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Interest rates underpin almost every instrument/transaction in conventional financial markets. Valuation of the instruments in relation to interest rates remains meaningful only if cash can be attributed a worth of its own (which is generally assumed to accumulate over time). The relevant concepts such as the stochastic short rate and the conventional numéraire (i.e. the money market account) not only become restrictive when one attempts to build more realistic models in quantitative finance, but also – as we demonstrate in this work – their application has de-stabilizing effects on asset valuations. This paper presents a detailed critique of the conventional numéraire and proposes an asset-backed stable numéraire for sustainable valuation of assets and/or transactions. In particular, we demonstrate how some of the key benchmarks of a sustainable footprint may be used as a numéraire currency. We also unveil the implicit assumption underlying the common practice of straightforward factorization in conventional relative pricing to be false. [ABSTRACT FROM AUTHOR]

A new synthetic route to monoclinic bismuth vanadate using a concept adapted from a nonaqueous synthesis strategy for nanostructured organic-inorganic hybrid materials and metal oxides, introduced recently as twin polymerization, is reported. Microwave-assisted synthesis starting from bismuth(III) tert-butoxide and vanadium(V) oxytri( tert-butoxide) in the presence of 2-methoxybenzyl alcohol, 2,4-dimethoxybenzyl alcohol, and 2-(2-thienyl) iso-propyl alcohol gave hybrid materials, which were characterized by solid-state 13C NMR spectroscopy, CH analysis, atomic absorption spectroscopy, and energy dispersive X-ray spectroscopy. Subsequent pyrolysis resulted in nanoscale monoclinic bismuth vanadate that showed a high photocatalytic activity. The photocatalytic decomposition of model pollutants such as rhodamine B (RhB), methyl orange, methylene blue, and orange G under visible-light irradiation using the as-prepared bismuth vanadate samples was studied with a focus on the influence of the agglomeration behavior. The interplay between the photooxidation of RhB, induced by the excitation of the catalyst using visible light, and the photosensitized decomposition pathway is demonstrated by degradation experiments in solution and the solid state. [ABSTRACT FROM AUTHOR]

Energy risk management requires an efficient prediction of CO2 future prices. We apply a neural network calibrated stochastic model to predict day ahead future prices suitable for risk management with a given time horizon (e.g. 10 business days). The model is training neural perceptron layers to learn the weights of historical time series points within a past horizon equal to the the memory depth (e.g. previous 10 business days). With this weights, the parameters of the stochastic model are optimized, and the day ahead carbon future price is computed. Iterating these steps, we obtain price forecasts, which demonstrate the learning efficiency of our method, which essentially corresponds to a stochastic process with time-dependent parameters, the dynamics of the parameters being themselves learned continuously by the neural network. The back propagation in training the previous weights is limited by the memory depth. The latter is the analogue of the maximal time lag of an autoregressive processes. [ABSTRACT FROM AUTHOR]

If a given dynamical process contains an inherently unpredictable component, it may be modeled as a stochastic process. Typical examples from financial markets are the dynamics of prices (e.g. prices of stocks or commodities) or fundamental rates (exchange rates etc.). The unknown future value of the corresponding stochastic process is usually estimated as the expected value under a suitable measure, which may be determined from distribution of past (historical) values. The predictive power of this estimation is limited by the simplifying assumptions of common calibration methods. Here we propose a novel method of 'intelligent' calibration, using learning (2-layer) neural networks in order to dynamically adapt the parameters of a stochastic model to the most recent time series of fixed length (memory depth) to the past. The process parameters are determined by the weights of the intermediate layer of the neural network. The final layer combines these parameters in a meaningful manner yielding the forecasting value for the stochastic process. On each actual finite memory, the neural network is trained by back-propagation, obtaining a much more flexible and realistic parameter calibration than an analogous fit to an autoregressive models could do. In the context of processes related to financial assets, the final combination of the output layer relates to their market-price-of-risk. The back propagation is limited to the typical memory length of the financial market (for example 10 previous business days). We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts with one typical examples each, for the asset classes of currencies and stocks. [ABSTRACT FROM AUTHOR]

For the pricing of interest rate derivatives various stochastic interest rate models are used. The shape of such a model can take very different forms, such as direct modelling of the probability distribution (e.g. a generalized beta function of second kind), a short-rate model (e.g. a Hull-White model) or a forward rate model (e.g. a LIBOR market model). This article describes the general structure of optimization in the context of interest rate derivatives. Optimization in finance finds its particular application within the context of calibration problems. In this case, calibration of the (vector-valued) state of a given stochastic model to some target state, which is determined by available relevant market data, implies a continuous optimization of the model parameters such that a global minimum of the distance between the target state and the model state is achieved. In this article, a novel numerical algorithm for the optimization of parameters of stochastic interest rate models is presented. The optimization algorithm operates within the model parameter space on an adaptive lattice with a number of lattice points per dimension which is both low and fixed. In this way, a considerable performance gain is achieved, as compared to algorithms working with non-adaptive lattices requiring increasing and/or large numbers of lattice points. As compared to standard algorithms, e.g. those of the Levenberg-Marquardt type, the presented adaptive lattice algorithm also reduces the danger of getting trapped near a wrong local minimum. As a numerical example, its application is demonstrated by optimizing volatility and mean reversion parameters of the Hull-White model, such that the latter becomes calibrated to the swaption volatility market relevant for a given OTC (over-the-counter, i.e. not exchange traded) bond option. [ABSTRACT FROM AUTHOR]

Abstract. The gastropods Cratena peregrina and Flabellina affinis (Nudibranchia, Aeolidacea) feed on Eudendrium racemosum (Hydrozoa). The question is addressed how the snails are protected against discharging nematocysts. When nematocysts of the hydrozoan discharged against the skin of the gastropods, the impact caused the epidermis to fold, and packets of epidermal vesicles were sloughed off. As vesicles opened, characteristic filamentous spindle-like structures were released. The discharging small microbasic eurytele nematocysts of the hydroid tentacles were blocked at the surface of the skin by mucus and/or by a layer of microvilli. Many secreted spindles from the epidermis of the snails were found among the everted nematocyst components. Discharging holotrichous isorhiza nematocysts of the hydroid cnidophores, however, caused complete lysis of epidermal cells of the gastropods, presumably due to the action of the hydrozoan toxins, which resulted in release of masses of spindles. Spindles then coated the nematocyst threads. It is proposed that the spindles in the epidermal cells of the snails serve a protective role, which ensures detachment of the hydroid tentacles and subsequent regeneration of the injured region of epidermis. [ABSTRACT FROM AUTHOR]

In this study we have determined gap sequences from nine different spirochetes. Phylogenetic analyses of these sequences in the context of all other available eubacterial and a selection of eukaryotic Gap sequences demonstrated that the eubacterial glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene diversity encompasses at least five highly distinct gene families. Within these gene families, spirochetes show an extreme degree of sequence divergence that is probably the result of several lateral gene transfer events between spirochetes and other eubacterial phyla, and early gene duplications in the eubacterial ancestor. A Gap1 sequence from the syphilis spirochete Treponema pallidum has recently been shown to be closely related to GapC sequences from Euglenozoa. Here we demonstrate that several other spirochetal species are part of this cluster, supporting the conclusion that an interkingdom gene transfer from spirochetes to Euglenozoa must have occurred. Furthermore, we provide evidence that the GAPDH genes present in the protists Parabasalia may also be of spirochetal descent. [ABSTRACT FROM PUBLISHER]

Inhomogeneous multidimensional cosmological models with a higher-dimensional space-time manifold $$M = \overline {M_0 }$$ 0 i=1 n Mi ( n ≥ 1) are in stigated under dimensional reduction to a D0-dimensional effective non-minimally coupled σ-model which generalizes the familiar Brans–Dicke model. The general form of the Einstein frame representation of multidimensional solutions known in the Brans–Dicke frame is given with respect to cosmic synchronous time. As an example, the transformation is demonstrated explicitly for the generalized Kasner solutions where it is shown that solutions in the Einstein frame show no inflation of the external space although they can undergo deflation after the cosmic synchronous time inversion. [ABSTRACT FROM AUTHOR]

Studies the properties of the effective sigma-model for D-dimensional Einstein gravity based on multidimensional geometries. Extended target space from coupled scalars and (p+2)-forms; Orthobrane conditions required for exact solutions; Static spherically symmetric solutions with a particular configuration of intersecting electric and magnetic branes; Role of the Einstein frame for four-geometries.

A finite subgroup of the conformal group SL(2,C) can be related to invariant polynomials on a hypersurface in C3. The latter then carries a simple singularity, which resolves by a finite iteration of basic cycles of deprojections. The homological intersection graph of these cycles is the Dynkin graph of an ADE Lie group, i.e., a Lie group from the cartan series A, D, or E. The deformation of the simple singularity corresponds to ADE symmetry breaking. A (3+1)-dimensional topological model of observation is constructed, transforming consistently under SL(2,C), as an evolving three-dimensional system of world tubes, which connect ‘‘possible points of observation.’’ The existence of an initial singularity for the four-dimensional space–time is related to its global topological structure. Associating the geometry of ADE singularities to the vertex structure of the topological model puts forward the conjecture on a likewise relation of inner symmetries of elementary particles to local space–time structure. [ABSTRACT FROM AUTHOR]