In this paper we present a copula-based model for a binary and a continuous variable in a time series setup. Within this modeling framework both marginals can be equipped with their own dynamics whereas the contemporaneous dependence between both processes can be flexibly captured via a copula function. We propose a method for testing the goodness-of-fit of such a time series model using probability integral transforms (PIT). This verification procedure allows not only a verification of the goodness-of-fit of the estimated marginal distribution for a continuous variable but also the conditional distribution of a continuous variable given the outcome of its binary counterpart (i.e. the adequacy of the copula choice). We test the model on an empirical example: investigating the relationship between trading volume and the indicators of arbitrarily ’large’ price movements on the interbank EUR/PLN spot market.

We discuss the empirical importance of long term cyclical effects in the volatility of financial returns. Following Amado and Teräsvirta (2009), ČiŽek and Spokoiny (2009) and others, we consider a general conditionally heteroscedastic process with stationarity property distorted by a deterministic function that governs the possible time variability of the unconditional variance. The function proposed in this paper can be interpreted as a finite Fourier approximation of an Almost Periodic (AP) function as defined by Corduneanu (1989). The resulting model has a particular form of a GARCH process with time varying parameters, intensively discussed in the recent literature.

In the empirical analyses we apply a generalisation of the Bayesian AR(1)-GARCH model for daily returns of S&P500, covering the period of sixty years of US postwar economy, including the recently observed global financial crisis. The results of a formal Bayesian model comparison clearly indicate the existence of significant long term cyclical patterns in volatility with a strongly supported periodic component corresponding to a 14 year cycle. Our main results are invariant with respect to the changes of the conditional distribution from Normal to Student-tand to the changes of the volatility equation from regular GARCH to the Asymmetric GARCH.

In this paper, we use weekly stock market data to examine whether the volatility of stock returns of ten emerging capital markets of the new EU member countries has changed since the opening of their capital markets. In particular we are interested in understanding whether there are high and low periods of stock returns volatility and what the degree of correlation across these markets is. We estimate a Markov-Switching ARCH (SWARCH) model proposed by Hamilton and Susmel (1994) and we allow for the possibility that two or three volatility regimes may exist for stock returns volatility. The main finding of the present study is that the high volatility of stock returns of all new EU emerging stock markets is associated mainly with the 1997‒1998 Asian and Russian financial crises as well as over the 2007‒2009 financial turmoil, while there is a transition to the low volatility regime as they approach the accession to the EU in 2004. It is also shown that the capital flows liberalization process has resulted in an increase in volatility of stock returns in most cases.

The paper deals with application of the Gumbel model to evaluation of the environmental loads. According to recommendations of Eurocodes, the conventional method of determining return period and characteristic values of loads utilizes the theory of extremes and implicitly assumes that the cumulative distribution function of the annual or other basic period extremes is the Gumbel distribution. However, the extreme value theory shows that the distribution of extremes asymptotically approaches the Gumbel distribution when the number of independent observations in each observation period from which the maximum is abstracted increases to infinity. Results of calculations based on simulation show that in practice the rate of convergence is very slow and significantly depends on the type of parent results distribution, values of coefficient of variation, and number of observation periods. In this connection, a straightforward purely empirical method based on fitting a curve to the observed extremes is suggested.

Several previous investigations on failure of a certain type lattice girders railway bridge (on so called BJD line) have not convincingly explained reasons nor have they described potential hazards. This paper attempts to provide an answer, employing static, dynamic, and fatigue analysis of the structure, focusing on previously not analyzed vibrations of elements constituting a lattice node. Detailed models of two types of such nodes – damaged and non- damaged were compared, inside carefully defined limits of applicability.

People living in buildings may be exposed to dynamic actions. In the diagnosis and design of buildings there is an increasing need of taking into account these activities and verification of compliance of the building requirements for vibration comfort of people residing in buildings. This study presents the results of analysis of such criteria in the following standards: Polish PN-88/B-02171 [1], British BS 6472-1 [2], German DIN 4150 [3], and ISO international standards [4,5]. Basing on the results of this analysis and on the review of selected items of literature, the application of standards recommendations in diagnosis and design of buildings, as well as areas for further research on this subject is indicated. This article is an extended version of the conference paper [6] presented on the conference Urban Transport 2011.

For solving a partial different equation by a numerical method, a possible alternative may be either to use a mesh method or a meshless method. A flexible computational procedure for solving 1D linear elastic beam problems is presented that currently uses two forms of approximation function (moving least squares and kernel approximation functions) and two types of formulations, namely the weak form and collocation technique, respectively, to reproduce Element Free Galerkin (EFG) and Smooth Particle Hydrodynamics (SPH) meshless methods. The numerical implementation for beam problems of these two formulations is discussed and numerical tests are presented to illustrate the difference between the formulations.

The technology of recycling with foamed bitumen is a new technology of road rehabilitation. Due to the climatic conditions in the Central European countries, road pavement structure should be moisture and frost resistant. Because of its specific production conditions, this is especially important for pavements rehabilitation with the cold recycling technology. Determining the physical and mechanical properties, as well as moisture and frost resistance, depends on binder and filler contents. They are the key elements before its use for road building. The tests presented here have been performed on mineral recycled base mixes with foamed bitumen. The material from the existing layers was used. The content of bitumen binder amounted to 2.0%, 2.5%, 3.0% and 3.5%, while that of cement to 1.0%, 1.5%, 2.0%, 2.5%. The results were subject to the optimization process. This allowed to state that with the use of 2.5% foamed bitumen and 2.0% of cement, the base had the required properties, as well as the moisture and frost resistance.

A method of detecting honeycombing damage in a reinforced concrete beam using the finite element model updating technique was proposed. A control beam and two finite element model srepresenting different severity of damage were constructed using available software and the defect parameters were updated. Analyses were performed on the finite element models to approximate the modal parameters. A datum and a control finite element model to match the datum test beams with honeycombs were prepared. Results from the finite element model were corrected by updating the Young’s modulus and the damage parameters. There was a loss of stiffness of 3% for one case, and a loss of 7% for another. The more severe the damage, the higher the loss of stiffness. There was no significant loss of stiffness by doubling the volume of the honeycombs.

Creep compliance of the hot-mix asphalt (HMA) is a primary input of the current pavement thermal cracking prediction model used in the US. This paper discusses a process of training an Artificial Neural Network (ANN) to correlate the creep compliance values obtained from the Indirect Tension (IDT) with similar values obtained on small HMA beams from the Bending Beam Rheometer (BBR). In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance functions built on the ANN-predicted discrete values also exhibited a good correlation when compared with the laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the measured values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.