Bias correcting transaction cost models for characteristic function based estimators

Paper: arXiv 2610.25445

Authors: Abhik Rana, Sarvesh Ravichandran Iyer, Sandeep Juneja

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.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a mathematically sophisticated approach to transaction cost estimation using characteristic functions, offering a novel bias correction. It includes numerical experiments to validate the method against existing models, demonstrating strong empirical considerations. The theoretical derivations are rigorous, and the proposed method addresses known limitations of established models.

Research Flowchart

  flowchart TD
    A[Research Goal: Bias Correct Transaction Cost Models] --> B{Existing Models Limitations};
    B -- Roll's Model --> B1[Fails with positive autocovariance];
    B -- Hasbrouck's Model --> B2[Computationally intensive];
    B --> C{Proposed Methodology: Empirical Characteristic Function with Bias Correction};
    C --> D[Data: Daily Transaction Prices];
    D --> E[Computational Process: Spread Estimation & Asymptotic Distribution Derivation];
    E --> F[Outcomes: Bias-variance tradeoff analysis & Computational advantage];

Cited References (27)

1. Bid‐Ask Spread Estimators: Current State, Gaps, and Future Research Agendas (2026) — link
2. Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices (2024) — link
3. When Do Low-Frequency Measures Really Measure Effective Spreads? Evidence from Equity and Foreign Exchange Markets (2023) — link
4. High-Dimensional Probability: An Introduction with Applications in Data Science (2020) — link
5. Asymptotic comparison of three spread estimators based on Roll’s model (2019) — link
6. Bid–ask spread estimator from high and low daily prices: Practical implementation for corporate bonds (2018) — link
7. A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices (2017) — link
8. The Best in Town: A Comparative Analysis of Low-Frequency Liquidity Estimators (2017) — link

0 of 27 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.