Some statistical models vs. models based on SC for high frequency financial time series applied to bonds of commercial banks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F14%3A%230005348" target="_blank" >RIV/47813059:19240/14:#0005348 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Some statistical models vs. models based on SC for high frequency financial time series applied to bonds of commercial banks

Original language description

In neural networks modeling approach, a non-linear model is estimated based on machine learning methods. The study discusses, analytically and numerically demonstrates the quality and interpretability of the obtained prediction accuracy results from prediction models based on advanced statistical methods and models based on neural networks (intelligent methods). Both proposed approaches are applied to the financial time series of s of VUB bond prices. We found that it is possible to achieve significantrisk reduction in managerial decision-making by applying intelligent forecasting models based on the latest information technologies. In a comparative study is shown, that both presented modeling approaches are able to model and predict high frequency data with reasonable accuracy, but the neural network approach is more effective.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>

Continuities

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

Publication year

2014

Confidentiality

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

Name of the periodical

Advanced Material Research

ISSN

1022-6680

e-ISSN

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Volume of the periodical

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Issue of the periodical within the volume

neuvedeno

Country of publishing house

CH - SWITZERLAND

Number of pages

6

Pages from-to

435-440

UT code for WoS article

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EID of the result in the Scopus database

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