Measuring and explaining liquidity on an electronic limit order book : Evidence from Reuters D 20002

This paper examines the determination of liquidity on an order-driven FX broking system. Exploiting full order level information from the system we examine aspects of liquidity that have been largely ignored in empirical work, augmenting analysis of spreads with examination of order entry rates and depth measures derived from the entire excess demand/supply curves for currency. Amongst our results, we provide evidence of predictability in the arrival of liquidity supply/demand events. After market buys, for example, limit sell arrival probability is reduced — indicative of high-frequency liquidity shortages. Also, in line with recent theoretical analysis, we demonstrate that in times of concentrated trading activity and high volatility, limit order arrivals are more frequent relative to market order arrivals. However, as these limit orders are at relatively poor prices, at such times depth is reduced. The ability to accurately define, measure and explain financial market liquidity is an issue of paramount importance to academics and market participants alike. However, much of the extant empirical work in this area relies on measures, derived from bid-ask spreads and trading activity, that give only very narrow views of liquidity. The purpose of this paper is to augment existing research by focussing on the determination of aspects of liquidity that have been largely ignored. Specifically, using order level data from a foreign exchange broking system, we empirically analyse various liquidity measures derived from the implied excess demand and supply curves for currency. The task of measuring liquidity is made difficult due to the fact that there is no generally accepted definition of a “liquid market”. An often-quoted definition of such a market is given by Kyle (1985). By his definition, a liquid market should be tight in that trading costs for small quantities (i.e. bid-ask spreads) are small; the market should be deep in that trading costs for large quantities should also be small; finally, the market should be resili nt in that any deviation of the market price from fundamental value should be corrected quickly. Clearly then, if one wished to implement Kyle’s definition empirically one would need to evaluate multiple characteristics of a given market. However, extant empirical analysis rarely does this. Studies carried out by financial market practitioners often rely on simple measures of trading activity to proxy liquidity while academic studies tend to focus solely on spreads. There are several reasons for this focus in the academic literature. First the inventory control and asymmetric information literature developed in the 1970s and 1980s gives clear predictions regarding the determination of bid-ask spreads (Ho and Stoll 1983, Glosten and Milgrom 1985, Easley and O’Hara 1987). Second, estimators of spread components based upon these theories were successfully developed (Roll 1984, Stoll 1989, Huang and Stoll 1997). Last, most microstructure databases (a notable exception being the NYSE TORQ data, see Hasbrouck (1992)) contain no information on orders outside the best bid and ask implying that measurement of depth is impossible. Moreover, Kyle’s definition misses an important element of liquidity – the dynamic be-