This paper proposes a dynamic model of bid and ask quotes that incorporates a stochastic cost of market-making, discreteness (restriction of quotes to a fixed grid) and clustering (the tendency of quotes to lie on `natural' multiples of the tick size). The Gibbs sampler provides a convenient vehicle for estimation. The model is estimated for daily and intradaily US Dollar/Deutschemark Reuters quotes.

Motivated by economic models of sequential trade, empirical analyses of market dynamics frequently estimate liquidity as the coefficient of signed order flow in a price change regression. This paper implements such an analysis for futures transaction data from pit trading. To deal with the absence of timely bid and ask quotes (which are used to sign trades in most equity market studies), this paper proposes new techniques based on Markov chain Monte Carlo estimation. The model is estimated for four representative Chicago Mercantile Exchange contracts. The highest liquidity (lowest order flow coefficient) is found for the S&P 500 index. Liquidity for the Euro and U.K. £ contracts is somewhat lower. The pork belly contract exhibits the least liquidity.

This paper is an econometric analysis of the information content of automated orders arriving at the NYSE. The model captures the joint behavior of automated orders and also the return on the stock index future and the futures-spot basis. The results indicate that orders contain information useful in predicting stock returns beyond the information contained in the reported trades. Furthermore, program and index-arbitrage orders contain information beyond that available from the futures return and basis, suggesting that these orders are not merely passive conveyors of common-factor information. Nonprogram, program, and index-arbitrage orders have roughly similar price impacts.

The market for U.S. equity indexes presently comprises floor‐traded index futures contracts, exchange‐traded funds (ETFs), electronically traded, small‐denomination futures contracts (E‐minis), and sector ETFs that decompose the S&P 500 index into component industry portfolios. This paper empirically investigates price discovery in this environment. For the S&P 500 and Nasdaq‐100 indexes, most of the price discovery occurs in the E‐mini market. For the S&P 400 MidCap index, price discovery is shared between the regular futures contract and the ETF. The S&P 500 ETF contributes markedly to price discovery in the sector ETFs, but there are only minor effects in the reverse direction.