Hybrid ML models for volatility prediction in financial risk management
This study develops a hybrid ML system combining VMD, deep learning, and Q-learning to predict volatility, improving financial risk management.
What it examines
The study applies advanced machine learning to predict financial volatility. Using high-frequency data, it decomposes volatility with Variational Mode Decomposition (VMD) and uses neural networks (ANN, LSTM, GRU) with a Q-learning ensemble to improve prediction accuracy. It aims to enhance risk management and investment decision-making.
What it concludes
The results show the Q-VMD-ANN-LSTM-GRU model predicts volatility best. This can help portfolio managers, risk teams, and policymakers adjust investments, hedge risks, and design better financial policies. Future work may further refine these tools for real-time financial decision-making.
Evidence objects
A novel hybrid model combining Variational Mode Decomposition, Artificial Neural Networks, Long Short-Term Memory, Gated Recurrent Units, and an innovative Q-learning ensemble improves volatility predictions compared to traditional forecasting methods.
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Using one-minute data from 2015 to 2022, the hybrid model lowers error metrics like $$MAPE$$, $$MSE$$, and $$RMSE$$ by decomposing signals into intrinsic mode functions, capturing short- and long-term trends.
key_findings bullet 2 · key_findings · validation V0
The study unveils a framework that fuses neural networks with reinforcement learning, significantly improving forecasting and risk management for trading and portfolio optimization, but necessitates further scalability and efficiency evaluations.
key_findings bullet 3 · key_findings · validation V0
The paper presents a hybrid method that melds established machine learning techniques including $\text{ANN}$, $\text{LSTM}$, and $\text{GRU}$ with signal decomposition ($\text{VMD}$) and reinforcement learning ($\text{Q-learning}$) for volatility prediction. Although the approach primarily extends existing methodologies, its timely integration offers compelling, unique insights for financial risk management and derivative modeling applications.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … This study focuses on forecasting realized volatility in stock indices using advanced machine learning techniques. We examine three key indices: the Shanghai Stock …
Source row: 1064 · abstract type: snippet