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Sains
Malaysiana 53(9)(2024): 3229-3239
http://doi.org/10.17576/jsm-2024-5309-25
Enhanced Foreign Exchange Volatility Forecasting using
CEEMDAN with Optuna-Optimized Ensemble Deep Learning Model
(Ramalan Kemeruapan Tukaran Asing yang Dipertingkatkan
menggunakan CEEMDAN dengan Model Pembelajaran Mendalam Ensembel Dioptimumkan
Optuna)
REHAN KAUSAR1,
FARHAT IQBAL2,3,*, ABDUL RAZIQ2, NAVEED SHEIKH4 & ABDUL REHMAN4
1Department of Statistics, Sardar Bahadur Khan
Women's University, Quetta, Pakistan
2Department of Statistics, University of
Balochistan, Quetta, Pakistan
3Department of Mathematics, Imam Abdulrahman
Bin Faisal University, Saudi Arabia
4Department of Mathematics, University of
Balochistan, Quetta, Pakistan
Received: 9 December 2023/Accepted: 15 July 2024
Abstract
Foreign
Exchange (FX) is the largest financial market in the world, with a daily
trading volume that significantly exceeds that of stock and futures markets.
The prediction of FX volatility is a critical financial concern that has
garnered significant attention from researchers and practitioners due to its
far-reaching implications in the financial markets. This paper presents a novel
hybrid ensemble forecasting model integrating a decomposition strategy and
three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional
LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination
addresses individual models' limitations and further improves the accuracy and
stability of FX volatility forecasting. The proposed approach utilizes the
CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode
functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to
form the input dataset for the DL models. In addition, we employed an attention
mechanism to improve the effectiveness of the DL techniques. Furthermore, the
hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble
model for forecasting exchange rate volatility is developed by combining the
predictions of three distinct DL models. The proposed approach is evaluated
against various benchmark models using evaluation measures such as MSE, MAE,
HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The
results demonstrate that our proposed approach provides accurate and reliable
forecasts of FX volatility under different forecasting regimes, making it a
valuable tool for financial practitioners and researchers.
Keywords: Currency
exchange rate volatility; deep learning; ensemble; CEEMDAN; Optuna
Abstrak
Tukaran Asing
(FX) merupakan pasaran kewangan terbesar di dunia dengan volum dagangan harian
yang jauh melebihi pasaran saham dan pasaran hadapan. Ramalan turun naik FX merupakan
kebimbangan kewangan yang kritikal serta telah mendapat perhatian daripada
penyelidik dan pengamal kerana implikasinya yang meluas dalam pasaran kewangan.
Kajian ini membentangkan model ramalan ensembel hibrid baharu yang menyepadukan
strategi penguraian dan tiga model pembelajaran mendalam (DL): Memori Jangka
Pendek Panjang (LSTM), LSTM Dwiarah (BiLSTM) dan Rangkaian Neural Konvolusi
(CNN). Gabungan ini menangani had model individu dan meningkatkan lagi
ketepatan dan kestabilan ramalan turun naik FX. Pendekatan yang dicadangkan
menggunakan teknik CEEMDAN untuk menguraikan turun naik kepada pelbagai fungsi
mod intrinsik (IMF) yang berbeza dan menggabungkan IMF ini dengan turun naik
GARCH dan EGARCH untuk membentuk set data input bagi model DL. Di samping itu,
kami menggunakan mekanisme perhatian untuk meningkatkan keberkesanan teknik DL.
Tambahan pula, hiperparameter untuk model DL dioptimumkan menggunakan algoritma
Optuna. Akhir sekali, model ensembel hibrid untuk meramalkan turun naik kadar
pertukaran dibangunkan dengan menggabungkan ramalan tiga model DL yang berbeza.
Pendekatan yang dicadangkan dinilai berdasarkan pelbagai model penanda aras
menggunakan ukuran penilaian seperti MSE, MAE, HMSE, HMAE, RMSE, Q-like dan
pendekatan set keyakinan model (MCS). Keputusan menunjukkan bahawa pendekatan
yang dicadangkan dalam kajian ini menyediakan ramalan turun naik FX yang tepat
dan boleh dipercayai di bawah rejim ramalan yang berbeza, menjadikannya alat
yang berharga untuk pengamal dan penyelidik kewangan.
Kata kunci:
CEEMDAN; ensembel; kemeruapan kadar pertukaran mata wang; Optuna; pembelajaran
mendalam
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*Corresponding
author; email: fsmuhammad@iau.edu.sa
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