Paper: arXiv 2302.01668

Authors: Shunya Chomei

Abstract

In this paper, we build on the analysis of Muni Toke and Yoshida (2020) and conduct several empirical studies using high-frequency financial data. Muni Toke and Yoshida (2020) showed the consistency and asymptotic behavior of the Cox-type model estimators for relative intensities of orders in the limit order book, and then by using high-frequency trading data for 36 stocks traded on the Paris Stock Exchange, they carry out model selection and trading sign prediction. In this study, we add new covariates and carry out model selection and trading sign prediction using high-frequency trading data for 222 stocks traded on the Tokyo Stock Exchange. We not only show that the Cox-type model performs well in the Japanese market as well as in the Euronext Paris market, but also present the key factors for more accurate estimation. We also suggest how often the covariates should be calibrated.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a solid empirical extension of Cox-type intensity models to a large Japanese equity dataset (222 stocks). While it builds heavily on the existing methodology of Muni Toke and Yoshida (2020), its rigorous high-frequency empirical testing, addition of new covariates, and calibration analysis make it valuable for limit order book modeling.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate Cox-type Intensity Model & New Covariates on TSE] --> B[Data Input: High-frequency data for 222 Nikkei 225 stocks]
    B --> C[Methodology: Add new covariates & calibrate update frequencies]
    C --> D[Computational Process: Model selection & Order intensity estimation]
    D --> E[Evaluation: Trading sign prediction & Performance analysis]
    E --> F[Outcomes: Validated model on TSE, identified key accuracy factors & optimal calibration frequency]