C53 - Forecasting Models; Simulation MethodsReturn

This study examines the significant influence of oil price fluctuations on the economies of oil--exporting countries. While elevated oil prices can result in foreign currency inflows and advantages for oil-exporting countries, they can also trigger adverse effects, including a reduction in manufacturing sectors and a loss of price competitiveness due to currency appreciation. This research focuses on the period from 2004Q1 to 2021Q4, examining the influence of oil price fluctuations on key macroeconomic indicators in Russia, including industrial production, exchange rates, inflation and interest rates. The structural VAR model findings confirm that the monetary channel demonstrates a higher degree of responsiveness to oil price shocks compared to the fiscal channel. Specifically, the study observes that industrial production exhibits a pronounced procyclical response to oil price shocks through the fiscal channel. Conversely, the monetary channel reveals that increased oil price volatility exerts pressure on the Russian rouble, resulting in a counter-cyclical behaviour in inflation and interest rates.

The urgent need to address climate change and the depletion of natural resources has led governments worldwide to allocate significant resources towards research and development in clean energy technologies and energy efficiency. This study evaluates the effectiveness of renewable energy and energy efficiency initiatives in reducing CO₂ emissions, taking into account the influences of natural resource availability and political risk. Using data from France spanning from 1985 to 2021, we employ the kernel-based regularized least squares (KRLS) methodology, complemented by quantile regression (QR), to analyse these relationships. Our findings indicate that policies promoting energy efficiency and green energy have a positive impact on reducing CO₂ emissions. However, the availability of natural resources and political risk exacerbate environmental challenges by increasing CO₂ emissions. Thus, our study underscores the importance of continued support from policymakers for renewable energy development and energy efficiency research to effectively pursue Sustainable Development Goals (SDGs). Additionally, as the world prepares for COP28, our findings emphasize the urgency of these initiatives in meeting global climate targets.

Bankruptcy prediction has long been regarded as a critical topic within the academic and banking community. To the best of our knowledge, no previous study in the Slovak Republic has attempted to develop a bankruptcy prediction model putting together statistical and artificial intelligence approaches performed on a such an amount of data. This paper seeks to fill this gap. Our aim is to develop a hybrid bankruptcy prediction model using a genetic algorithm in the process of training a neural network (GA-NN). The research data set comprises a balanced sample of both healthy and bankrupt firms operating in Slovakia in the period from 2014 to 2017. Financial information regarding a firm's financial situation are acquired from the Finstat database, which stores annual reports. For the purpose of comparing the classification accuracy of the proposed GA-NN model, two more models are constructed, namely BP-NN (back-propagation neural network model) as well as MDA (multiple discrimination model). The results gained by utilizing these models suggest the superiority of the developed GA-NN model to both BP-NN and MDA models in terms of prediction performance.

This paper uses a parametric statistical simulation (Monte Carlo) model to investigate and com-pare the effective lifetime tax burden on variable incomes for private-sector employees in the Czech Republic, Slovakia, Poland and Hungary, based on current rates and calibrated against actual income distributions. It is shown that the existing Czech system is highly inefficient, lacking horizontal as well as vertical equity, and this is aggravated by increased income volatility. On the other hand, higher income risk in all countries, except Hungary using flat tax, results in less progression than expected, primarily due to the existence of a minimum wage, which effectively serves as insurance, and which the dynamic model captures as a system feedback. This finding may contribute to a reassessment of existing assumptions on the detrimental effect of progressive tax systems on the incentives of individuals to undertake risky decisions, such as investments in human capital.

This article deals with multistate demography approach applied on the labour market. The multistate demography - methodology and history - are briefly described. The application of this method is presented using transitions between economically active and inactive states on the labour market and among economically inactive, employed and unemployed states. Estimation based on the Labour Force Sample Survey (LFSS) for the Czech Republic shows that during the life course (15-64 years of age) the expected length of stay for each state changes depending on the original state and age. Main differences were found for younger (25-40 years) and older (55+ years) persons.

Predictions of economic activity are sine qua non of economic policy. Nevertheless, predictions are characterised by the high level of inaccuracy; the main aim of the paper is to provide a potential theoretical explanation of frequent failures by focusing on the refl ection of real market proces in forecasting apparatus. It is argued that the role of intermediate products is underestimated in the considerations of future development of economic activity, even if these has presumably higher relevance to the level of economic activity in the future. Mutual relation between expenditures on consumption and production expense is discussed and verifi ed by use of econometric tools. It is found that even if there is long-term relation between consumption and production expenditures, from the short-term perspective, actual consumption does not have statistically signifi cant impact on future decisions of producers.

The paper is concerned with the use of several methods that can be useful from the point of view of trend reversal in financial time series. These methods are demonstrated on PX index time series during 2002-2009. The research itself is subdivided into four parts corresponding to individual analytical methods used. The first group contains the use of moving EGARCH(1,1) model to daily relative returns of PX index. The results obtained are indicative of the importance of negative parameter values, which can be considered as precursors of the trend reversal. The second group contains different moving characteristics that are able to signalize regime changes in certain time intervals. Particularly, the information related to intraday price variations proved to be useful. Third, selected price indicators from technical analysis were employed. Among them, Simple Moving Averages, Bollinger Bands, Relative Strength Index and Stochastic led to acceptable predictions. Last, the predictive ability of Artificial Neural Networks was tested with respect to different network structure and number of delayed values of explanatory variable. The results obtained here are promising, but further research in this direction is necessary.

The article discusses the relationship between monetary policy and price of oil, in broader sense price of commodities. Firstly it focuses on describing the relationship of key macroeconomic variables, gas prices and other commodities against oil prices. Subsequently, it discusses the existence of a "transmission channels" through which monetary policy can be propagated into oil prices (or prices of commodities). Secondly it provides further insight into the forecasting process of the CNB, in both a retrospective look back at the prospects of oil prices in the past and the analysis of transitory and permanent shock (the rise in oil prices of 30 USD/b). Simulated oil price shock is calculated from the average level of Brent oil prices in the first quarter of 2010, i.e. 77.50 USD/b.

We show that the cusp catastrophe model explains the crash of stock exchanges much better than other models. On the data of U.S. stock markets we demonstrate that the crash of 1987 may be better explained by cusp catastrophe theory, which is not true for the crash of 2001. With the help of sentiment measures, such as index put/call options ratio and volume (the former models the proportion of the chartists, while the latter the fundamentalists), we have found that the 1987 returns are clearly bimodal and contain bifurcation flags. The cusp catastrophe model fits these data better then alternative models. Therefore we may say that the crash may have been led by internal forces. However, the causes for the crash of Sept. 11, 2001 are external, which is also evident in much weaker presence of bifurcations in the data. Thus alterantive models may be used for its explanation.

The article aims at introducing new methodology for recognizing suitable indicators to monitor the potential risk of extensive pressure on the exchange rate (early warning indicators) and for identifying vulnerabilities in an economy to this pressure reflected by simultaneous negative development of the observed early warning indicators. The construction of the empirical model is based on the combination of the signal approach and the binary model of behaviour. This model focuses on both strong and increased pressure on exchange rate and the parameters are adjusted to the Czech environment. The results indicate that the most significant early warning indicator for the Czech economy is the gap between current account deficit and foreign direct investment and further M2 to foreign exchange ratio, GDP growth and export growth. In total the model identified correctly 72 per cent periods of increased vulnerability of the Czech economy to excessive devaluation pressure. At the same time it identified correctly 78 per cent of tranquil periods. This methodology constitutes an innovative approach, which needs a further verification.

The large financial crises in the last decade have increased the interest of many economists in searching for some indicators, which can predict speculative attacks on currencies. Most of these studies concern on emerging economies, because they are more vulnerable to such speculative attacks. This paper points out main methodological issues of two standard approaches to the construction of the early warning system - the signal approach and the regression probit or logit model approach. Based on avoiding these issues alternative approaches have been evolved. Three of them - two regimes model, VAR model and Markov-switching model - are described in the next part of this paper. The comparison of predictive power shows the importance of the construction of country specific early warning systems.