Asymmetric Autoregressive Conditional Duration Research Articles

HIGH-FREQUENCY TRANSACTION DATA: A COMPARISON BETWEEN TWO ASYMMETRIC MODELS

This paper compares two asymmetric market microstructure models, namely, the three-state asymmetric autoregressive conditional duration (AACD) model and the activity-direction-size (ADS) model. It is shown that the two models measure different aspects of the same underlying asymmetric nature of market microstructure. It is also shown that by extending the AACD model to include two size variables and adjusting for partial durations, each model’s parameter estimates can be used to estimate the other model’s parameters exactly. Thus, the two asymmetric market microstructure models are equivalent.

Extension and verification of the asymmetric autoregressive conditional duration models

ABSTRACTWe extend the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot [Asymmetric ACD models: Introducing price information in ACD models, Empir. Econ. 28 (2003), pp. 709–731] which allows describing the joint dynamics of financial durations, i.e. times between selected micro-events (trades, price, or volume changes) and the corresponding event categories. The generalized beta distribution of the second kind (GB2) is proposed as the distribution for the error terms in the model. The GB2 distribution allows for flexible non-monotonic shapes of baseline hazard functions, which facilitates a plethora of temporal dependence patterns for event arrival times. We propose the procedures for checking the adequacy of the parametric specification with the help of simulation methods that allow computing the model residuals. Moreover, we show how to assess the goodness of fit of the AACD model using probability integral transforms. The model is used to investigate striking regularities in the manner currency dealers execute their market orders in the interbank foreign exchange spot market.

Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach

We propose to compute the Intraday Value-at-Risk (IVaR) for stocks using real-time transaction data. Tick-by-tick data filtered by price duration are modeled using a two-state asymmetric autoregressive conditional duration (AACD) model, and the IVaR is calculated using Monte Carlo simulation based on the estimated AACD model. Backtesting results for the New York Stock Exchange (NYSE) show that the IVaR calculated using the AACD method outperforms those using the Dionne et al. (2009) and Giot (2005) methods.

Does the Probability of Informed Trading Measure Informed Trading?

Recent research has raised concerns over whether the probability of informed trading model (PIN) is an appropriate proxy of information asymmetry. We investigate PIN and test whether the model can detect illegal insider trading prior to M&A announcements. We then compare the performance of PIN to an alternative proxy, the PIN asymmetric autoregressive conditional duration model (PIN-AACD), to determine if this model offers a better proxy for informed trading. We find that PIN does not measure informed trading prior to M&A announcements, and that PIN-AACD offers a better measure of information asymmetry.

Distribution Choice for the Asymmetric ACD Models

In the paper, I generalize the Asymmetric Autoregressive Conditional Duration (AACD) model proposed by Bauwens and Giot (2003) with respect to the generalized gamma and the Burr distribution for an error term. I derive the log likelihood functions for the augmented models and show how to check the goodness-of-fit of the distributional assumptions with the application of the probability integral transforms proposed by Diebold, Gunther and Tay (1998).Moreover, I present an exemplary empirical application of the Asymmetric ACD model for the durations between submissions of market or best limit orders on the interbank trading platform for the Polish zloty. I test the impact of selected market microstructure factors (i.e. the bid-ask spread, volatility) on the time of order submissions.

The impact of transaction duration, volume and direction on price dynamics and volatility

We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading.

Modeling Informed State in Fixed Income Market: Empirical Evidence from HFT Data

Motivated by findings in the existing literature on the information content of order flows, we specify Probability of Informed Trading – Asymmetric Autoregressive Conditional Duration (PIN-AACD) model to identify the ‘informed state’ in the Fixed Income Markets. Using tick-by-tick price and order flow data, we obtain the estimates for the probability of informed (PIN) state throughout the trading day. The trade direction (buy versus sell orders) and the duration between trades are modeled jointly. The model allows the Probabilities of Good News and Bad News to vary each interval. Results show that intraday patterns of the PIN estimates are consistent with the scheduled arrival of public information or news. Further, we show that PIN is positively related to overall market depth. The effect of realized volatility on information asymmetry is unexpectedly found to be negative. We also find positive correlation between news and trading volume. The study intends to support trading and sales teams in identifying informed trades.

Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading

This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara (2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley--Hvidkjaer--O'Hara framework by allowing the probabilities of good news and bad news to vary each day. Our PIN estimates can be computed daily as well as over intraday intervals. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

Forecasting using high‐frequency data: a comparison of asymmetric financial duration models

AbstractThe first purpose of this paper is to assess the short‐run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomial–Autoregressive Conditional Duration (ACM‐ACD) model is better than the Asymmetric Autoregressive Conditional Duration (AACD) model. However, the ACM‐ACD model is more complex in terms of the computational setting and is more sensitive to starting values. The second purpose is to examine the effects of market microstructure on the forecasting performance of the two models. The results indicate that the forecast performance of the models generally decreases as the liquidity of the stock increases, with the exception of the most liquid stocks. Furthermore, a simple filter of the raw data improves the performance of both models. Finally, the results suggest that both models capture the characteristics of the micro data very well with a minimum sample length of 20 days. Copyright © 2008 John Wiley & Sons, Ltd.

The submission of limit orders or market orders: The role of timing and information in the Reuters D2000-2 system

Recent work in the market microstructure literature suggests that the speed with which orders arrive in the market impacts traders' order submission decisions. In this study we use an asymmetric autoregressive conditional duration (ACD) model to empirically investigate the influence on the submission of limit and market orders of changes in the time between the past submissions of different types of orders, changes in the slope of the limit order book, and changes in price uncertainty. We find that the expected time between the arrivals of successive orders in the foreign exchange market depends on the previous type of order submitted and the slope on both sides of the order book. Price uncertainty (volatility) plays a secondary role after accounting for the impact of changes in the slope of the order book. Lastly, we find that there are fundamental changes in the level of information contained in the submission of orders at the opening and closing of markets.

Asymmetric ACD models: Introducing price information in ACD models

This paper proposes a class of asymmetric Autoregressive Conditional Duration models, which extends the ACD model of Engle and Russell (1997). The asymmetry consists of letting the duration process depend on the state of the price process in the beginning and at the end of each duration. If the price has increased, the parameters of the ACD can differ from what they are if the price has decreased. Thus, the model is also a transition model for the price process, with durations following an ACD process. The logarithmic version of the model is applied to the bid/ask price revision process by the specialist for the IBM stock on the New York Stock Exchange. The empirical evidence in favour of asymmetry is compelling.