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Journal of Automation and Information Sciences

Выходит 12 номеров в год

ISSN Печать: 1064-2315

ISSN Онлайн: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

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Forecasting the Volatility of Financial Processes with Conditional Variance Models

Том 46, Выпуск 10, 2014, pp. 11-19
DOI: 10.1615/JAutomatInfScien.v46.i10.20
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Краткое описание

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.

ЦИТИРОВАНО В
  1. Kuznietsova Nataliia, Bidyuk Peter, Intelligence Information Technologies for Financial Data Processing in Risk Management, in Data Stream Mining & Processing, 1158, 2020. Crossref

  2. Bidyuk Petro, Polozhaenko Sergii, Kuznietsova Nataliia, Levenchuk Liudmyla, Probabilistic Data Analysis in Non-Stationary Processes Forecasting, 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC), 2020. Crossref

  3. Kuznietsova Nataliia, Bidyuk Petro, Heteroskedasticity Models for Financial Processes Modelling and Forecasting, 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), 2020. Crossref

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