A new approach to the evaluation and selection of leading indicators

This note examines how the DEM/USD rate and US short-term and long-term interest rates respond to the release of payroll announcements. In contrast to a recent paper by Edison (1997), who employs a linear econometric model, we test the influence of news by comparing the absolute values of the percentage change between the means of symmetrically sampled values of daily exchange rate and interest rates before and after the announcement day to the distribution of absolute changes in means for all periods excluding non-farm payroll news. We find a highly significant reaction for both the DEM/USD rate and bond yields, depending on the window size. Short-term US interest rates, by contrast are hardly affected. Finally, the reaction of inflation indexed bond yields to news announcements is investigated.. --leading indicator,turning point,prediction

Stability issues in German money multiplier forecasts

This paper investigates the stability of the German money supply focusing on the period 1991 - 1998. It is shown that the standard ARIMA-Transfer model approach in the literature needs to be augmented by a cointegration term to adequately model the dynamics of money supply in Germany. Additional analysis with regard to the influence of financial innovations on the control of money supply yields evidence that the influence of financial innovations on the multiplier has increased steadily during the observation period. --Money Supply,Financial Innovation,Forecasting Money Multiplier

Approximation properties of the neuro-fuzzy minimum function

The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the minimum fuzzy implication rule as well as a differentiable substitute function that allows fast optimization and function approximation with neuro-fuzzy networks. --Fuzzy Logic,Neural Networks,Nonlinear Modeling,Optimization

Closed form integration of artificial neural networks with some applications

Many economic and econometric applications require the integration of functions lacking a closed form antiderivative, which is therefore a task that can only be solved by numerical methods. We propose a new family of probability densities that can be used as substitutes and have the property of closed form integrability. This is especially advantageous in cases where either the complexity of a problem makes numerical function evaluations very costly, or fast information extraction is required for time-varying environments. Our approach allows generally for nonparametric maximum likelihood density estimation and may thus find a variety of applications, two of which are illustrated briefly: Estimation of Value at Risk based on approximations to the density of stock returns; Recovering risk neutral densities for the valuation of options from the option price - strike price relation. --Option Pricing,Neural Networks,Nonparametric Density Estimation

Approximation properties of the neuro-fuzzy minimum function

The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the minimum fuzzy implication rule as well as a differentiable substitute function that allows fast optimization and function approximation with neuro-fuzzy networks

Stability issues in German money multiplier forecasts

This paper investigates the stability of the German money supply focusing on the period 1991 - 1998. It is shown that the standard ARIMA-Transfer model approach in the literature needs to be augmented by a cointegration term to adequately model the dynamics of money supply in Germany. Additional analysis with regard to the influence of financial innovations on the control of money supply yields evidence that the influence of financial innovations on the multiplier has increased steadily during the observation period

Mixtures of t-distributions for Finance and Forecasting

We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the cumulative density function). We illustrate the usefulness of these distributions in two applications. In the first application, we use a scaled and shifted t-distribution to produce density forecasts of U.S. inflation and show that these forecasts are more accurate, out-of-sample, than density forecasts obtained using normal or standard t-distributions. In the second application, we replicate the option-pricing exercise of Abadir and Rockinger (2003) using a mixture of scaled and shifted t-distributions and obtain comparably good results, while gaining analytical tractability.ARMA-GARCH models, neural networks, nonparametric density estimation, forecast accuracy, option pricing, risk neutral density

Closed form integration of artificial neural networks with some applications

Many economic and econometric applications require the integration of functions lacking a closed form antiderivative, which is therefore a task that can only be solved by numerical methods. We propose a new family of probability densities that can be used as substitutes and have the property of closed form integrability. This is especially advantageous in cases where either the complexity of a problem makes numerical function evaluations very costly, or fast information extraction is required for time-varying environments. Our approach allows generally for nonparametric maximum likelihood density estimation and may thus find a variety of applications, two of which are illustrated briefly: Estimation of Value at Risk based on approximations to the density of stock returns; Recovering risk neutral densities for the valuation of options from the option price - strike price relation

Hypernormal Densities

We propose a new family of density function that posses both flexibility and closed form expressions for moments and anti-derivatives, making them particularly appealing for applications. We illustrate its usefulness by applying our new family to obtain density forecasts of U.S. inflation. Our methods generate forecasts that improve on standard methods based on AR-ARCH models relying on normal or Student's t-distributional assumptions

The second most abundant dinophyte in the ponds of a botanical garden is a species new to science

In the microscopy realm, a large body of dark biodiversity still awaits to be uncovered. Unarmoured dinophytes are particularly neglected here, as they only present inconspicuous traits. In a remote German locality, we collected cells, from which a monoclonal strain was established, to study morphology using light and electron microscopy and to gain DNA sequences from the rRNA operon. In parallel, we detected unicellular eukaryotes in ponds of the Botanical Garden Munich-Nymphenburg by DNA-metabarcoding (V4 region of the 18S rRNA gene), weekly sampled over the course of a year. Strain GeoK*077 turned out to be a new species of Borghiella with a distinct position in molecular phylogenetics and characteristic coccoid cells of ovoid shape as the most important diagnostic trait. Borghiella ovum, sp. nov., was also present in artificial ponds of the Botanical Garden and was the second most abundant dinophyte detected in the samples. More specifically, Borghiella ovum, sp. nov., shows a clear seasonality, with high frequency during winter months and complete absence during summer months. The study underlines the necessity to assess the biodiversity, particularly of the microscopy realm more ambitiously, if even common species such as formerly Borghiella ovum are yet unknown to science