Stochastic Conditional Duration Models with "Leverage Effect" for Financial Transaction Data

Abstract

This article proposes stochastic conditional duration (SCD) models with "leverage effect" for financial transaction data, which extends both the autoregressive conditional duration (ACD) model (Engle and Russell, 1998, Econometrica, 66, 1127-1162) and the existing SCD model (Bauwens and Veredas, 2004, Journal of Econometrics, 119, 381-412). The proposed models belong to a class of linear nongaussian state-space models, where the observation equation for the duration process takes an additive form of a latent process and a noise term. The latent process is driven by an autoregressive component to characterize the transition property and a term associated with the observed duration. The inclusion of such a term allows the model to capture the asymmetric behavior or "leverage effect" of the expected duration. The Monte Carlo maximum-likelihood (MCML) method is employed for consistent and efficient parameter estimation with applications to the transaction data of IBM and other stocks. Our analysis suggests that trade intensity is correlated with stock return volatility and modeling the duration process with "leverage effect" can enhance the forecasting performance of intraday volatility.

Keywords: autoregressive conditional duration (ACD) model, ergodicity, financial transaction data, leverage effect, Monte Carlo maximum-likelihood (MCML) estimation, stationarity, stochastic conditional duration (SCD) model