Impacts of trades in an error-correction model of quote prices

Abstract

In this paper we analyze and interpret the quote price dynamics of 100 NYSE stocks stratified by trade frequency. We specify an error-correction model for the log difference of the bid and the ask price with the spread acting as the error-correction term, and include as regressors the characteristics of the trades occurring between quote observations, if any. From this model we are also able to extract the implied model for the spread and the mid-quote. We find that short duration and medium volume trades have the largest impacts on quote prices for all one hundred stocks. Further, we find that buys have a greater impact on the ask price than on the bid price, while sells have a greater impact on the bid price than on the ask price. Both buys and sells increase spreads in the short run, but in the absence of further trades, the spreads mean revert. Trades have a greater impact on quotes for the infrequently traded stocks than for the more actively traded stocks. r

Asymmetries in bid and ask responses to innovations in the trading process

“…Even closer to the purpose of this paper is the independent study by Engle and Patton (2004). These authors (henceforth, EP) also estimate an error correction model for ask and bid quotes using data on a large set of NYSE listed stocks.…”

The impact of macroeconomic news on quote adjustments, noise, and informational volatility

“…Following Engle and Patton (2004), a parameterization of , and , also implies a parameterization of changes in the log spread := − and the log mid-quote := 0.5( + ). Pre-multiplying (1) by the matrix (1 : −1, 0.5 : 0.5) yields the reduced form…”

“…Hasbrouck (1993) proposes decomposing security transaction prices into a random walk component and stationary error components. Engle and Patton (2004) and Escribano and Pascual (2006) extend the framework by Hasbrouck (1991) and propose a vector error correction model for bid and ask quotes with the spread acting as the co-integrating vector. Madhavan, Richardson, and Roomans (1997) introduce a structural model of price formation by decomposing transaction price volatility into volatility arising from news shocks (trade-unrelated information) and volatility arising from market frictions such as price discreteness, asymmetric information, and real frictions.…”

The Impact of Macroeconomic News on Quote Adjustments, Noise, and Informational Volatility

“…Since we model ask and bid log returns we implicitly also model log midquote returns and log changes in the bid-ask spread. The two latter specifications are easily obtained by rotating the model as in Engle and Patton (2004). By interpreting the model as a state space system, the model can be estimated by quasi maximum likelihood using the Kalman filter.…”

“…With respect to the literature on price formation our model builds on the papers by Madhavan, Richardson, and Roomans (1997), Hasbrouck (1993), Engle and Patton (2004) as well as Veredas (2006, 2007). Madhavan, Richardson, and Roomans (1997) use a structural model of price formation to decompose transaction price volatility into volatility arising from news shocks (trade-unrelated information) and volatility arising from market frictions such as price discreteness, asymmetric information, and real frictions.…”