Bias correcting transaction cost models for characteristic function based estimators

By Abhik Rana, Sarvesh Ravichandran Iyer, Sandeep Juneja

Rating

1740
Battle Count: 50

Relevance

8/10
Highly relevant for practitioners needing efficient transaction cost estimates without quote data. Offers a significant computational advantage over Bayesian methods (Hasbrouck) while addressing the inadmissibility issues of Roll's model.

Implementation Complexity

6/10
Requires implementing characteristic function estimators, solving for bias-neutral frequencies, and performing pilot selection. More complex than Roll's simple covariance calculation but significantly simpler than Gibbs sampling for Hasbrouck's model.

Reproducibility

3/5
The paper provides detailed mathematical derivations, algorithm pseudocode (Algorithm 1), and specifies the simulation parameters (1,000 paths, 252 days, c=0.02). However, no public code repository URL is provided in the text. Data for the Indian equities experiment is from CMIE Prowess, which is proprietary.

Abstract

Effective transaction cost is a necessary step for devising any competing financial trading strategy in the absence of quote data. Classically, Roll's model estimated the transaction cost using the first order autocovariance of the difference between daily transaction prices, while Hasbrouck's Bayesian approach used iterative posterior simulation. However, Roll's model fails when the first order autocovariance becomes positive, while Hasbrouck's estimator is computationally intensive, especially when many securities are involved. We develop empirical characteristic function methods with a first order bias correcting for spread estimation, and establish the asymptotic distribution of the corrected estimator under suitable conditions. Our numerical experiments illustrate the bias-variance tradeoff of characteristic-function methods compared to Roll's model, and their computational advantage over Hasbrouck's model.

Links

arXiv:2610.25445 ยท Paper rankings

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