Quantum Reservoir Computing for Realized Volatility Forecasting

TL;DR

This paper demonstrates that quantum reservoir computing can effectively forecast realized volatility in finance, outperforming traditional models and offering a promising avenue for quantum-enhanced financial analysis.

Contribution

It introduces a quantum reservoir computing model using a transverse-field Ising Hamiltonian for volatility forecasting, showing its superiority over classical models.

Findings

Quantum reservoir computing outperforms benchmark models in volatility prediction.

Feature selection via wrapper-based forward selection improves model interpretability.

Results highlight potential of quantum computing in financial econometrics despite hardware limitations.

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 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.