Realized Volatility Prediction of Stock Price Index Based 
on GA-CEEMDAN-LSTM Model

Siqi Zhang; Mingwei Li*

doi:10.70711/frim.v3i3.6173

Realized Volatility Prediction of Stock Price Index Based on GA-CEEMDAN-LSTM Model

Siqi Zhang, Mingwei Li*

Abstract

The realized volatility (RV) has the characteristics of nonlinear and unstable, and the accuracy of direct prediction is relatively low.
In this paper, CEEMDAN is used to decompose the original RV sequence, and the decomposed signals are respectively predicted by RNN,
SVR, HAR, LSTM and other models. The comparison of evaluation indicators shows that the LSTM model has a better effect. Finally, genetic
algorithm optimizes the hyperparameters of LSTM model and improves the prediction performance of the model.

Keywords

Realized volatility; Genetic Algorithm; Complete Ensemble Empirical Mode Decomposition with Adaptive Noise; Long ShortTerm Memory Network model

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References

[1] Andersen, T.G., Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.

[2] Andersen TG, Bollerslev T, Diebold FX, Labys P. Modeling and forecasting realized volatility. Econometrica. 2003 Mar;71(2):579-625.

[3] Barndorff-Nielsen, O.E., Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility

models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 253-280.

[4] Corsi, F. (2009). A simple long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.

[5] Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (1999). Manuscript, Northwestern University, Duke University and University of Pennsylvania.

[6] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Liu, H. H. (1998). The empirical mode decomposition and

the Hilbert spectrum for non-linear and non- stationary time series analysis. In Proceedings of the royal society of london a: Mathematical, physical and engineering sciences: 454 (pp. 903995). The Royal Society.

[7] Wu, Z. H., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise assisted data analysis method. Advances in Adaptive Data Analysis, 1, 141.

[8] Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with

adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 41444147).

[9] Hansen, P.R., Lunde, A. (2006). Realized volatility forecast evaluation. International Economic Review, 47(2), 527-556.

DOI: http://dx.doi.org/10.70711/frim.v3i3.6173

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