Author

Kiseop Lee

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Preprint Oct 2026

Scale or speed? When do path signatures improve volatility-regime forecasts?

We study when path signatures, increasingly used as model-free features of return paths, improve out-of-sample forecasts of volatility regimes. In daily United States and developed-market equity returns from 2005 to 2025, machine-learning classifiers do not beat a heterogeneous autoregressive (HAR) regression of realiz...

Yang Lyu, Kiseop Lee · 0 citations

#machine learning Preprint Sep 2026

ReLOBGen: Replayable Limit Order Book Message Generation

ReLOBGen achieves 100% replayability, improves market realism, particularly for top-of-book statistics and the relative prices of LOB messages, and provides a $2.7\text{-}3.6\times$ speedup per replayed message over the LOBS5 baseline.

Junoh Kang, Kiseop Lee, Bohyung Han · 0 citations

Preprint Sep 2026

Neural Networks Learning the Radon--Nikodym Derivative: Empirical Option Pricing in Incomplete Markets

The identifiability of Q from option data is characterized and a measure that is identifiable from data yet prices any claim consistently is proposed, called XiNet, which is a single measure that absorbs available instruments and prices all claims consistently.

Zi-Yuan Zhang, Kiseop Lee · 0 citations