The primary goal of the study is to diagnose satisfaction and loyalty drivers in Polish retail banking sector. The problem is approached with Customer Satisfaction Index (CSI) models, which were developed for national satisfaction studies in the United States and European countries. These are multiequation path models with latent variables. The data come from a survey on Poles’ usage and attitude towards retail banks, conducted quarterly on a representative sample. The model used in the study is a compromise between author’s synthesis of national CSI models and the data constraints. There are two approaches to the estimation of the CSI models: Partial Least Squares – used in national satisfaction studies and Covariance Based Methods (SEM, Lisrel). A discussion is held on which of those two methods is better and in what circumstances. In this study both methods are used. Comparison of their performance is the secondary goal of the study.
In this paper we show that in the lognormal discrete-time stochastic volatility model with predictable conditional expected returns, the conditional expected value of the discounted payoff of a European call option is infinite. Our empirical illustration shows that the characteristics of the predictive distributions of the discounted payoffs, obtained using Monte Carlo methods, do not indicate directly that the expected discounted payoffs are infinite.
In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior simulations we use an alorithm based on the collapsed Gibbs sampler. The presented methods are applied to the analysis of the price – wage mechanism in the Polish economy.
This article introduces and applies two refinements to the algorithm of solving rational expectations models of a currency union. Firstly, building upon Klein (2000), it generalizes the standard methods of solving rational expectations models to the case of time-varying nonstochastic parameters, recurring in a finite cycle. Such a specification occurs in a simple stylized New Keynesian model of the euro area after a joint introduction of (i) rotation in the ECB Governing Council (as constituted by the Treaty of Nice) and (ii) home bias in the interest rate decisions preferred by its members. Secondly, we apply the method of Christiano (2002) to solve the model with heterogenous information sets. This is justified if we argue that the information set of domestic economic agents in a currency union is home-biased (i.e. foreign shocks enter only with a lag). Both methods of solution are illustrated with simulation results.
The aim of this paper is to examine the empirical usefulness of two new MSF – Scalar BEKK(1,1) models of n-variate volatility. These models formally belong to the MSV class, but in fact are some hybrids of the simplest MGARCH and MSV specifications. Such hybrid structures have been proposed as feasible (yet non-trivial) tools for analyzing highly dimensional financial data (large n). This research shows Bayesian model comparison for two data sets with n = 2, since in bivariate cases we can obtain Bayes factors against many (even unparsimonious) MGARCH and MSV specifications. Also, for bivariate data, approximate posterior results (based on preliminary estimates of nuisance matrix parameters) are compared to the exact ones in both MSF-SBEKK models. Finally, approximate results are obtained for a large set of returns on equities (n = 34).
Volatility persistence is a stylized statistical property of financial time-series data such as exchange rates and stock returns. The purpose of this letter is to investigate the relationship between volatility persistence and predictability of squared returns.
This paper deals with the problem of nonstationarity of regressors in binary choice model. The limit distribution of the ML-estimator is mixed normal, but restriction testing shall not be based on standard t-statistic. The results of the conducted Monte Carlo experiment demonstrate that the true size of the restriction test is far from the significance level. Therefore, the t-Student statistic should be modified and this paper proposes its modification. The results of the Monte Carlo investigation point to the superiority of the new statistic.
Homeownership rates are very different across European countries. They range from below 50% in Germany to over 80% in Greece, Spain or Ireland. However the differences lie not only in the overall homeownership rates but also in its structure, and this is the focus of this paper. Its aim is to study the impact of microeconomic factors on household’s tenure choice, using a crosscountry comparative approach. Logit models are constructed for each country using data for year 2000 from the Consortium of Household Panels for European Socio-Economic Research micro-database. The models show that marriage is a significant determinant of the decision to move to homeownership in all analysed countries, while cohabitating households are more likely to rent, except for Denmark. Nationality, income and age proved to be significant explanatory variables in several countries, while staying insignificant in others.
We investigate the problem of setting revenue sharing rules in a team production environment with a principal and two agents. We assume that the project output is binary and that the principal can observe the level of agents’ actual eort, but does not know the production function. Identifying conditions that ensure the eciency of the revenue sharing rule, we show that the rule of equal percentage markups can lead to ination of project costs. This result provides an explanation for project cost overruns other than untruthful cost reporting.
The economy of Slovakia experienced a turning point in the 1st half of 2008 and entered a phase of decline. The negative impacts of the global economic crisis became evident in the 2nd half of 2008 and led into a recession in the 1st quarter of 2009. The composite leading indicator was originally intended for forecasting of business cycle turning points between the decline and growth phases. The aim of this paper is to transform the qualitative information from composite leading indicator into quantitative forecast and verify whether the beginning of recession in Slovakia could have been identied in advance. The ARIMAX and error correction models are used for the composite reference series and GDP forecasts respectively. The nal result shows that the composite leading indicator is useful not only for identifying turning points, but also for the prediction of recession phase.
This paper estimates the magnitude of the Baumol-Bowen and Balassa-Samuleson effects in the Polish economy. The purpose of the analysis is to establish to what extent the differential price dynamics in Poland and in the euro area and the real appreciation of PLN against EUR are explained by the differential in respective productivity dynamics. The historical contribution of the Baumol-Bowen effect to Polish inflation rate is estimated at 0.9 − 1.0 percentage points in the short run. According to estimation results, the Balassa-Samuelson effect contributed around 0.9 to 1.0 percentage point per annum to the rate of relative price growth between Poland and the euro area and 1.0 to 1.2 p.p. to real exchange rate appreciation. The long-run effects are of an approximately twice larger magnitude. Sub-sample calculations and productivity trends over the last decade suggest that this impact should be declining. However, its size is still non-negligible for policymakers in the context of euro adoption in Poland.
The Walters critique of EMU presumed that pro-cyclical country-specific real interest rates would incorporate significant macroeconomic instability in an environment of asymmetric shocks. The literature on optimum currency areas suggests a number of criteria to minimize this risk, such as market flexibility, high degrees of openness, financial integration or similarity in inflation rates. In this paper, we argue that an essential part of macroeconomic volatility in a monetary union’s member country also depends on the mechanism of forming expectations. This is mainly due to (i) the construction of ex ante countryspecific real interest rate, implying a strong or weak negative correlation with current inflation rate and (ii) anticipated (and hence smoothed) loss in competitiveness and boom-bust cycle. In a 2-region 2-sector New Keynesian DSGE model, we apply 5 different specifications of ex ante real interest rates, based on commonly considered types of expectations: rational, adaptive, static, extrapolative and regressive, as well as their hybrids. Our simulations show that rational expectations dominate the other specifications in terms of minimizing the volatility of the most macroeconomic variables. This conclusion is generally insensitive to which group of agents (producers or consumers) and which region (home or foreign) forms the expectations. It also turns out that for some types of expectations the Walters critique indeed applies, i.e. the system does not fulfil the Blanchard-Kahn conditions or the system’s companion matrix has explosive eigenvalues.
This paper points out that the ARMA models followed by GARCH squares are volatile and gives explicit and general forms of their dependent and volatile innovations. The volatility function of the ARMA innovations is shown to be the square of the corresponding GARCH volatility function. The prediction of GARCH squares is facilitated by the ARMA structure and predictive intervals are considered. Further, the developments suggest families of volatile ARMA processes.
In the paper an approach to decision making in situations with non-point-like characterisation and subjective evaluation of the actions is considered. The decision situation is represented mathematically as fuzzy multiobjective linear programming (fMOLP) model, where we apply the reduced fuzzy matrices instead of fuzzy classical numbers. The fMOLP model with reduced parameters is decomposable into the set of point-like models and the point-like models enable effective construction of an optimisation procedure – fBIP, see Wojewnik (2006ab), extending the bireference procedure by Michalowski and Szapiro (1992). The approach is applied to a fuzzy optimization problem in the area of telecommunication services.
The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models are able to generate forecasts for the nominal exchange rate of the Polish zloty that are more accurate than forecasts from a random walk. Our results confirm the main findings from the literature, namely that it is difficult to outperform a naive random walk in exchange rate forecasting contest.
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.
Forecasting yield curves with regime switches is important in academia and financial industry. As the number of interest rate maturities increases, it poses difficulties in estimating parameters due to the curse of dimensionality. To deal with such a feature, factor models have been developed. However, the existing approaches are restrictive and largely based on the stationarity assumption of the factors. This inaccuracy creates non-ignorable financial risks, especially when the market is volatile. In this paper, a new methodology is proposed to adaptively forecast yield curves. Specifically, functional principal component analysis (FPCA) is used to extract factors capable of representing the features of yield curves. The local AR(1) model with time-dependent parameters is used to forecast each factor. Simulation and empirical studies reveal the superiority of this method over its natural competitor, the dynamic Nelson-Siegel (DNS) model. For the yield curves of the U.S. and China, the adaptive method provides more accurate 6- and 12-month ahead forecasts.