Forecasting Models for WIG20 Index Based on Advenced Statistical Models vs. SC Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F14%3A86095864" target="_blank" >RIV/61989100:27510/14:86095864 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting Models for WIG20 Index Based on Advenced Statistical Models vs. SC Models

Popis výsledku v původním jazyce

In this paper, we consider the accuracy of forecasting models based on statistical (stochastic), machine learning methods and an intelligent methodology based on soft or granular computing. In this paper, we illustrate the ARCH-GARCH methodology on the developing a forecast model for time series of polish WIG20 stock indexes and make comparisons the forecast accuracy with the class of RBF neural network models. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented. We show a new approach of function estimation for nonlinear time series model by means of a granular neural network based on Gaussian activation function modeled by cloud concept. In a comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.

Název v anglickém jazyce

Forecasting Models for WIG20 Index Based on Advenced Statistical Models vs. SC Models

Popis výsledku anglicky

In this paper, we consider the accuracy of forecasting models based on statistical (stochastic), machine learning methods and an intelligent methodology based on soft or granular computing. In this paper, we illustrate the ARCH-GARCH methodology on the developing a forecast model for time series of polish WIG20 stock indexes and make comparisons the forecast accuracy with the class of RBF neural network models. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented. We show a new approach of function estimation for nonlinear time series model by means of a granular neural network based on Gaussian activation function modeled by cloud concept. In a comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Výzkumný tým pro modelování ekonomických a finančních procesů na VŠB-TU Ostrava</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Managing and Modeling of Financial Risks : 7th international scientific conference : proceedings : 8th-9th September 2014, Ostrava, Czech Republic. [Part I-III]

ISBN

978-80-248-3631-7

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

470-477

Název nakladatele

VŠB - Technical University of Ostrava

Místo vydání

Ostrava

Místo konání akce

Ostrava

Datum konání akce

8. 9. 2014

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000350605800059