REALIZED VOLATILITY PREDICTION WITH A HYBRID MODEL: LSTM-CEEMDAN
Keywords:
LSTM, CEEMDAN, Realized volatilityAbstract
The realized volatility (RV) in financial time series is characterized by nonlinearity, volatility, and noise. It is challenging to predict RV with a solitary forecasting model for precision. This study employs a hybrid model that integrates the Long Short-Term Memory (LSTM) network with the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for the purpose of forecasting the returns volatility (RV) of the S&P 500 index, thereby validating its accuracy and robustness.References
[1] Singh R, Srivastava S. Stock prediction using deep learning. Multimedia Tools and Applications, 2017, 76: 18569-18584.
[2] Bhattacharjee I, Bhattacharja P. Stock price prediction: a comparative study between traditional statistical approach and machine learning approach. 2019 4th international conference on electrical information and communication technology (EICT), 2019: 1-6
[3] Ariyo AA, Adewumi AO, Ayo CK. Stock price prediction using the ARIMA model. 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, 2014: 106-112.
[4] Cortes C, Vapnik V. Support-vector networks. Mach Learn, 1995, 20: 273–97.
[5] Suykens JA, Van Gestel T, De Brabanter J, De Moor B, Vandewalle JP. Least squares support vector machines. World scientific, 2002.
[6] Zhu BZ, Wei YM. Carbon price prediction based on integration of GMDH, particle swarm optimization and least squares support vector machines. Syst Eng-Theory Pract, 2011, 31(12): 2264–71.
[7] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436–44
[8] Yah?i M, ?anako?lu E, A?ral? S. Carbon price forecasting models based on big data analytics. Carbon Manage, 2019, 10(2):175–87.
[9] Holthausen RW, Larcker DF. The prediction of stock returns using financial statement information. Journal of accounting and economics, 1992, 15(2-3): 373-411.
[10] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735–80.
[11] Zhang Z. Research on stock index prediction based on ARIMA-CNN-LSTM model. 9th International Conference on Financial Innovation and Economic Development (ICFIED 2024), 2024, 558-565.
[12] Daubechies I. Ten lectures on wavelets. Society for industrial and applied mathematics, 1992.
[13] Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the Hilbert spectrum for non-linear and nonstationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci, 1998, 454(1971): 903–95.
[14] Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal, 2009, 1(01): 1–41.
[15] Yeh JR, Shieh JS, Huang NE. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Adv Adapt Data Anal, 2010, 2(02): 135–56.
[16] Torres ME, Colominas MA, Schlotthauer G, Flandrin P. A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE international conference on acoustics, speech, and signal processing (ICASSP). , 2011, 4144–7.
[17] Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process, 2013, 62(3): 531–44.