Title: Stochastic conditional duration model with intraday seasonality and limit order book information
Authors: Tomoki Toyabe - Keio University (Japan) [presenting]
Teruo Nakatsuma - Keio University (Japan)
Abstract: Intraday financial transactions are irregularly spaced, and their durations exhibit positive autocorrelation and intraday seasonality. In the literature, the former is formulated as a time-dependent duration model such as the stochastic conditional duration (SCD) model while the latter is dealt with by filtering out any cyclical fluctuations in time series of durations with a spline smoothing method before the duration model is estimated. We propose a Bayesian approach to model both autocorrelation and intraday seasonality in durations simultaneously. In our new approach, the autocorrelation structure of durations is captured by the SCD model while the intraday seasonality is approximated with B-spline smoothing. Moreover, in our new approach, it is straightforward to include limit order book information (e.g., bid-ask spread) as a covariate in the SCD model. The resultant model is regarded as a non-linear non-Gaussian state space model, for which a Bayesian approach is suitable. We developed an efficient Markov chain sampling scheme for the posterior analysis of the proposed model and applied it to high-frequency transaction data in the Tokyo stock exchange.