Emergent Latent-State Computation under Stochastic Volatility

Battle Count: 79

The paper is moderately relevant to quantitative trading. It provides mechanistic insights into how neural models internally represent latent volatility states, which is directly relevant to volatility forecasting used in risk management, options pricing, and portfolio construction. The finding that MSE training causes readout misalignment (while the representation still encodes the latent state) has practical implications for model training in trading systems. However, the paper is primarily an interpretability study on synthetic data rather than a direct trading strategy or forecasting improvement paper. The insights could inform better model design for volatility prediction but do not directly propose trading signals or strategies.

The core methodology is relatively straightforward: train small Transformers/MLPs on synthetic MSV data, fit linear ridge probes, and perform stage-wise analysis. The architectures are intentionally compact (one-layer Transformer, d_model=64). However, the full analysis pipeline involves multiple components: linear probing, output-head replacement, causal perturbation/ablation at multiple stages, and comparison across volatility periods, loss functions, and architectures. The synthetic data generation with cross-asset spillover matrices requires careful implementation. Overall, the computational requirements are modest but the experimental design is multi-faceted.

Code is publicly available on GitHub. The paper uses synthetic data from a well-specified multivariate stochastic volatility process with explicit parameters (N=6 assets, T=21, L=22, n=6000 windows). Architectural details are fully specified in Appendix A. The spillover matrix A is explicitly provided for all volatility periods. However, some hyperparameter choices (learning rate, batch size, number of epochs) are not fully detailed in the main text. Results are reported over n=10 seeds with median and IQR.