Forecast S&P 500 Volatility with Quantum Reservoir ComputingOne-Step-Ahead Realized Volatility
Predict next-month volatility from market and macro history.
Forecast Realized Volatility from Market and Macro Signals
Market Volatility Forecasting | Quantum Reservoir Computing
The source material solves one-step-ahead forecasting of realized volatility for the S&P 500 using 816 monthly observations from 1950-01-31 to 2017-12-31. The dataset contains 17 numeric predictors plus the Date column, with no missing periods and no duplicate rows.
Inputs include market and macroeconomic series such as DP, EP, MKT, SMB, HML, TB, DEF, IP, INF, STR, and lagged volatility terms. The implementation builds lag windows of length 3 and forecasts the next realized-volatility value from recent history.
- • End-to-end Quantum Reservoir Computing executed on a quantum simulator.
- • Results: MSE = 0.1062, RMSE = 0.3258, MAE = 0.2457, MAPE = 8.1221, and QLIKE = 1.1600.
- • Downloadable research-style report covering methods, experiments, results, and references.
- • Executable Python code with Ridge, RCX, LSTM, and QRC benchmark pipelines.
- • Architecture details: 10 qubits, 10 trainable weights in the readout, and approximately 4-6 min training time.
- • Classical baseline comparison against Ridge, Echo State Network, and LSTM.
Quantum Reservoir Computing (QRC)
In this workflow, each input vector is encoded through parameterized single-qubit rotations, the joint quantum state evolves under a fixed Hamiltonian with evolution time τ = 1, and expectation values of Pauli-Z observables form a nonlinear feature representation for a classical ridge readout. The source material refers to this fixed-reservoir variant as QR1 and benchmarks it against Ridge, RCX, and LSTM baselines.
Strengths (Hypotheses)
- The fixed quantum reservoir provides a nonlinear representation of sequential inputs with memory effects.
- Only the classical readout is trained, which lowers optimization cost versus fully variational models.
- Input and hidden qubits let the model combine current features with information from previous time steps.
- The relatively small reservoir and shallow temporal depth are more realistic for near-term hardware than deeper trainable circuits.
Weaknesses & Risks
- Quality gains are task-dependent and still need verification against strong classical baselines.
- Performance can be sensitive to feature selection, encoding, Hamiltonian choice, evolution time, and qubit allocation.
- A small quantum reservoir may have limited memory capacity for longer or richer sequences.
- The current source material reports simulator results only, with no noise-model or real-device validation.
Proof-of-Concept Simulation Results
Task: one-step-ahead monthly realized-volatility forecasting for the S&P 500.
Dataset
816 monthly rows
Setup
10 qubits
Execution Environment
Quantum simulator
Key Outcomes
Test MSE
0.1062
MAPE
8.1221%
QLIKE
1.1600
Training Time
4-6 min
Business Impact
The source material converts the forecasting uplift into a reference business case for a hypothetical $100M S&P 500-tracking portfolio. It estimates a total annual impact of $82K-$123K, while explicitly noting that these figures are scenario-based and not direct trading profits.
Risk-Buffer Efficiency
Estimated annual value per $100M portfolio
Scenario-based estimate assuming a 10% annual cost of risk-buffer capital.
Volatility Targeting
Estimated annual value per $100M portfolio
Scenario-based estimate assuming 5% annual value capture from better exposure adjustment.
Hedging Support
Estimated annual value per $100M notional
Scenario-based estimate derived from QLIKE improvement and a conservative hedging-error budget.
How it works
Simple and transparent: from your brief to quantum results, code, and a paper
Describe
Map your forecasting brief to one-step-ahead realized volatility with market and macro signals
Confirm
Validate the monthly 1950-2017 dataset, lag-window design, and baseline comparison setup
Run
Run the 10-qubit QRC pipeline with fixed seeds and reproduce Ridge, RCX, and LSTM baselines
Review
Inspect MSE, MAE, MAPE, and QLIKE and review how the forecast tracks volatility regimes
Benchmark
Compare QRC against classical baselines and assess simulator-to-hardware readiness constraints
Ready to See It in Action?
Experience Quantum Reservoir Computing for market volatility forecasting, review benchmark metrics and reproducible run logs, and download the complete code and report.
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