Researchers propose a characteristic-function spread estimator with first-order bias correction
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Abhik Rana, Sarvesh Ravichandran Iyer, and Sandeep Juneja released a working paper proposing an empirical characteristic-function method with first-order bias correction for estimating transaction-cost spreads when quote data are unavailable, and established the asymptotic distribution of the corrected estimator under suitable conditions. The method addresses limitations of existing models: the Roll model fails when the first-order autocovariance is positive, while the Hasbrouck estimator is computationally expensive for large universes of securities. Numerical experiments show a bias–variance trade-off relative to the Roll model and computational efficiency relative to the Hasbrouck model.
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- arXiv q-finBias Correction of Transaction Cost Models for Characteristic Function-Based Estimators
To address the need for transaction cost estimation in scenarios without quote data, researchers developed an empirical characteristic function method with first-order bias correction for spread estimation, and established the asymptotic distribution of the corrected estimator under appropriate conditions. Numerical experiments show that this method has a bias-variance trade-off compared to the Roll model, and offers computational efficiency advantages over the Hasbrouck model.
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