Refine
Year of publication
Document Type
- Article (19) (remove)
Has Fulltext
- yes (19)
Is part of the Bibliography
- no (19)
Keywords
- risk culture (2)
- CEO attractiveness (1)
- Childhood mortality rates (1)
- Commercial real estate (1)
- Correlated risk (1)
- Cumulating survey data (1)
- DHS surveys (1)
- DSGE (1)
- European Central Bank (1)
- Fair market valuation (1)
Institute
- House of Finance (HoF) (19) (remove)
Highlights
• Six Newton methods for solving matrix quadratic equations in linear DSGE models.
• Compared to QZ using 99 different DSGE models including Smets and Wouters (2007).
• Newton methods more accurate than QZ with comparable computation burden.
• Apt for refining solutions from alternative methods or nearby parameterizations.
Abstract
This paper presents and compares Newton-based methods from the applied mathematics literature for solving the matrix quadratic that underlies the recursive solution of linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that Newton-based methods compare favorably in solving DSGE models, providing higher accuracy as measured by the forward error of the solution at a comparable computation burden. The methods, however, suffer from their inability to guarantee convergence to a particular, e.g. unique stable, solution, but their iterative procedures lend themselves to refining solutions either from different methods or parameterizations.
Producing reliable estimates for childhood mortality rates is essential to monitor progress towards the United Nations Sustainable Development Goals (UN SDGs) and correctly evaluate policies designed to reduce childhood mortality rates. Different model-based approaches have been proposed to assess levels and trends in childhood mortality indicators. In this paper, we propose a design-based complement that accumulates birth histories across different household surveys to increase the precision of childhood mortality rates estimates. We accumulate birth histories across different cross-sectional Demographic Health Surveys/Multiple Cluster Indicator Surveys collected in Senegal and Malawi and estimate pooled childhood mortality rates based on calendar years. We show that accumulating birth histories smoothens fluctuations in time series for national and sub-national mortality rates, establishes more stable and reliable time trends, and results in estimated standard errors of the cumulated rates that are about 50–60% lower than their counterparts from separate surveys.
The CEO beauty premium: Founder CEO attractiveness and firm valuation in initial coin offerings
(2021)
Research summary
We apply insights from research in social psychology and labor economics to the domain of entrepreneurial finance and investigate how founder chief executive officers' (founder CEOs') facial attractiveness influences firm valuation. Leveraging the novel context of initial coin offerings (ICOs), we document a pronounced founder CEO beauty premium, with a positive relationship between founder CEO attractiveness and firm valuation. We find only very limited evidence of stereotype-based evaluations, through the association of founder CEO attractiveness with latent traits such as competence, intelligence, likeability, or trustworthiness. Rather, attractiveness seems to bear economic value per se, especially in a context in which investors base their decisions on a limited information set. Indeed, attractiveness has a sustainable effect on post-ICO performance.
Managerial summary
ICOs allow ventures to collect funding from investors using blockchain technology. We leverage this novel funding context, in which information on the ventures and their future prospects is scarce, to empirically investigate whether the founder CEOs' physical attractiveness is associated with increased funding (i.e., amount raised) and post-funding performance (i.e., buy-and-hold returns). We find that ventures with more attractive founder CEOs outperform ventures with less attractive CEOs in both dimensions. For ICO investors, this suggests that ICOs of firms with more attractive founder CEOs are more appealing investment targets. Our findings are also interesting for startups seeking external finance in uncertain contexts, such as ICOs. If startups can appoint attractive leaders, they may have better access to growth capital.
We empirically examine how systemic risk in the banking sector leads to correlated risk in office markets of global financial centers. In so doing, we compute an aggregated measure of systemic risk in financial centers as the cumulated expected capital shortfall of local financial institutions. Our identification strategy is based on a double counterfactual approach by comparing normal with financial distress periods as well as office with retail markets. We find that office market interconnectedness arises from systemic risk during financial turmoil periods. Office market performance in a financial center is affected by returns of systemically linked financial center office markets only during a systemic banking crisis. In contrast, there is no evidence of correlated risk during normal times and among the within-city counterfactual retail sector. The decline in office market returns during a banking crisis is larger in financial centers compared to non-financial centers.
Combining market data with a publicly available monthly snapshot of Deutsche Börse’s index ranking list, I create a model that predicts index changes in the DAX, MDAX, SDAX, and TecDAX from 2010 to 2019 before they are officially announced. Even though I empirically show that index changes are predictable, they still earn sizeable post-announcement 1-day abnormal returns up to 1.42% and − 1.54% for promotions and demotions, respectively. While abnormal returns are larger in smaller stocks, I find no evidence that they are related to funding constraints or additional risk for trading on wrong predictions. A trading strategy that trades according to my model yields an annualized Sharpe ratio of 0.83 while being invested for just 4 days a year.
This paper analyses disclosure duties in insurance contract law in Germany on the basis of questions developed in preparation of the World Congress of the International Insurance Law Association (AIDA) 2018. As risk factors are within the policyholder’s sphere of knowledge, the insurer naturally depends on gaining such knowledge from its policyholder in order to calculate and evaluate premium and risk. Legal approaches as to how the insurer may obtain relevant information and the legal consequences differ in national insurance contract laws around the globe. Taking part in this legal comparison, the paper describes the key elements of such a mechanism from a German perspective and comprises both duties of the policyholder and duties of the insurer.
As for the policyholder, these issues are differences between a duty to (spontaneously) disclose and a duty not to misrepresent as a reaction to questions of the insurer, the prerequisites and remedies of such duty, the subjective standard of the disclosure duty and a duty to notify material changes during the contract term. On the other hand, the paper also addresses an insurer’s duty to investigate, a duty to ascertain the policyholder’s understanding of the policy and a duty to inform during the contract term or after the occurrence of an insured event. In doing so, the paper offers a comprehensive and critical overview on the transfer of knowledge in the insurance (pre-)contractual relationship.
This article investigates the roles of psychological biases for deviations between subjective survival beliefs (SSBs) and objective survival probabilities. We model these deviations through age-dependent inverse S-shaped probability weighting functions. Our estimates suggest that implied measures for cognitive weakness increase and relative optimism decrease with age. Direct measures of cognitive weakness and optimism share these trends. Our regression analyses confirm that these factors play strong quantitative roles in the formation of SSBs. Our main finding is that cognitive weakness instead of optimism becomes with age an increasingly important contributor to the well-documented overestimation of survival chances in old age.
The current discussion about a “risk culture” in financial services was triggered by the recent series of financial crises. The last decade saw a long list of hubris, misconduct and criminal activities by human beings on a single or even a collective basis in banks, in the industry or in the whole economy. As a counter-reaction, financial authorities called for a guidance by a “new” risk culture in financial institutions based on a set of abstract, formal, and normative governance processes. While traditional risk research in economics and in banking was focused on the statistical aspects of risk as the probability of loss multiplied by the amount of loss, culture is a paraphrase for the behavior in collectives and dynamics of organization found in human societies. Therefore, a “risk culture” should link the normative concepts of risk with the positive “real-world” decision-making in financial services. This paper will describe a novel view on “risk culture” from the perspective of human beings interacting in dynamical and intertemporal commercial relations. In this context “risk” is perceived by economic agents ex−ante as the consequence of the time lag between the present and the uncertain future development (compared to a probability distribution calculated by observers ex−post). For all those individual decisions—to be made under uncertainty—future “risk” includes the so-called “normal accidents”, i.e., failures that will happen at some uncertain point in time but are inevitable, and the only questions are when failure will happen and how to maintain function in the first line of defense. Finally, the shift from an abstract definition of “risk” as a probability distribution to a role model of “honorable merchants” as a benchmark for significant individual decision-making with individual responsibilities for the uncertain future outcome provides a new framework to discuss the responsibilities in the financial industry.
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the “unknown unknown.” While machine learning has been tested successfully in the regime of the “known,” heuristics typically provide better results for an active operational risk management (in the sense of forecasting). However, precursors in existing data can open a chance for machine learning to provide early warnings even for the regime of the “unknown unknown.”