Working Paper
Refine
Year of publication
Document Type
- Working Paper (2353) (remove)
Language
- English (2353) (remove)
Is part of the Bibliography
- no (2353)
Keywords
- Deutschland (115)
- USA (51)
- Geldpolitik (48)
- monetary policy (46)
- Schätzung (45)
- Europäische Union (43)
- Bank (38)
- Corporate Governance (36)
- Monetary Policy (31)
- Inflation (23)
Institute
- Center for Financial Studies (CFS) (1378)
- Wirtschaftswissenschaften (1308)
- Sustainable Architecture for Finance in Europe (SAFE) (740)
- House of Finance (HoF) (606)
- Institute for Monetary and Financial Stability (IMFS) (173)
- Rechtswissenschaft (148)
- Informatik (114)
- Foundation of Law and Finance (50)
- Exzellenzcluster Die Herausbildung normativer Ordnungen (34)
- Gesellschaftswissenschaften (29)
This paper studies a household’s optimal demand for a reverse mortgage. These contracts allow homeowners to tap their home equity to finance consumption needs. In stylized frameworks, we show that the decision to enter a reverse mortgage is mainly driven by the dierential between the aggregate appreciation of the house price and principal limiting factor on the one hand and the funding costs of a household on the other hand. We also study a rich life-cycle model that can explain the low demand for reverse mortgages as observed in US data. In this model, we analyze the optimal response of a household that is confronted with a health shock or financial disaster. If an agent suers from an unexpected health shock, she reduces the risky portfolio share and is more likely to enter a reverse mortgage. On the other hand, if there is a large drop in the stock market, she keeps the risky portfolio share almost constant by buying additional shares of stock. Besides, the probability to take out a reverse mortgage is hardly aected.
The ruling of the German Federal Constitutional Court and its call for conducting and communicating proportionality assessments regarding monetary policy have been the subject of some controversy. However, it can also be understood as a way to strengthen the de-facto independence of the European Central Bank. The authors shows how a regular proportionality check could be integrated in the ECB’s strategy that is currently undergoing a systematic review. In particular, they propose to include quantitative benchmarks for policy rates and the central bank balance sheet. Deviations from such benchmarks can have benefits in terms of the intended path for inflation while involving costs in terms of risks and side effects that need to be balanced. Practical applications to the euro area are provided
In this paper we adapt the Hamiltonian Monte Carlo (HMC) estimator to DSGE models, a method presently used in various fields due to its superior sampling and diagnostic properties. We implement it into a state-of-theart, freely available high-performance software package, STAN. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model using US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition, we find bimodality in the Smets-Wouters model even if we estimate the model using the original tight priors. Finally, we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm to create a powerful tool which permits the estimation of DSGE models with ill-behaved posterior densities.
This paper determines the cost of employee stock options (ESOs) to shareholders. I present a pricing method that seeks to replicate the empirics of exercise and cancellation as good as possible. In a first step, an intensity-based pricing model of El Karoui and Martellini is adapted to the needs of ESOs. In a second step, I calibrate the model with a regression analysis of exercise rates from the empirical work of Heath, Huddart and Lang. The pricing model thus takes account for all effects captured in the regression. Separate regressions enable me to compare options for top executives with those for subordinates. I find no price differences. The model is also applied to test the precision of the fair value accounting method for ESOs, SFAS 123. Using my model as a reference, the SFAS method results in surprisingly accurate prices.
JEL classification: G13; J33; M41; M52
This paper studies the link between bank recapitalization and welfare in a dynamic production economy. The model features financial frictions because banks benefit of a cost advantage at monitoring firms and face costly equity issuance. The competitive equilibrium outcome is inefficient because agents do not internalize the effects banks’ capitalization over the allocation of capital, its price and, in turn, firms investments. It follows, individual recapitalizations are sub-optimal and bailout policies may benefit social welfare in the long-run. Bailouts improve capital allocation in states where aggregate banks are poorly capitalized, therefore enhancing their market valuation, fostering investments, and stabilizing the economy recovery path.
Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.
The Multilingual Assessment Instrument for Narratives (MAIN) is a theoretically grounded toolkit that employs parallel pictorial stimuli to explore and assess narrative skills in children in many different languages. It is part of the LITMUS (Language Impairment Testing in Multilingual Settings) battery of tests that were developed in connection with the COST Action IS0804 Language Impairment in a Multilingual Society: Linguistic Patterns and the Road to Assessment (2009−2013). MAIN has been designed to assess both narrative production and comprehension in children who acquire one or more languages from birth or from early age. Its design allows for the comparable assessment of narrative skills in several languages in the same child and in different elicitation modes: Telling, Retelling and Model Story. MAIN contains four parallel stories, each with a carefully designed six-picture sequence based on a theoretical model of multidimensional story organization. The stories are controlled for cognitive and linguistic complexity, parallelism in macrostructure and microstructure, as well as for cultural appropriateness and robustness. As a tool MAIN had been used to compare children’s narrative skills across languages, and also to help differentiate between children with and without developmental language disorders, both monolinguals and bilinguals.
This volume consists of two parts. The main content of Part I consists of 33 papers describing the process of adapting and translating MAIN to a large number of languages from different parts of the world. Part II contains materials for use for about 80 languages, including pictorial stimuli, which are accessible after registration.
MAIN was first published in 2012/2013 (ZASPiL 56). Several years of theory development and material construction preceded this launch. In 2019 (ZASPiL 63), the revised English version (revised on the basis of over 2,500 transcribed MAIN narratives as well as ca 24,000 responses to MAIN comprehension questions, collected from around 700 monolingual and bilingual children in Germany, Russia and Sweden between 2013-2019) was published together with revised versions in German, Russian, Swedish, and Turkish for the bilingual Turkish-Swedish population in Sweden. The present 2020 (ZASPiL 64) volume contains new and revised language versions of MAIN.
On the basis of the economic theory of network effects, this article provides a novel explanation of the so-called patent paradox, i.e. the question why the propensity to patent is so strong when the expected average value of most patents is low. It demonstrates that the patent system of a country resembles a telephone network or a social media platform. Patents are perceived as nodes in a virtual network that, as a whole, exhibits network effects. It is explained why patents are not independent of other patents but that they complement each other in several ways both within and beyond markets and fields of technology, and that patents thus create synchronization value over and above individual interests of patent holders in exclusivity. As a consequence, the more patents there are, the more valuable it is to also seek patents, and vice versa. Since patents thus display increasing returns to adoption, the willingness to pay for the next patent slopes upwards. This explains why, after a phase of early instability and a certain tipping point, many countries’ patent systems expanded quickly and eventually became a rigid standard (“lock-in”). The concluding section raises the question what regulatory measures are suitable to effectively address the ensuing anticommons effects.
The long-standing battle between economic nationalism and globalism has again taken center stage in geopolitics. This article applies this dichotomy to the law and policy of international intellectual property (IP). Most commentators see IP as a prime example of globalization. The article challenges this view on several levels. In a nutshell, it claims that economic nationalist concerns about domestic industries and economic development lie at the heart of the global IP system. To support this argument, the article summarizes and categorizes IP policies adopted by selected European countries, the European Union, and the U.S. Section I presents three types of inbound IP policies that aim to foster local economic development and innovation. Section II adds three versions of outbound IP policies that, in contrast, target foreign countries and markets. Concluding section III traces a dialectic virtuous circle of economic nationalist motives leading to global legal structures and identifies the function and legal structure of IP as the reason for the resilience and even dominance of economic nationalist motives in international IP politics. IP concerns exclusive private rights that are territorially limited creatures of (supra-)national statutes. These legal structures make up the economic nationalist DNA of IP.
Using a structural life-cycle model, we quantify the long-term impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. In the model, public investment through schooling is combined with parental time and resource investments in the production of child human capital at different stages in the children's development process. We quantitatively characterize both the long-term earnings consequences on children from a Covid-19 induced loss of schooling, as well as the associated welfare losses. Due to self-productivity in the human capital production function, skill attainment at a younger stage of the life cycle raises skill attainment at later stages, and thus younger children are hurt more by the school closures than older children. We find that parental reactions reduce the negative impact of the school closures, but do not fully offset it. The negative impact of the crisis on children's welfare is especially severe for those with parents with low educational attainment and low assets. The school closures themselves are primarily responsible for the negative impact of the Covid-19 shock on the long-run welfare of the children, with the pandemic-induced income shock to parents playing a secondary role.
Central banks unexpectedly tightening policy rates often observe the exchange value of their currency depreciate, rather than appreciate as predicted by standard models. We document this for Fed and ECB policy days using event studies and ask whether an information effect, where the public attributes the policy surprise to an unobserved state of the economy that the central bank is signaling by its policy may explain the abnormality. It turns out that many informational assumptions make a standard two- country New Keynesian model match this behavior. To identify the particular mechanism, we condition on multiple asset prices in the event study and model implications for these. We find that there is heterogeneity in this dimension in the event study and no model with a single regime can match the evidence. Further, even after conditioning on possible information effects driving longer term interest rates, there appear to be other drivers of exchange rates. Our results show that existing models have a long way to go in reconciling event study analysis with model-based mechanisms of asset pricing.
The Multilingual Assessment Instrument for Narratives (MAIN) is part of LITMUS (Language Impairment Testing in Multilingual Settings). LITMUS is a battery of tests that have been developed in connection with the COST Action IS0804 Language Impairment in a Multilingual Society: Linguistic Patterns and the Road to Assessment (2009−2013).
In these volumes, we are very pleased to present a collection of papers based on talks and posters at Sinn und Bedeutung 22, which took place in Berlin and Potsdam on September 7-10, 2017, jointly organized by the Leibniz-Centre for General Linguistics (ZAS) and the University of Potsdam.
SuB22 received 183 submitted abstracts. Out of these, the organizing committee selected 39 oral presentations in the main session, 4 oral presentations in the special session ‘Semantics and Natural Logic’, and 24 poster presentations. There were an additional 6 invited talks. In total, 58 of these contributions appear in paper form in the present volumes.
Past research suggests that international real estate markets show return characteristics and interrelationships with other asset classes, which probably qualify them as an interesting component of national and international asset allocation decisions. However, the special characteristics of real estate assets are quite distinct from that of financial assets, such as stocks and bonds. This is also the case for real estate return distributions. Therefore, the proper integration of real estate markets into asset allocation decisions requires profound understanding of real estate returns' distributional characteristics .
Because of the particular characteristics of real estate, representing real estate markets through reliable a time-series is a complex task. Consequently, reliable real estate indices with a sufficiently long history in major international real estate markets are only scarcely available. Most of the research that has been done on real estate returns was done for the U.K. and U.S., where eligible indices exist. On the other hand, in other important real estate markets, such as Germany, either little or no research has been perfoimed.
In this analysis, the methodology of Maurer, Sebastian and Stephan (2000) for indirectly deriving an appraisal-based index for the German commercial real estate market will be applied. This approach is solely based on publicly available data from German open-ended real estate investment trusts. It could also provide a solution to deriving a reliable real estate time-series for other markets.
We will extend previous analyses for the U.K. and U.S. to provide additional fundamental insights into the return characteristics of the German commercial real estate market. Despite univariate considerations, the main focus is the interrelationships between various international real estate markets, as well as between those respective markets and the international stock and bond markets.
The classical approaches to asset allocation give very different conclusions about how much foreign stocks a US investor should hold. US investors should either allocate a large portion of about 40% to foreign stocks (which is the result of mean/variance optimization and the international CAPM) or they should hold no foreign stocks at all (which is the conclusion of the domestic CAPM and mean/variance spanning tests). There is no way in between.
The idea of the Bayesian approach discussed in this article is to shrink the mean/variance efficient portfolio towards the market portfolio. The shrinkage effect is determined by the investor's prior belief in the efficiency of the market portfolio and by the degree of violation of the CAPM in the sample. Interestingly, this Bayesian approach leads to the same implications for asset allocation as the mean-variance/tracking error criterion. In both cases, the optimal portfolio is a combination of the market portfolio and the mean/variance efficient portfolio with the highest Sharpe ratio.
Applying both approaches to the subject of international diversification, we find that a substantial home bias is only justified when a US investor has a strong belief in the global mean/variance efficiency of the US market portfolio and when he has a high regret aversion of falling behind the US market portfolio. We also find that the current level of home bias can be justified whenever-regret aversion is significantly higher than risk aversion.
Finally, we compare the Bayesian approach of shrinking the mean/variance efficient portfolio towards the market portfolio to another Bayesian approach which shrinks the mean/variance efficient portfolio towards the minimum-variance portfolio. An empirical out-of-sample study shows that both Bayesian approaches lead to a clearly superior performance compared to the classical mean/variance efficient portfolio.
Predictability and the cross-section of expected returns: a challenge for asset pricing models
(2020)
Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive criteria models have to satisfy to produce expected return patterns in line with the data and discuss various examples.
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.
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.
In these volumes, we are very pleased to present a collection of papers based on talks and posters at Sinn und Bedeutung 22, which took place in Berlin and Potsdam on September 7-10, 2017, jointly organized by the Leibniz-Centre for General Linguistics (ZAS) and the University of Potsdam.
SuB22 received 183 submitted abstracts. Out of these, the organizing committee selected 39 oral presentations in the main session, 4 oral presentations in the special session ‘Semantics and Natural Logic’, and 24 poster presentations. There were an additional 6 invited talks. In total, 58 of these contributions appear in paper form in the present volumes.