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This paper analyzes how the combination of borrowing constraints and idiosyncratic risk affects the equity premium in an overlapping generations economy. I find that introducing a zero-borrowing constraint in an economy without idiosyncratic risk increases the equity premium by 70 percent, which means that the mechanism described in Constantinides, Donaldson, and Mehra (2002) is dampened because of the large number of generations and production. With social security the effect of the zero-borrowing constraint is a lot weaker. More surprisingly, when I introduce idiosyncratic labor income risk in an economy without a zero-borrowing constraint, the equity premium increases by 50 percent, even though the income shocks are independent of aggregate risk and are not permanent. The reason is that idiosyncratic risk makes the endogenous natural borrowing limits much tighter, so that they have a similar effect to an exogenously imposed zero-borrowing constraint. This intuition is confirmed when I add idiosyncratic risk in an economy with a zero-borrowing constraint: neither the equity premium nor the Sharpe ratio change, because the zero-borrowing constraint is already tighter than the natural borrowing limits that result when idiosyncratic risk is added.
This paper provides an assessment framework for privacy policies of Internet of Things Services which is based on particular GDPR requirements. The objective of the framework is to serve as supportive tool for users to take privacy-related informed decisions. For example when buying a new fitness tracker, users could compare different models in respect to privacy friendliness or more particular aspects of the framework such as if data is given to a third party. The framework consists of 16 parameters with one to four yes-or-no-questions each and allows the users to bring in their own weights for the different parameters. We assessed 110 devices which had 94 different policies. Furthermore, we did a legal assessment for the parameters to deal with the case that there is no statement at all regarding a certain parameter. The results of this comparative study show that most of the examined privacy policies of IoT devices/services are insufficient to address particular GDPR requirements and beyond. We also found a correlation between the length of the policy and the privacy transparency score, respectively.
In this study we investigate which economic ideas were prevalent in the macroprudential discourse post-crises in order to understand the availability of ideas for reform minded agents. We base our analysis on new findings in the field of ideational shifts and regulatory science, which posit that change-agents engage with new ideas pragmatically and strategically in their effort to have their economic ideas institutionalized. We argue that in these epistemic battles over new regulation, scientific backing by academia is the key resource determining the outcome. We show that the present reforms implemented internationally follow this pattern. In our analysis we contrast the entire discourse on systemic risk and macroprudential regulation with Borio’s initial 2003 proposal for a macroprudential framework. We find that mostly cross-sectional measures targeted towards increasing the resilience of the financial system rather than inter-temporal measures dampening the financial cycle have been implemented. We provide evidence for the lacking support of new macroprudential thinking within academia and argue that this is partially responsible for the lack of anti-cyclical macroprudential regulation. Most worryingly, the financial cycle is largely absent in the academic discourse and is only tacitly assumed instead of fully fledged out in technocratic discourses, pointing to the possibility that no anti-cyclical measures will be forthcoming.
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.”
Augmented reality (AR) gained much public attention since the success of Pok´emon Go in 2016. Technology companies like Apple or Google are currently focusing primarily on mobile AR (MAR) technologies, i.e. applications on mobile devices, like smartphones or tablets. Associated privacy issues have to be investigated early to foster market adoption. This is especially relevant since past research found several threats associated with the use of smartphone applications. Thus, we investigate two of the main privacy risks for MAR application users based on a sample of 19 of the most downloaded MAR applications for Android. First, we assess threats arising from bad privacy policies based on a machine-learning approach. Second, we investigate which smartphone data resources are accessed by the MAR applications. Third, we combine both approaches to evaluate whether privacy policies cover certain data accesses or not. We provide theoretical and practical implications and recommendations based on our results.
A tale of one exchange and two order books : effects of fragmentation in the absence of competition
(2018)
Exchanges nowadays routinely operate multiple, almost identically structured limit order markets for the same security. We study the effects of such fragmentation on market performance using a dynamic model where agents trade strategically across two identically-organized limit order books. We show that fragmented markets, in equilibrium, offer higher welfare to intermediaries at the expense of investors with intrinsic trading motives, and lower liquidity than consolidated markets. Consistent with our theory, we document improvements in liquidity and lower profits for liquidity providers when Euronext, in 2009, consolidated its order ow for stocks traded across two country-specific and identically-organized order books into a single order book. Our results suggest that competition in market design, not fragmentation, drives previously documented improvements in market quality when new trading venues emerge; in the absence of such competition, market fragmentation is harmful.
A new governance architecture for european financial markets? Towards a european supervision of CCPs
(2018)
Does the new European outlook on financial markets, as voiced by the EU Commission since the beginning of the Capital Market Unions imply a movement of the EU towards an alignment of market integration and direct supervision of common rules? This paper sets out to answer this question for the case of common supervision for Central Counterparties (CCPs) in the European Union. Those entities gained crucial importance post-crisis due to new regulation which requires the mandatory clearing of standardized derivative contracts, transforming clearing houses into central nodes for cross-border financial transactions. While the EU-wide regulatory framework EMIR, enacted in 2012, stipulates common regulatory requirements, the framework still relies on home-country supervision of those rules, arguably leading to regulatory as well as supervisory arbitrage. Therefore, the regulatory reform to stabilize the OTC derivatives market replicated at its center a governance flaw, which had been identified as one of the major causes for the gravity of the financial crisis in the EU: the coupling of intense competition based on private risk management systems with a national supervision of European rules. This paper traces the history of this problem awareness and inquires which factors account for the fact that only in 2017 serious negotiations at the EU level ensued that envisioned a common supervision of CCPs to fix the flawed system of governance. Analyzing this shift in the European governance architecture, we argue that Brexit has opened a window of opportunity for a centralization of supervision for CCPs. Brexit aligns the urgency of the problem with material interests of crucial political stakeholder, in particular of Germany and France, providing the possibility for a grand European bargain.
I present a new business cycle model in which decision making follows a simple mental process motivated by neuroeconomics. Decision makers first compute the value of two different options and then choose the option that offers the highest value, but with errors. The resulting model is highly tractable and intuitive. A demand function in level replaces the traditional Euler equation. As a result, even liquid consumers can have a large marginal propensity to consume. The interest rate affects consumption through the cost of borrowing and not through intertemporal substitution. I discuss the implications for stimulus policies.
We assess the relationship between finance and growth over the period 1980-2014. We estimate a cross-country growth regression for 48 countries during 20 periods of 15 years starting in 1980 (to 1995) and ending in 1999 (to 2014). We use OLS and IV estimations and we find that: 1) overall financial development had a positive effect on economic growth during all periods of our sample, i.e., we confirm that from 1980 to 2014 financial services provided by the various financial systems were significant (to various degrees) for firm creation, industrial expansion and economic growth; but that, 2) the structure of financial markets was particularly relevant for economic growth until the financial crisis; while 3) the structure of the banking sector played a major role since; and finally that, 4) the legal system is the primary determinant of the effectiveness of the overall financial system in facilitating innovation and growth in (almost) all of our sample period. Hence, overall our results suggest that the relationship between finance and growth matters but also that it varies over time in strength and in sector origination.
JEL Classification: O16, G16, G20.
"A groundbreaking decision"
(2018)