The aim of this research was to determine the persistence of profit in an emerging banking sector of the Republic of Croatia. Most developing countries have experienced common changes within restructuring of the banking system and therefore, this issue has become crucial, especially after comparing poor empirical findings in these countries to the findings of developed countries. However, research related to this issue is non-existent in the Croatian banking sector. Moreover, among the few studies that were carried out on the territory of the Central and Eastern European banking markets, none of the studies have analysed the persistence of profit in terms of the Markov Chain stochastic process. In addition to the profit persistence analysis, authors defined and estimated a model that would enable the identification of the profitability determinants of Croatian banks. In this sense, the model incorporated three groups of profitability determinants: bank-specific, industry-specific and macroeconomic. The variables with a statistically significant impact on the profitability of banks were identified using a dynamic panel model, while the application of the Markov Chains stochastic process revealed that profit persistence was less likely to occur in banks with higher profit.

Abstract. Portfolio managers, option traders and market makers are all interested in volatility forecasting in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. A standard GARCH(1,1) model usually indicates high persistence in the conditional variance, which may originate from structural changes. The first objective of this paper is to develop a parsimonious neural networks (NN) model, which can capture the nonlinear relationship between past return innovations and conditional variance. Therefore, the goal is to develop a neural network with an appropriate recurrent connection in the context of nonlinear ARMA models, i.e., the Jordan neural network (JNN). The second objective of this paper is to determine if JNN outperforms the standard GARCH model. Out-of-sample forecasts of the JNN and the GARCH model will be compared to determine their predictive accuracy. The data set consists of returns of the CROBEX index daily closing prices obtained from the Zagreb Stock Exchange. The results indicate that the selected JNN(1,1,1) model has superior performances compared to the standard GARCH(1,1) model. The contribution of this paper can be seen in determining the appropriate NN that is comparable to the standard GARCH(1,1) model and its application in forecasting conditional variance of stock returns. Moreover, from the econometric perspective, NN models are used as a semiparametric method that combines flexibility of nonparametric methods and the interpretability of parameters of parametric methods.

The purpose of the article is to survey the role of information and communication technology (ICT) for hotel firm's competitiveness. Based on competitive advantage factor (CAF) and resource theory, this article empirically tests ICT as one of several possible competitiveness factors. The research is focused on analyse of ICT competitiveness position over time, with special attention to different generations of ICT technologies. An electronic survey instrument has been used to collect Slovenian hotel manager's opinion on competitiveness resources in 2000 and 2010. Hypothesis testing and cluster analyses has been applied, SPSS was also used. The article's findings indicate that hotels need time to recognise the competitiveness potential of every new resource, and once they start to implement it its importance may change over time. Some firms might be slower in implementing new ICT resources, yet, over time, the resource use converges among the firms. The process is repeated with every new ICT generation. The study informs firms and researchers on practical and research issues forthcoming with ICT progression. Research results directly benefits hotel managers by providing actual information on how to employ different generations of ICT. This contribution is a novel way of connecting a firm's competitiveness with different web generations over time.

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non-normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand-pull and cost-push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017.Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in-the-sample and outof-sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the 'best' NN because the optimal NN in-the-sample, based on MSE and/or AIC criteria, often has high out-ofsample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN.Moreover, JNN has a better forecasting performance than FNN.

Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. realized variance (RV) can be computed. Commonly used models for RV forecasting suffer from strong persistence with a high sensitivity to the returns distribution assumption and they use only daily returns. Objectives: The main objective is measurement and forecasting of RV. Two approaches are compared: Heterogeneous AutoRegressive model (HAR-RV) and Feedforward Neural Networks (FNNs). Even though HAR-RV-type models describe RV stylized facts very well, they ignore its nonlinear behaviour. Therefore, FNN-HAR-type models are developed. Methods/Approach: Firstly, an optimal sampling frequency with application to the DAX index is chosen. Secondly, in and out of sample predictions within HAR models and FNNs are compared using RMSE, AIC, the Wald test and the DM test. Weights of FNN-HAR-type models are estimated using the BP algorithm. Results: The optimal sampling frequency of RV is 10 minutes. Within HAR-type models, HAR-RV-J has better, but not significant, forecasting performances, while FNN-HAR-J and FNNLHAR- J have significantly better predictive accuracy in comparison to the FNN-HAR model. Conclusions: Compared to the traditional ones, FNN-HAR-type models are better in capturing nonlinear behaviour of RV. FNN-HAR-type models have better accuracy compared to traditional HAR-type models, but only on the sample data, whereas their out-of-sample predictive accuracy is approximately equal.

The importance of volatility for all market participants has led to the development and application of various econometric models. The most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. Since standard GARCH(1,1) model usually indicate high persistence in the conditional variance, the empirical researches turned to GJR-GARCH model and reveal its superiority in fitting the asymmetric heteroscedasticity in the data. In order to capture both asymmetry and nonlinearity in data, the goal of this paper is to develop a parsimonious NN model as an extension to GJR-GARCH model and to determine if GJR-GARCH-NN outperforms the GJR-GARCH model.

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The purpose of this paper is to investigate which of the proposed parametric models for extracting risk-neutral