Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities

TL;DR

This study extends Cox-type limit order book models to Tokyo Stock Exchange data, demonstrating their effectiveness and identifying key factors for improved estimation and calibration frequency.

Contribution

It introduces new covariates and applies Cox-type models to a larger set of stocks in the Japanese market, enhancing model accuracy and practical calibration guidance.

Findings

Cox-type models perform well for Nikkei 225 stocks.

Key covariates improve estimation accuracy.

Frequent calibration of covariates enhances model performance.

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.