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The European Commission is trying to reboot the CMU project: The High-Level Forum on Capital Markets Union – a group of 28 selected experts from industry, academia and civil society – is expected to submit policy recommendations by the end of May 2020 which will feed into the Commission’s new CMU agenda. This contribution is largely based on a letter to the High-Level Forum that gives feedback on the Interim Report published in February. There, we introduce a comprehensive approach to distinguish, from a functional finance perspective, between the ‘game changers’ and what is nice to have. We highlight the importance of common and consistent supervisory practices across Member States and recommend building up a European Securities and Exchange Commission (E-SEC) according to the American model.
The Wirecard scandal is a wake-up call alerting German politics to the importance of securities market integrity. The role of market supervision is to ensure the smooth functioning of capital markets and their integrity, creating trust among and acceptance by investors locally and globally. The existing patchwork of national supervisory practice in Europe is under discussion today, in the wake of Brexit that will end the role of London as a de-facto lead supervisor in stock and bond markets. A fundamental overhaul of a fragmented securities markets supervisory regime in Europe would offer the potential to lead to the establishment of an independent European Single Market Supervisor (ESMS). Endowed with strong enforcement powers, and supported by the existing national agencies, the ESMS would be entrusted with ensuring a uniform market standard as to transparency and other issues of market integrity across Europe. This would not rule out maintaining a variety of market organization structures at the national level. The ESMS would need executive powers in the world of markets (i.e. securities and trading), much like the SSM in the world of banking. To fill this new role, ESMS would have to be established as a new, independent institution, including an enormously scaled up staff if compared, e.g., to ESMA.
This paper studies the impact of financial sector size and leverage on business cycles and risk-free rates dynamics. We model a general equilibrium productive economy where financial intermediaries provide costly risk mitigation to households by pooling the idiosyncratic risks of their investment activities. We find that leverage amplifies variations of intermediaries’ relative size, but may also mitigate the business cycle. Moreover, it makes risk-free rates pro-cyclical. Households benefit the most when the financial sector is neither too small, thus avoiding high consumption fluctuations and costly mitigation, nor too big, so that fewer resources are lost after intermediation costs.
Advertising arbitrage
(2020)
Arbitrageurs with a short investment horizon gain from accelerating price discovery by advertising their private information. However, advertising many assets may overload investors' attention, reducing the number of informed traders per asset and slowing price discovery. So arbitrageurs optimally concentrate advertising on just a few assets, which they overweight in their portfolios. Unlike classic insiders, advertisers prefer assets with the least noise trading. If several arbitrageurs share information about the same assets, inefficient equilibria can arise, where investors' attention is overloaded and substantial mispricing persists. When they do not share, the overloading of investors' attention is maximal.
Perspectives on participation in continuous vocational education training - an interview study
(2020)
In European industrialized countries, a large number of companies in the healthcare, hotel, and catering sectors, as well as in the technology sector, are affected by demographic, political, and technological developments resulting in a greater need of skilled workers with a simultaneous shortage of skilled workers (CEDEFOP, 2015, 2016). Consequently, employers have to address workers who have not been taken into account such as low-skilled workers, workers returning from a career break, people with a migrant background, older people, and jobseekers and train them, in order to guarantee the professionalization of this workforce (Festing and Harsch, 2018). Continuing vocational education and training (CVET) is seen as an indispensable tool; because CVET has advantages for both employers and employees, it helps to increase the productivity of companies (Barrett and O’Connell, 2001), to prevent the widening of socioeconomic disparities (Dieckhoff, 2007), and to open up career opportunities for the workforce (Rubenson and Desjardins, 2009). However, participation rate on CVET seems to differ, depending on institutional factors (such as sector and size of the company) and individual characteristics (such as qualification level, migration background, age and time of absence from work) (e.g., Rubenson and Desjardins, 2009; Wiseman and Parry, 2017). In contrast to previous research, our study aims to provide a holistic view of reasons for and against CVET, combining the different perspectives of employers and (potential) employees. The analysis of reasons and barriers was carried out based on semi-structured interviews. Fifty-seven employers, 73 employees, and 42 jobseekers (potential employees) from the sectors retail, healthcare and social services, hotels and catering, and technology were interviewed. Results point to considerable differences in the reasons and barriers mentioned by the disadvantaged groups. These differences are particularly significant between employees on the one side and employers, as well as jobseekers, on the other side, while the reasons to attend CVET of jobseekers are more similar to those of employers. The results can be used to tailor CVET more closely to the needs of (potential) employees and thus strengthen both the qualification and career opportunities of (potential) employees and the competitiveness and productivity of companies.
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.
Learning to fly through informational turbulence: critical thinking and the case of the minimum wage
(2020)
The paper addresses online reasoning and information processing with respect to a much debated issue: the pros and cons of the minimum wage. Like with all controversial issues, one can easily remain in a self-reinforcing bubble, once one has taken sides, and immunize oneself against criticism. Paradoxically, the more information we have at our disposal, the easier this gets (Roetzel, 2019). The only (and possibly universal) antidote seems to be “critical thinking” (Ennis, 1987, 2011). However, critical thinking is a very broad concept, purported to include diverse kinds of information processing, and it is also thought to be content-specific. Therefore, we aim at addressing both understanding of content knowledge and reasoning processes. We pursue three goals with this paper: First, we conduct a conceptual analysis of the learning content and of reasoning patterns for and against the minimum wage. Second, we explicate an inferential framework that can be applied for processes of critical thinking. Third, teaching strategies are discussed to support reasoning processes and to promote critical thinking skills.
This working paper suggests to analyse agencification as a double process of institutional and policy centralisation. To that end, it develops a categorisation of agencies that incorporates these two dimensions. More specifically, it is argued that mixed outcomes where the levels of institutional and policy centralisation diverge can be expected to be the rule rather than the exception, in line with the hybrid nature of EU agencies as inbetweeners. Moreover, the fiduciary setting hits important legal constraints given the limits to delegation in the EU context. Against this backdrop a process whereby institutional centralisation develops incrementally and remains limited, yet is accompanied by a process of substantial policy centralisation, appears as the most promising path for EU agencification. A fiduciary setting, where a strong agency enjoys a high degree of independence and operates in a centralised policy space, by contrast, should be the exception. The comparative study of the process of agencification in the energy and banking sector is insightful in the light of these expectations. The incremental nature of institutional change in energy exemplifies the usual path of agencification, which is conducive to a weak agency operating in a relatively centralised policy space. Agencification in banking, by contrast, has led to a rather unusual outcome where the strong agency model combines with a fragmented policy context.
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.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
The modern tontine : an innovative instrument for longevity risk management in an aging society
(2020)
We investigate whether a historical pension concept, the tontine, yields enough innovative potential to extend and improve the prevailing privately funded pension solutions in a modern way. The tontine basically generates an age-increasing cash flow, which can help to match the increasing financing needs at old ages. In contrast to traditional pension products, however, the tontine generates volatile cash flows, which means that the insurance character of the tontine cannot be guaranteed in every situation. By employing Multi Cumulative Prospect Theory (MCPT) we answer the question to what extent tontines can be a complement to or a substitute for traditional annuities. We find that it is only optimal to invest in tontines for a certain range of initial wealth. In addition, we investigate in how far the tontine size, the volatility of individual liquidity needs and expected mortality rates contribute to the demand for tontines.
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.
We extend the canonical income process with persistent and transitory risk to shock distributions with left-skewness and excess kurtosis, to which we refer as higher- order risk. We estimate our extended income process by GMM for household data from the United States. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The existing tax and transfer system reduces dispersion and left-skewness of shocks. We then show that in a standard incomplete-markets life-cycle model, first, higher-order risk has sizable welfare implications, which depend crucially on risk attitudes of households; second, higher-order risk matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, higher-order risk has non-trivial implications for the degree of self-insurance against both transitory and persistent shocks.
The case for corona bonds
(2020)
Corona bonds are feasible and important to preserve the European project. We set out a number of principles that might serve as a blueprint for the European institutions. Importantly, Corona bonds could be issued through a new public law entity and include all the safeguards required for the protection of the fundamental values of the EU. This proposal is pragmatic in the sense that it facilitates the choice European leaders have to make now; necessary to secure the resilience of the European Union. The political risks are significantly higher now than in 2010. The gargantuan challenge of tackling the combined impact of climate change, migration, digitalization, geopolitical shifts, and the spread of autocracy, requires leadership and joint action by the Council and the Eurogroup.
Making agriculture sustainable is a global challenge. In the European Union (EU), the Common Agricultural Policy (CAP) is failing with respect to biodiversity, climate, soil, land degradation as well as socio‐economic challenges.
The European Commission's proposal for a CAP post‐2020 provides a scope for enhanced sustainability. However, it also allows Member States to choose low‐ambition implementation pathways. It therefore remains essential to address citizens' demands for sustainable agriculture and rectify systemic weaknesses in the CAP, using the full breadth of available scientific evidence and knowledge.
Concerned about current attempts to dilute the environmental ambition of the future CAP, and the lack of concrete proposals for improving the CAP in the draft of the European Green Deal, we call on the European Parliament, Council and Commission to adopt 10 urgent action points for delivering sustainable food production, biodiversity conservation and climate mitigation.
Knowledge is available to help moving towards evidence‐based, sustainable European agriculture that can benefit people, nature and their joint futures.
The statements made in this article have the broad support of the scientific community, as expressed by above 3,600 signatories to the preprint version of this manuscript. The list can be found here (https://doi.org/10.5281/zenodo.3685632).
A free Plain Language Summary can be found within the Supporting Information of this article.
We have designed and implemented an experimental module in the 2014 Health and Retirement Study to measure older persons' willingness to defer claiming of Social Security benefits. Under the current system’ status quo where delaying claiming boosts eventual benefits, we show that 46% of the respondents would delay claiming and work longer. If respondents were instead offered an actuarially fair lump sum payment instead of higher lifelong benefits, about 56% indicate they would delay claiming. Without a work requirement, the average amount needed to induce delayed claiming is only $60,400, while when part-time work is stipulated, the amount is slightly higher, $66,700. This small difference implies a low utility value of leisure foregone, of under 20% of average household income.
The increasing digitization of the world of work is associated with accelerated structural changes. These are connected with changed qualification profiles and thus new challenges for vocational education and training (VET). Companies, vocational schools and other educational institutions must respond appropriately. The volume focuses on the diverse demands placed on teachers, learners and educational institutions in vocational education and training and aims to provide up-to-date results on learning in the digital age.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
Participation in further education is a central success factor for economic growth and societal as well as individual development. This is especially true today because in most industrialized countries, labor markets and work processes are changing rapidly. Data on further education, however, show that not everybody participates and that different social groups participate to different degrees. Activities in continuous vocational education and training (CVET) are mainly differentiated as formal, non-formal and informal CVET, whereby further differences between offers of non-formal and informal CVET are seldom elaborated. Furthermore, reasons for participation or non-participation are often neglected. In this study, we therefore analyze and compare predictors for participation in both forms of CVET, namely, non-formal and informal. To learn more about the reasons for participation, we focus on the individual perspective of employees (invidual factors, job-related factors, and learning biography) and additionally integrate institutional characteristics (workplace and company-based characteristics). The results mainly show that non-formal CVET is still strongly influenced by institutional settings. In the case of informal CVET, on the other hand, the learning biography plays a central role.
Capital in the corona crisis
(2020)
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.
We use data from a German online brokerage and a survey to show that retail investors sharply reduce risk-taking in response to nearby firm bankruptcies, which are not pre- dictive of returns. The effects on trading are spatially highly concentrated, immediate and not persistent. They seem to operate through more pessimistic expected returns and increased risk aversion and do not reflect wealth effects or changes in background risks. Investors learn about bankruptcies through immediate coverage in local newspapers. Our findings suggest that non-informative local experiences that make downside risks of stock investment more salient contribute to idiosyncratic short-term fluctuations in trading.
Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also a↵ect sustainable investments leading to the identification of di↵erent investment universes and consequently to the creation of di↵erent benchmarks. This implies that in the asset management industry it is extremely dicult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the e↵ect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.
Incentivized experiments in which individuals receive monetary rewards according to the outcomes of their decisions are regarded as the gold standard for preference elicitation in experimental economics. These task-related real payments are considered necessary to reveal subjects' "true preferences". Using a systematic, large-sample approach with three subject pools of private investors, professional investors, and students, we test the effect of task-related monetary incentives on risk preferences elicited in four standard experimental tasks. We find no systematic differences in behavior between subjects in the incentivized and non-incentivized regimes. We discuss implications for academic research and for applications in the field.
Household finance
(2020)
Household financial decisions are complex, interdependent, and heterogeneous, and central to the functioning of the financial system. We present an overview of the rapidly expanding literature on household finance (with some important exceptions) and suggest directions for future research. We begin with the theory and empirics of asset market participation and asset allocation over the lifecycle. We then discuss house-hold choices in insurance markets, trading behavior, decisions on retirement saving, and financial choices by retirees. We survey research on liabilities, including mortgage choice, refinancing, and default, and household behavior in unsecured credit markets, including credit cards and payday lending. We then connect the household to its social environment, including peer effects, cultural and hereditary factors, intra-household financial decision making, financial literacy, cognition and educational interventions. We also discuss literature on the provision and consumption of financial advice.
We introduce Implied Volatility Duration (IVD) as a new measure for the timing of uncertainty resolution, with a high IVD corresponding to late resolution. Portfolio sorts on a large cross-section of stocks indicate that investors demand on average about seven percent return per year as a compensation for a late resolution of uncertainty. In a general equilibrium model, we show that `late' stocks can only have higher expected returns than `early' stocks if the investor exhibits a preference for early resolution of uncertainty. Our empirical analysis thus provides a purely market-based assessment of the timing preferences of the marginal investor.
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.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is una↵ected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
Prior studies indicate the protective role of Ultraviolet-B (UVB) radiation in human health, mediated by vitamin D synthesis. In this observational study, we empirically outline a negative association of UVB radiation as measured by ultraviolet index (UVI) with the number of COVID-19 deaths. We apply a fixed-effect log-linear regression model to a panel dataset of 152 countries over 108 days (n = 6524). We use the cumulative number of COVID-19 deaths and case-fatality rate (CFR) as the main dependent variables and isolate the UVI effect from potential confounding factors. After controlling for time-constant and time-varying factors, we find that a permanent unit increase in UVI is associated with a 1.2 percentage points decline in daily growth rates of cumulative COVID-19 deaths [p < 0.01] and a 1.0 percentage points decline in the CFR daily growth rate [p < 0.05]. These results represent a significant percentage reduction in terms of daily growth rates of cumulative COVID-19 deaths (− 12%) and CFR (− 38%). We find a significant negative association between UVI and COVID-19 deaths, indicating evidence of the protective role of UVB in mitigating COVID-19 deaths. If confirmed via clinical studies, then the possibility of mitigating COVID-19 deaths via sensible sunlight exposure or vitamin D intervention would be very attractive.
Background: Nations are imposing unprecedented measures at large-scale to contain the spread of COVID-19 pandemic. Recent studies indicate that measures such as lockdowns may have slowed down the growth of COVID-19. However, in addition to substantial economic and social costs, these measures also limit the exposure to Ultraviolet-B radiation (UVB). Emerging observational evidence indicate the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. In this observational study, we empirically outline the independent protective roles of lockdown and UVB exposure as measured by ultraviolet index (UVI), whilst also examining whether the severity of lockdown is associated with a reduction in the protective role.
Methods: We apply a log-linear fixed-effects model to a panel dataset of 162 countries over a period of 108 days (n=6049). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth-rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors.
Findings: After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with 17% [-1.8 percentage points] and 77% [-7.9 percentage points] decline in COVID-19 deaths growth rate, indicating their respective protective roles. However, the widely utilized and least severe lockdown (recommendation to not leave the house) already fully mitigates the protective role of UVI by 95% [1.8 percentage points] indicating its downside.
Interpretation: We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find consistent evidence that increase in lockdown severity is associated with a significant reduction in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have provided even more substantial health benefits, than lockdowns alone. For example, we estimate that there would be 21% fewer deaths on average with sufficient UVB exposure while people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns and may support policy decision making in countries imposing such measures.
Competing Interest Statement: RKM is a PhD researcher at Goethe University, Frankfurt. He also is an employee of a multinational chemical company involved in vitamin D business and holds the shares of the company. This study is intended to contribute to the ongoing COVID-19 crisis and is not sponsored by his company. All other authors declare no competing interests. The views expressed in the paper are those of the authors and do not represent that of any organization. No other relationships or activities that could appear to have influenced the submitted work.
This Policy Letter outlines a pandemic insurance solution through a pandemic-related “Insurance Linked Bond”. It would be originated by governments, with a principal amount to cover significant costs resulting from a pandemic. These bonds, which would be traded on a secondary market, generate a risk-adequate return for private and institutional investors that is financed through the insurance premiums paid by the public domain. In case of a pre-defined pandemic trigger event, the principal of the bond becomes available for the originating governments to cover pandemic-related costs. Through this approach, governments can insure themselves against future pandemic-related risks, while funding comes primarily from private and institutional investors.
We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated to fundamentals, typically observed during a flash crash. Slow traders provide liquidity instead of HFTs, taking advantage of the discounted price. We thus uncover a trade-o↵ between the greater liquidity and efficiency provided by HFTs in normal times, and the disruptive consequences of their trading activity during distressed times.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is unaffected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
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.
In this paper we adopt the Hamiltonian Monte Carlo (HMC) estimator for DSGE models by implementing it into a state-of-the-art, freely available high-performance software package. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model on US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm which permits the estimation of DSGE models with ill-behaved posterior densities.