Explaining Aggregated Recovery Rates

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times.

Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices

We propose a cointegration-based Permanent-Transitory decomposition for non-stationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common component is further split into a long-term non-stationary and a short-term stationary part. A Monte Carlo experiment shows that incorporating the cointegration structure into the DFM leads to a better reconstruction of the space spanned by the factors, compared to the most standard technique of applying a factor model in differenced systems. We apply our procedure to a set of commodity prices to analyse the comovement among different markets and find that commodity prices move together mostly due to long-term common forces; while the trend for the prices of most primary goods is declining, metals and energy exhibit an upward or at least stable pattern since the 2000s.

CONCENTRATION OF SOLUBLE SALTS IN COOLING WATER OF POWER PLANTS WITH BLOW DOWN RECIRCULATION

The method for calculating the dynamics of concentration of soluble salts (tracers) using the assumption of their homogeneous distribution in the water volume of the circulated cooling system (CCS) under the conditions of recirculation of part of the purge water was proposed. The assumption of homogeneity of salt distribution is substantiated by the fact that the period of mixing of cold water (CW) in CCS is equal to several hours, and the period of setting the concentration limits is calculated in tens of days. In this approximation the law of change of concentration of tracers in time from the moment of their supply in CCS in modes without and with recirculation of a part of purge water and without any processing is received. It is shown that without recirculating treatment, recirculation of purge water increases the maximum salt concentration and the time to reach it. Attention is drawn to the fact that the flow rate consists of two parts: controlled and uncontrolled. When calculating the allowable amount of purge recirculation, it is necessary to use the value of the total purge flow. It is important to understand that the relative change in total purge is always less than the controlled one. To obtain the cost of full purge, it is necessary to calculate its uncontrolled part. The procedure for calculating the uncontrolled purge is given in the paper. Since there are restrictions for of maximum allowable concentration (MAC) on the concentrations of tracers in the CCS, the expressions obtained in this work are consistent with the requirements of the MAC and allow for a given degree of concentration of salts φ, to calculate the limit of recirculation flow. It is shown that the recirculation regime can be applied without additional measures for CCS with a low initial degree of salt concentration and low concentration in the feed water. The proposed algorithm is used to predict the results of recirculation in the case of a specific CСS for thermal power plants with an electrical capacity of 1200 MW and the ratio CMAC/C0 = 5. It is shown that in this case recirculation has significant economic and environmental effects. However, at high, concentrations of tracers C0, compared to MAC, feed water recirculation is meaningless. The calculation algorithm applies to tracers, i.e. salts that do not give deposits, but only limited by the MAC requirements and the risk of corrosion or environmental considerations. Therefore, the above technique cannot be directly used for sparingly soluble salts, such as calcium carbonate. In this case, it requires significant correction. Note the convenient use in calculations of the earlier introduced by us concept of regime (dynamic) factor φ, especially when comparing the real consumption of the main flows of ССS to calculated and in determining the evaporative capacity of cooling towers, or assessing the degree of salt concentration.

A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

Tax compliance, public spending and happiness in Europe

Purpose This study aims to analyze whether tax compliance is the basis for the short-run dynamics of the development of welfare and happiness. The strengthening of tax compliance of corporates and citizens is not only important to achieve the goals assumed by fiscal policy but also is part of the values that can generate a higher level of welfare and happiness in Europe. Design/methodology/approach This study uses a dynamic factor model to offer new indexes that allow to monitor tax compliance, public spending and happiness trajectories and to evaluate their short-run relationships. Next, an analysis of the cyclical characteristics in terms of duration, amplitude and intensity is provided using the Harding and Pagan method (2002). Findings The empirical findings show that the European countries were able to reinforce tax compliance during the expansionary periods of the economy, and this has made it possible to increase public spending, and indirectly, happiness. Otherwise, this paper shows that the contractions of public resources during the global crisis, such as the case in the COVID-19, reduced the possibilities of well-being in Europe and made it more difficult to increase public spending and happiness. Research limitations/implications This study tries to analyze the transmission channels and relationships of three very complex variables: tax compliance, public spending and happiness. Incorporating these three variables into this research, with a short-run perspective, the authors have opened a new line of research that enriched the previous analysis. Therefore, the authors’ results should be considered the first step, that this study is going to continue to unravel the complexity of these relationships. Practical implications The design of policies aimed at improving individual, corporate and the well-being of nations needs them to incorporate elements of tax compliance as an objective that has economic and social implications. Individuals and corporates contribute to a fairer and more equitable society through compliance with tax obligations. Originality/value To the best of the authors’ knowledge, this is the first paper that offers evidence on the short-run dynamics of tax revenue, public spending and happiness for a better understanding of their relationships and behavior during the different periods of the economy.

Dynamical Effective Field Model for Interacting Ferrofluids: II. The proper relaxation time and effects of dynamic correlations

The recently proposed dynamical effective field model (DEFM) is quantitatively accurate for ferrofluid dynamics. It is derived in paper I within the framework of dynamical density functional theory (DDFT) along with a phenomenological description of nonadiabatic effects. However, it remains to clarify how the characteristic rotational relaxation time of a dressed particle, denoted by τr, is quantitatively related to that of a bare particle, denoted by τr0. By building macro-micro connections via two different routes, I reveal that under some gentle assumptions τr can be identified with the mean time characterizing long-time rotational self-diffusion. I further introduce two simple but useful integrated correlation factors, describing the effects of quasi-static (adiabatic) and dynamic (nonadiabatic) inter-particle correlations, respectively. In terms of both the dynamic magnetic susceptibility is expressed in an illuminating and elegant form. Remarkably, it shows that the macro-micro connection is established via two successive steps: a dynamical coarse-graining with nonadiabatic effects accounted for by the dynamic factor, followed by equilibrium ensemble averaging captured by the static factor. By analyzing data from Brownian dynamics simulations on monodisperse interacting ferrofluids, I find τr/τr0 is, somehow unexpectedly, insensitive to changes of particle volume fraction. A physical picture is proposed to explain it. Furthermore, an empirical formula is proposed to characterize the dependence of τr/τr0 on dipole-dipole interaction strength. The DEFM supplemented with this formula leads to parameter-free predictions in good agreement with results from Brownian dynamics simulations. The theoretical developments presented in this paper may have important consequences to studies of ferrofluid dynamics in particular and other systems modelled by DDFTs in general.

VALIDATING DSGE MODELS WITH SVARS AND HIGH-DIMENSIONAL DYNAMIC FACTOR MODELS

A popular validation procedure for Dynamic Stochastic General Equilibrium (DSGE) models consists in comparing the structural shocks and impulse-response functions obtained by estimation-calibration of the DSGE with those obtained in an Structural Vector Autoregressions (SVAR) identified by means of some of the DSGE restrictions. I show that this practice can be seriously misleading when the variables used in the SVAR contain measurement errors. If this is the case, for generic values of the parameters of the DSGE, the shocks estimated in the SVAR are not “made of” the corresponding structural shocks plus measurement error. Rather, each of the SVAR shocks is contaminated by noncorresponding structural shocks. We argue that High-Dimensional Dynamic Factor Models are free from this drawback and are the natural model to use in validation procedures for DSGEs.

Short-Run Links in Ecological Footprint: A Dynamic Factor Analysis for the EU

The Ecological Footprint (EFP) is a useful indicator for assessing the progress of environmental performance and offers a solid basis for sustainability studies. In this paper, we contribute to the broadening of its possibilities of investigation by measuring the cross-country links in the EFP in global hectares per capita. The modeling framework is based on the dynamic factor analysis to estimate, in the parametric form, an index that provides information about the short-run dynamics of the EFP in the EU. Following this approach, we identify different patterns in the EFP behavior of the European countries during the period of 1962–2017. The results show stronger links across the EFP of the main European countries: France, Austria, Belgium, Germany, Denmark and the U.K. The proposed analysis gives a better understanding of the links behind environmental degradation in the EU and is applicable for the implementation and design of environmental policies.