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Friedrich-Wilhelm-Lübke-Koog, Germany

Gunduz G.,Ankara University | Gunduz Y.,Deutsche Bundesbank
Physica A: Statistical Mechanics and its Applications | Year: 2010

The scattering diagram of a stock index results in a complex network structure, which can be used to analyze the viscoelastic properties of the index. The change along x- or y-direction of the diagram corresponds to purely elastic (or spring like) movement whereas the diagonal change at an angle of 45° corresponds to purely viscous (or dashpot like) movement. The viscous component pushes the price from its current value to any other value, while the elastic component acts like a restoring force. Four indices, namely, DJI, S&P-500, NASDAQ-100, and NASDAQ-composite were studied for the period of 20012009. NASDAQ-composite displayed very high elasticity while NASDAQ-100 displayed the highest fluidity in the time period considered. The fluidity of DJI and S&P-500 came out to be close to each other, and they are almost the same in the second half of the period. © 2010 Elsevier B.V. All rights reserved. Source


Vilsmeier J.,Deutsche Bundesbank
Journal of Computational Finance | Year: 2016

In this paper, we update the option implied probability of default (iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk-neutral density is derived, whose integrals can be solved analytically. Second, it is reasoned that the originally proposed approach for the estimation of the PoD produces arbitrary results; hence, an alternative procedure - based on the Lagrange multipliers - is suggested. Based on numerical evaluations and an illustrative empirical application, we conclude that the framework provides very promising results. © 2016 Incisive Risk Information (IP) Limited. Source


Breitung J.,University of Cologne | Roling C.,Deutsche Bundesbank
Journal of Forecasting | Year: 2015

In this paper a nonparametric approach for estimating mixed-frequency forecast equations is proposed. In contrast to the popular MIDAS approach that employs an (exponential) Almon or Beta lag distribution, we adopt a penalized least-squares estimator that imposes some degree of smoothness to the lag distribution. This estimator is related to nonparametric estimation procedures based on cubic splines and resembles the popular Hodrick-Prescott filtering technique for estimating a smooth trend function. Monte Carlo experiments suggest that the nonparametric estimator may provide more reliable and flexible approximations to the actual lag distribution than the conventional parametric MIDAS approach based on exponential lag polynomials. Parametric and nonparametric methods are applied to assess the predictive power of various daily indicators for forecasting monthly inflation rates. It turns out that the commodity price index is a useful predictor for inflations rates 20-30 days ahead with a hump-shaped lag distribution. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd. Source


Bremus F.,German Institute for Economic Research | Buch C.M.,Deutsche Bundesbank
Pacific Economic Review | Year: 2015

Does the structure of banking markets affect macroeconomic volatility and, if yes, is this link different in low-income countries? In this paper, we explore the channels through which the structure of banking markets affects macroeconomic volatility. Our research has three main findings. First, we study whether idiosyncratic volatility at the bank level can impact aggregate volatility. We find weak evidence for a link between granular banking sector volatility and macroeconomic fluctuations. Second, a higher share of domestic credit to GDP coincides with higher volatility in the short run. Third, a higher level of cross-border asset holdings increases volatility in low-income countries. © 2015 Wiley Publishing Asia Pty Ltd. Source


Daude C.,Oecd Nuclear Energy Agency | Nagengast A.,Deutsche Bundesbank | Perea J.R.,Oecd Nuclear Energy Agency
Journal of International Trade and Economic Development | Year: 2016

Recent contributions to the growth and trade literature have argued that the structure of an economy, as measured by its productive capabilities, is a key determinant for inter-country differences in development. Productive capabilities have been shown to be highly predictive of future economic growth, yet the country-level variables associated with them remain relatively unknown. In this paper, we empirically explore what variables are systematically associated with productive capabilities using a model averaging framework that can handle a very large number of potential explanatory variables without the need for arbitrary model selection. In order to estimate our dynamic panel specification, we propose a novel Bayesian averaging of classical estimates procedure based on the simple and efficient bias-corrected least squares dummy variable estimator. Our baseline and robustness analysis consider a large number of variables, sample periods and model priors. We find that there is persistence (as measured by the lagged dependent variable) and that variables, such as commodity terms of trade, energy availability, government consumption, capital per worker, arable land and capital inflows show a strong and robust association with capabilities. © 2015 Organisation for Economic Co-operation and Development (OECD). Source

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