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Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data Cover

Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data

Business Systems Research Journal
Volume 9 (2018): Issue 2 (July 2018)
By: Josip Arnerić,  Tea Poklepović and  Juin Wen Teai  
Open Access
|Jul 2018

Authors

Josip Arnerić

jarneric@efzg.hr

Faculty of Economics and Business, University of Zagreb,, Zagreb, Croatia

Tea Poklepović

tpoklepo@efst.hr

Faculty of Economics, Business and Tourism, University of, Split, Croatia

Juin Wen Teai

juinwen.teai@gmail.com

National University of, Singapore, Singapore
DOI: https://doi.org/10.2478/bsrj-2018-0016 | Journal eISSN: 1847-9375 | Journal ISSN: 1847-8344
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Language: English
Page range: 18 - 34
Submitted on: Jan 29, 2018
|
Accepted on: Apr 21, 2018
|
Published on: Jul 28, 2018
Published by: IRENET - Society for Advancing Innovation and Research in Economy
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
high-frequency data,
realized variance,
nonlinearity,
long memory,
jumps,
leverage,
feedforward neural networks,
Heterogeneous AutoRegressive model
Related subjects:
Business and economics,
Business management,
Management, organization, corporate governance,
Business management, other,
Mathematics and statistics for economists,
Mathematics

© 2018 Josip Arnerić, Tea Poklepović, Juin Wen Teai, published by IRENET - Society for Advancing Innovation and Research in Economy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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