Accurately predicting volatility has always been the focus of government decision-making departments, financial regulators and academia. Therefore, it is very crucial to precisely predict the realized volatility (RV) ...
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Accurately predicting volatility has always been the focus of government decision-making departments, financial regulators and academia. Therefore, it is very crucial to precisely predict the realized volatility (RV) of the stock price index. In this paper, we take the RV sequences of Shanghai Stock Exchange Composite Index (SSEC), Standard & Poor 500 index (SPX) and Financial Times Stock Exchange Index (FTSE) as the research objects, and propose a predictive model based on optimized variational mode decomposition (VMD), deep learning models including deep belief network (DBN), long short-term memory network (LSTM) and gated recurrent unit (GRU), and reinforcement learningq-learningalgorithm. Firstly, the original RV sequence is decomposed by using the VMD ideal parameters optimized by grey wolf optimizer (GWO) to obtain the intrinsic mode functions (IMFs). Then, DBN, LSTM and GRU are used to predict same IMF simultaneously. Finally, the optimal weights of the above three models are determined by the q-learningalgorithm to construct an integrated model, and the final results are obtained after accumulating the predicted values of each IMF. The predictive performance of the model was evaluated by four loss functions: the mean average error (MAE), mean squared error (MSE), heterogeneous mean average error (HMAE), heterogeneous mean squared error (HMSE) and modified Diebold and Mariano test (MDM). The experimental results show that the constructed GVMD-q-DBN-LSTM-GRU method has better performance that the comparison model in both emerging and developed markets.
Predicting volatility in financial markets is an important task with practical uses in decision- making, regulation, and academic research. This study focuses on forecasting realized volatility in stock indices using ...
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Predicting volatility in financial markets is an important task with practical uses in decision- making, regulation, and academic research. This study focuses on forecasting realized volatility in stock indices using advanced machine learning techniques. We examine three key indices: the Shanghai Stock Exchange Composite (SSE), Infosys (INFY), and the National Stock Exchange Index (NIFTY). To achieve this, we propose a hybrid model that combines optimized Variational Mode Decomposition (VMD) with deep learning methods like Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Using data from 2015 to 2022, we analyse how well these models predict volatility. Our findings reveal distinct patterns: the SSE shows high unpredictability, INFY is prone to extreme positive volatility, and NIFTY is relatively moderate. Among the models tested, the q-VMD-ANN-LSTM-GRU hybrid model consistently performs best, providing highly accurate predictions for all three indices. This model has practical benefits for financial institutions. It improves risk management, supports investment decisions, and provides real-time insights for traders and risk managers. Additionally, it can enhance stress testing and inspire innovative trading strategies. Overall, our study highlights the potential of advanced machine learning, especially hybrid models, to address financial market complexities and improve risk management practices.
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