C49 - Econometric and Statistical Methods: Special Topics: OtherReturn

We provide an extensive study assessing whether a prior wavelet-based denoising step enhances the forecast accuracy of standard forecasting models. Many combinations of attribute values of the thresholding (denoising) algorithm are explored together with several traditional forecasting models used in economic time series forecasting. The results are evaluated using M3 competition yearly time series. We conclude that the performance of a forecasting model combined with the prior denoising step is generally not recommended, which implies that a straightforward generalisation of some of the results available in the literature (which found the denoising step to be beneficial) is not possible. Even if cross-validation is used to select the value of the threshold, a superior performance of the forecasting model with the prior denoising step does not generally follow.

An interesting approach to economic and financial time series forecasting consists of decomposing an input time series additively into several components, each component capturing the dynamics of a different frequency range. Consequently, each component is modelled and forecasted separately, the predictions being summed up to form an overall forecast of the input time series. The present paper considers one very important aspect of the forecasting procedure. More specifically, it provides a better understanding of how an additive decomposition of the input time series into several components can be obtained using the wavelet transform and how boundary conditions in the individual components should be properly treated. Even though these aspects are presented as a part of the wavelet theory in several books on wavelets, their implementation is prone to misinterpretations in the literature on applied time series forecasting, possibly due to the complexity of the wavelet transform. Since our exposition is focused predominantly on these aspects, it provides a concise explanation which may be helpful to practitioners. The maximal overlap discrete wavelet transform is employed, other types of wavelet transforms also being briefly discussed.

Europe 2020 is a set of eight indicators used by the European Commission for monitoring headline targets of the Strategy for Smart, Sustainable and Inclusive Growth, which is considered to be the successor of the Lisbon Strategy. Values of these indicators vary among the European countries. Because some countries can be identified as outliers, robust regression as an acceptable analytic tool was applied. The aim of the paper is to construct relevant regression models for each Europe 2020 indicator as a dependent variable. The targets of the Europe 2020 indicators can be achieved by targeting some specific economic, social and environmental indicators.

By modifying and generalizing the wavelet-based approach of approximately simulating univariate long-memory processes that is available in the literature, we propose a methodology for simulating a bivariate stationary process, whose components exhibit different relationships at different scales. We derive the formulas for the autocovariance and cross-covariance sequences of the simulated bivariate process. We provide a setting for the parameters of the simulation which might generate a bivariate time series resembling that of stock log returns. Using this setting, we study the properties of our methodology via Monte Carlo simulation.

The paper focuses on competitiveness of the Prague University of Economics' graduates from the point of view of competence levels. The aim was to compare the achieved and required levels of competences evaluated by graduates. We analyzed the data collected within the international projects REFLEX 2006 and REFLEX 2010. We only analyzed responses of graduates from master degree studies at the University of Economics, Prague. We compared the achieved and required levels of competences within each period, then we compared the competence levels between the two periods, and finally we compared our results with the results of other surveys. The obtained knowledge indicates positive development in the area of competence levels acquired by the graduates. In addition to standard statistical coefficients of association, agreement and similarity, a new competence coefficient was proposed for the comparison of achieved and required levels of competences.

Percival and Walden (2002) present a wavelet methodology of the least squares estimation of the long memory parameter for fractionally differenced processes. We suggest that the general idea of using wavelets for estimating long memory could be used for the estimation of long memory in time series perturbed by noise. One prominent example thereof is the time series of log-Garman-Klass estimates of log volatility of financial markets. The estimator of Percival and Walden (2002) is biased if the long memory time series is perturbed by noise. We propose a new estimator of the long memory parameter which combines (in its construction) the frequency-domain approach of Sun & Phillips (2003) and the approach of Percival & Walden (2002). We illustrate the properties of the proposed estimator via Monte Carlo simulations. The results show that the estimator may be useful for the estimation of the long memory in volatility.

The article aims to survey recent advancements in risk management field. First a popular quantile-based risk measure Value-at-Risk (VaR), nowadays widely used to asses exposure to market and credit risk, is presented. Four different approaches are introduced, implemented and backtested on PSE index PX-50 time series. A class of so called coherent risk measures satisfying four qualities highly desired for a risk measure is propounded. As a response to VaR deficiencies several "improved" variants of VaR, some of them satisfying coherence axioms, are proposed. In the last section the motion of coherent risk measures is adapted to the multiperiod framework.