Central Bank Communication and Exchange Rate Volatility: A GARCH Analysis

We examine the effects of the Czech National Bank communication, macroeconomic news and interest rate differential on exchange rate volatility using generalized autoregressive conditional heteroscedasticity model. Our results suggest that central bank communication has a calming effect on exchange rate volatility. The timing of central bank communication seems to matter, too, as financial markets respond more to the communication before the policy meetings than after them. Next, macroeconomic news releases are found to reduce exchange rate volatility, while interest rate differential seems to increase it.http://deepblue.lib.umich.edu/bitstream/2027.42/64364/1/wp962.pd

Central Bank Communication and Exchange Rate Volatility: A GARCH Analysis

We examine the effects of the Czech National Bank communication, macroeconomic news and interest rate differential on exchange rate volatility using generalized autoregressive conditional heteroscedasticity model. Our results suggest that central bank communication has a calming effect on exchange rate volatility. The timing of central bank communication seems to matter, too, as financial markets respond more to the communication before the policy meetings than after them. Next, macroeconomic news releases are found to reduce exchange rate volatility, while interest rate differential seems to increase it.central bank communication, exchange rate, GARCH

The Army of One (Sample): the Characteristics of Sampling-based Probabilistic Neural Representations

There is growing evidence that humans and animals represent the uncertainty associated with sensory stimuli and utilize this uncertainty during planning and decision making in a statistically optimal way. Recently, a nonparametric framework for representing probabilistic information has been proposed whereby neural activity encodes samples from the distribution over external variables. Although such sample-based probabilistic representations have strong empirical and theoretical support, two major issues need to be clarified before they can be considered as viable candidate theories of cortical computation. First, in a fluctuating natural environment, can neural dynamics provide sufficient samples to accurately estimate a stimulus? Second, can such a code support accurate learning over biologically plausible time-scales? Although it is well known that sampling is statistically optimal if the number of samples is unlimited, biological constraints mean that estimation and learning in the cortex must be supported by a relatively small number of possibly dependent samples. We explored these issues in a cue combination task by comparing a neural circuit that employed a sampling-based representation to an optimal estimator. For static stimuli, we found that a single sample is sufficient to obtain an estimator with less than twice the optimal variance, and that performance improves with the inverse square root of the number of samples. For dynamic stimuli, with linear-Gaussian evolution, we found that the efficiency of the estimation improves significantly as temporal information stabilizes the estimate, and because sampling does not require a burn-in phase. Finally, we found that using a single sample, the dynamic model can accurately learn the parameters of the input neural populations up to a general scaling factor, which disappears for modest sample size. These results suggest that sample-based representations can support estimation and learning using a relatively small number of samples and are therefore highly feasible alternatives for performing probabilistic cortical computations.
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Learning complex tasks with probabilistic population codes

Recent psychophysical experiments imply that the brain employs a neural representation of the uncertainty in sensory stimuli and that probabilistic computations are supported by the cortex. Several candidate neural codes for uncertainty have been posited including Probabilistic Population Codes (PPCs). PPCs support various versions of probabilistic inference and marginalisation in a neurally plausible manner. However, in order to establish whether PPCs can be of general use, three important limitations must be addressed. First, it is critical that PPCs support learning. For example, during cue combination, subjects are able to learn the uncertainties associated with the sensory cues as well as the prior distribution over the stimulus. However, previous modelling work with PPCs requires these parameters to be carefully set by hand. Second, PPCs must be able to support inference in non-linear models. Previous work has focused on linear models and it is not clear whether non-linear models can be implemented in a neurally plausible manner. Third, PPCs must be shown to scale to high-dimensional problems with many variables. This contribution addresses these three limitations of PPCs by establishing a connection with variational Expectation Maximisation (vEM). In particular, we show that the usual PPC update for cue combination can be interpreted as the E-Step of a vEM algorithm. The corresponding M-Step then automatically provides a method for learning the parameters of the model by adapting the connection strengths in the PPC network in an unsupervised manner. Using a version of sparse coding as an example, we show that the vEM interpretation of PPC can be extended to non-linear and multi-dimensional models and we show how the approach scales with the dimensionality of the problem. Our results provide a rigorous assessment of the ability of PPCs to capture the probabilistic computations performed in the cortex.
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Two-Dimensional Forward Scattering – Comparisons of Approximate and Exact Solutions

Various methods for analyses of scattering are mentioned and new approximate relationships are derived. Experimental results for thin wire and several numerical simulations of forward scattering using approximate estimations, physical optics and exact solutions for two-dimensional scattering are presented both for far and near fields. That allows not only accuracy analyses but also conclusions about scattering and total fields in the presence of objects, which are important for many applications such as communications, bistatic and multistatic radars and electromagnetic compatibility

Site Diversity Gain Estimated from Rain Rate Records

The site diversity is used to mitigate the rain attenuation on satellite links. The attenuation is estimated through the rain rate-rain attenuation conversion based on the Assis-Einloft physical model in this study. Through the comparison of instantaneous attenuations at two receiving sites the site diversity gain is estimated. Examples of rain rate measurements in the Czech Republic followed by the site diversity gain estimation are added. This gain is greater on west-east situation of receiving sites achieving 10 dB on 0.01% exceedance level/100 km