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MODELOVANIE VOLATILITY A PREDIKČNÉ MODELY VYSOKOFREKVENČNÝCH FINANČNÝCH DÁT: ŠTATISTICKÝ A NEURONOVÝ PRÍSTUP

100%
Marček D.
Ekonomický časopis (Journal of Economics)
|
2014
|
vol. 62
|
issue 2
133 – 149
XX
In the article we first introduce asymmetric response of equity volatility to return shock and then the effect of good and bad news to volatility for empirical time series of EUR/USD (EUR currency against US dollar) exchange rates in the pre-crisis period, during the crisis and the post-crisis period. We found that GARCH-class models with normal errors are not capable to capture fully the leptokurtosis in empirical time series, while Student´s t and GED errors provide better description for the conditional volatility. Then, we alternatively develop forecasting models based on the ARIMA/GARCH methodology and on the neural approach. In the direct comparison between statistical and neural models, the experiment shows that the neural approach clearly improve the forecast accuracy.
2

MANAGERIAL DECISION-MAKING: MEASURING AND MANIFESTATIONS OF RISKS AND THE POSSIBILITIES OF THEIR REDUCING

51%
Marček D., Fraňo M., Marček M.
Ekonomický časopis (Journal of Economics)
|
2011
|
vol. 59
|
issue 4
392 – 411
EN
The paper is concerned with measuring and assessment of risk scenes in managerial decision-making. It builds upon the uncertainty of economic information, which is converted into the concept of risk scene expressed in terms of probability and using confidence intervals of the predicted quantities. The paper explains the relation of a degree of risk expressed by the classical information measure, bit, by the concept of confidence intervals, or possibly by the standard deviation. When making decisions, the manager is interested not only in the quantitatively expressed value of risk scene with the use of forecasting models, but mainly in the impact of decrease/increase of decision-making risk expressed by the effect, i.e. profit/loss caused by such a decision to achieve targets. A method of decision effect calculation is proposed which is derived from the information entropy change and the change in risk scene in managerial decision-making. Forecasting models are applied which are based on an expert estimate and a statistical theory, and the risk scenes are assessed in forecasting models based on neural networks.
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