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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v46.i10.20
pages 11-19

Forecasting the Volatility of Financial Processes with Conditional Variance Models

Petr I. Bidyuk
Institute for Applied System Analysis of National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute", Kiev
Natalya V. Kuznetsova
Institute for Applied System Analysis of National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute", Kiev

ABSTRAKT

Three structures of the dynamics models of a conditional variance, that are used for computing short-term predictions on training and test samples, have been estimated. Estimation of the model parameters is made on the basis of the Monte Carlo method for Markov chains. To calculate the estimates of volatility forecasts the prediction function has been obtained on the basis of constructed models. The estimates of the volatility forecasts, calculated on the basis of model of stochastic volatility and E-GARCH models, show similar results, which confirms the correctness of the approach as a whole.


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