Abstract not found

A continuous time econometric modelling framework for multivariate market event (or ‘transactions’) data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning σ-field is updated continuously in time as new information arrives. We introduce the class of generalised Hawkes models which allow the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and we show how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time bivariate point process model of the timing of trades and mid-quote changes is presented for a NYSE stock and the empirical findings are related to the theoretical and empirical market microstructure literature.

Mark Ready, Schmuel Baruch and session participants at the 2002 AFA meetings for helpful comments. The latest version of the paper can be downloaded from

We consider possible instantaneous causality between transaction times and transaction prices in a financial market in a structural setting. Although a large part of the current literature neglects this possible instantaneous causality, we provide moment conditions that identify these effects both statistically and economically. Based on ultra-high frequency data for IBM, we find that about two-thirds of its volatility can be attributed to instantaneous durations. From an empirical point of view, we find that transaction times indeed cause transaction prices and that failure to take this into account may lead to erroneous inference.

We provide a structural approach to identify instantaneous causality effects between durations and stock price volatility. So far, in the literature, instantaneous causality effects have either been excluded or cannot be identified separately from Granger type causality effects. By giving explicit moment conditions for observed returns over (random) duration intervals, we are able to identify an instantaneous causality effect. The documented causality effect has significant impact on inference for tick-by-tick data. We find that instantaneous volatility forecasts for, e.g., IBM stock returns must be decreased by as much as 40 % when not having seen the next quote change before its (conditionally) median time. Also, instantaneous volatilities are found to be much higher than indicated by standard volatility assessment procedures using tick-by-tick data. For IBM, a naive assessment of spot volatility based on observed returns between quote changes would only account for 60 % of the actual volatility. For less liquidly traded stocks at NYSE this effect is even stronger.

models were the ARCH and GARCH models of Engle(1982) and Bollerslev(1986) and then the stochastic volatility models of Taylor(1986) and Harvey, Ruiz and Shepherd(1994). Multivariate GARCH methods were implemented by Bollerslev, Engle and Wooldridge(1988), Bollerslev(1990), and Engle and Kroner(1996). Engle and Gonzales-Rivera(1991) allowed general non-normal errors but a paper by Hansen(1994) is one of the few successful efforts to estimate time varying higher moments of this density. In response to the needs of regulators and risk managers for calculations of Value at Risk, new methods now are being designed to examine the tails of this 2 distribution. It is not clear whether the tails have the same dynamic behavior as the rest of the distribution as would be assumed by GARCH style models. The new models include the Hybrid model of Boudoukh, Richardson and Whitelaw(1998), the CAViaR model of Engle and Manganelli(1999), and extreme value theory estimation of tail shapes as in Embrec

This paper addresses the question of how an informed trader’s propensity to trade on inside information in a given company’s stock varies with firm and industry characteristics. I present a simple model of informed trading in which asset values are derived from imperfectly competitive product markets and private information events occur at individual firms. Privately informed traders decide whether to trade in the stock of the firm at which the information event occurred or in the stock of a competing firm. The model predicts that informed traders may have incentives to make information-based trades in the stocks of competing firms, especially when events occur at firms with large market shares. In the context of 921 quarterly earnings announcements in 136 industries, I use intraday transactions data to empirically test the hypothesis that net order flow and returns in the stocks of nonannouncing competing firms have information content for announcing firms. I find evidence of cross-stock order flow and returns relationships that are consistent with the predictions of the model.

We survey the literature analyzing the price formation and trading process, and the consequences of market organization for price discovery and welfare. We o er a synthesis of the theoretical microfoundations and empirical approaches. Within this framework, we confront adverse selection, inventory costs and market power theories to the evidence on transactions costs and price impact. Building on these results, we proceed to an equilibrium analysis of policy issues. We review the extent to which market frictions can be mitigated by such features of market design as the degree of transparency, the use of call auctions, the pricing grid, and the regulation of competition between liquidity suppliers or exchanges.

In this paper, we compare four months of Reuters EFX high frequency indicative data with D2000-1 inter-dealer transaction data for DEM/USD and GBP/USD. Contrary to previous studies, we find, using various information measures, that the matched tick-by-tick indicative data bear no qualitative difference from the transaction data, and have higher information content. Expanding the system to include order flow, due to its growing importance in exchange rate theory, we find that indicative data has a similar impact on order flow as transaction data. However, order flow has no impact on either price. We would like to thank Martin Evans for making available to us the transaction data and an anonymous referee for useful comments. We would also like to thank the Emerging Markets Group, Cass Business School, in London for financial support. 1

A key feature of many nonlinear time series models is that they allow for the possibility that the model structure experiences changes, depending on, for example, the state of the economy or of the financial market. A common property of these models is that it generally is difficult, if not impossible, to fully understand and interpret the structure of the model by considering the estimated values of the model parameters only. To shed light on the characteristics of a nonlinear model it can then be useful to consider the effects of shocks on the future patterns of a time series variable. Most interest in such impulse response analysis has concentrated on measuring the persistence of shocks, or the magnitude of their (ultimate) effect on the time series variable. Interestingly, far less attention has been given to measuring the speed at which this final effect is attained, that is, how fast shocks are “absorbed ” by a time series. In this paper we develop and implement a framework that can be used to assess the absorption rate of shocks in nonlinear models. The floor-and-ceiling model for output growth of Pesaran and Potter (1997) and a multivariate smooth transition model for income, consumption