Quantum Reservoir Computing for Realized Volatility Forecasting
This paper demonstrates quantum reservoir computing applied to realized volatility forecasting, outperforming classical econometric and machine learning models.
What it examines
This paper introduces a quantum reservoir computing approach using disordered spin systems to forecast market volatility. It combines quantum circuits with classical financial features, employing ensemble methods and forward feature selection to enhance prediction accuracy.
What it concludes
The study finds that small-scale quantum reservoir computing can outperform classical models in predicting volatility, suggesting applications in financial risk management and time series analysis, while future research could scale and refine quantum architectures for broader use.
Evidence objects
Quantum reservoir computing leverages a ten-qubit fully connected transverse-field Ising system to forecast S&P 500 volatility, outperforming classical models such as HAR, ARMAX, and LSTM-based methods with superior predictive capabilities.
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Researchers introduce QR1 and QR2 quantum models, demonstrating QR2s ensemble yields lower $$MSE$$ and enhanced Model Confidence Set performance while capturing complex time dependencies through Shapley value analysis, remarkably accurate.
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The study employs a rigorous rolling-window approach on long-term financial data, revealing how quantum mechanics properties such as $$\text{superposition}$$ and $$\text{entanglement}$$ may enhance non-linear pattern processing, despite hardware scalability challenges.
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This paper introduces a groundbreaking integration of quantum reservoir computing with financial econometrics to forecast realized volatility. Its originality emerges from merging quantum algorithms with standard reservoir computing techniques, presenting a novel perspective on derivative modeling. The fusion promises enhanced forecasting performance and profound market volatility understanding worthy of exploration.
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Raw abstract and provenance
Abstract: Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disci… ▽ More Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of quantum reservoir computing for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The quantum reservoir computing approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets, and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof-of-concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance. △ Less
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