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https://doi.org/10.2478/bsrj-2018-0016

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

Josip Arnerić   ORCID icon orcid.org/0000-0002-2901-2609 ; Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia
Tea Poklepović   ORCID icon orcid.org/0000-0002-6279-6070 ; Faculty of Economics, Business and Tourism, University of Split, Croatia
Juin Wen Teai ; National University of Singapore, Singapore

Puni tekst: engleski, pdf (518 KB) str. 18-34 preuzimanja: 118* citiraj
APA 6th Edition
Arnerić, J., Poklepović, T. i Teai, J.W. (2018). Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data. Business Systems Research, 9 (2), 18-34. https://doi.org/10.2478/bsrj-2018-0016
MLA 8th Edition
Arnerić, Josip, et al. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data." Business Systems Research, vol. 9, br. 2, 2018, str. 18-34. https://doi.org/10.2478/bsrj-2018-0016. Citirano 20.07.2019.
Chicago 17th Edition
Arnerić, Josip, Tea Poklepović i Juin Wen Teai. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data." Business Systems Research 9, br. 2 (2018): 18-34. https://doi.org/10.2478/bsrj-2018-0016
Harvard
Arnerić, J., Poklepović, T., i Teai, J.W. (2018). 'Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data', Business Systems Research, 9(2), str. 18-34. https://doi.org/10.2478/bsrj-2018-0016
Vancouver
Arnerić J, Poklepović T, Teai JW. Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data. Business Systems Research [Internet]. 2018 [pristupljeno 20.07.2019.];9(2):18-34. https://doi.org/10.2478/bsrj-2018-0016
IEEE
J. Arnerić, T. Poklepović i J.W. Teai, "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data", Business Systems Research, vol.9, br. 2, str. 18-34, 2018. [Online]. https://doi.org/10.2478/bsrj-2018-0016

Sažetak
Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. realized variance (RV) can be computed. Commonly used models for RV forecasting suffer from strong persistence with a high sensitivity to the returns distribution assumption and they use only daily returns. Objectives: The main objective is measurement and forecasting of RV. Two approaches are compared: Heterogeneous AutoRegressive model (HAR-RV) and Feedforward Neural Networks (FNNs). Even though HAR-RV-type models describe RV stylized facts very well, they ignore its nonlinear behaviour. Therefore, FNN-HAR-type models are developed. Methods/Approach: Firstly, an optimal sampling frequency with application to the DAX index is chosen. Secondly, in and out of sample predictions within HAR models and FNNs are compared using RMSE, AIC, the Wald test and the DM test. Weights of FNN-HAR-type models are estimated using the BP algorithm. Results: The optimal sampling frequency of RV is 10 minutes. Within HAR-type models, HAR-RV-J has better, but not significant, forecasting performances, while FNN-HAR-J and FNN-LHAR-J have significantly better predictive accuracy in comparison to the FNN-HAR model. Conclusions: Compared to the traditional ones, FNN-HAR-type models are better in capturing nonlinear behaviour of RV. FNN-HAR-type models have better accuracy compared to traditional HAR-type models, but only on the sample data, whereas their out-of-sample predictive accuracy is approximately equal.

Ključne riječi
high-frequency data; realized variance; nonlinearity; long memory; jumps; leverage; feedforward neural networks; Heterogeneous AutoRegressive model

Hrčak ID: 203479

URI
https://hrcak.srce.hr/203479

Posjeta: 193 *