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

Arnerić Josip (), Poklepović Tea () and Teai Juin Wen ()
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Arnerić Josip: Faculty of Economics and Business, University of Zagreb,Zagreb, Croatia
Poklepović Tea: Faculty of Economics, Business and Tourism, University ofSplit, Croatia
Teai Juin Wen: National University ofSingapore, Singapore

Business Systems Research, 2018, vol. 9, issue 2, 18-34

Abstract: 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 FNNLHAR- 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.

Keywords: high-frequency data; realized variance; nonlinearity; long memory; jumps; leverage; feedforward neural networks; Heterogeneous AutoRegressive model (search for similar items in EconPapers)
Date: 2018
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