Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information

Tomoki Toyabe, Teruo Nakatsuma

Research output: Contribution to journalArticlepeer-review

Abstract

It is a widely known fact that the intraday seasonality of trading intervals for financial transactions such as stocks is short at the beginning of business hours and long in the middle of the day. In this paper, we extend the stochastic conditional duration (SCD) model to capture the pattern of intraday trading intervals and propose a new Markov chain Monte Carlo method to estimate this intraday seasonality simultaneously. To efficiently generate the Monte Carlo sample, we used a hybrid of the Gibbs/Metropolis–Hastings (MH) sampling scheme and also applied generalized Gibbs sampling. In addition to capturing this intraday seasonality, this paper also considers limit order book information. Three-day tick data for three stocks obtained from Nikkei NEEDS are used for estimation, and model selection is performed on smooth parameters, Weibull distribution and Gamma distribution. The typical intraday regularity of frequent trading immediately after the start of trading is confirmed, and the spread of the limit order book information is also found to affect the trading time interval.

Original languageEnglish
Article number470
JournalJournal of Risk and Financial Management
Volume15
Issue number10
DOIs
Publication statusPublished - 2022 Oct

Keywords

  • Bayesian inference
  • block sampler
  • Markov chain Monte Carlo
  • Metropolis–Hastings algorithm
  • state space model

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics
  • Accounting
  • Business, Management and Accounting (miscellaneous)

Fingerprint

Dive into the research topics of 'Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information'. Together they form a unique fingerprint.

Cite this