect the expected inflation, even in a frictionless economy. The effects of maturity rebalancing on expected inflation in the fiscal theory directly depend on the conditional nominal term premium, giving rise to an optimal debt maturity policy that is state dependent. In a calibrated macro-finance model, we demonstrate that maturity operations have sizable effects on expected inflation and output through our novel risk transmission mechanism.
We analyze the extent to which individual audit partners influence the audited narrative disclosures in their clients’ financial reports. Using a sample of 3,281,423 private and public client firm-pairs, we find that the similarity among audited narrative disclosures is higher when two client firms share the same audit partner. Specifically, we find that the wording similarity of management reports (notes) increases by 30 (48) percent, the content similarity by 29 (49) percent, and the structure similarity by 48 (121) percent. Moreover, we find that audit partners in particular are relevant for their clients’ narrative disclosures because the increase in narrative disclosure similarity when sharing the same audit partner is nine (four) times greater than when sharing the same audit firm (audit office). We show that this influence of audit partners goes beyond adding boilerplate statements and, using novel field evidence, we shed light on the underlying mechanisms. Our findings are economically relevant because a stronger involvement of audit partners with their clients’ narratives is associated with a higher quality of narrative disclosures, which helps users better predict the future profitability of client firms.
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.