This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5 % annually, adjusted for exposures to the market return as well as size, value, and momentum factors.

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We model a dynamic limit order market as a stochastic sequential game. Since the model is analytically intractable, we provide an algorithm based on Pakes and McGuire (2001) to find a stationary Markov-perfect equilibrium. Given the stationary equilibrium, we generate artificial time series and perform comparative dynamics. As we know the data generating process, we can compare transaction prices to the true value of the asset, as well as explicitly determine the welfare gains accruing to investors.

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High-frequency financial data are not only discretely sampled in time but the time separating successive observations is often random. We analyze the consequences of this dual feature of the data when estimating a continuous-time model. In particular, we measure the additional effects of the randomness of the sampling intervals over and beyond those due to the discreteness of the data. We also examine the effect of simply ignoring the sampling randomness. We find that in many situations the randomness of the sampling has a larger impact than the discreteness of the data.

This paper provides new perspective on the poor performance of exchange rate models by focusing on the information structure of FX trading. I present a new theoretical model of FX trading that emphasizes the role of incomplete and heterogeneous information. The model shows how an equilibrium distribution of FX transaction prices and orders can arise at each point in time from the optimal trading decisions of dealers. This result motivates an empirical investigation of how the equilibrium distribution of FX prices behaves using a new data set that details trading activity in the FX market. This analysis produces two striking results: (i) Much of the observed short-term volatility in exchange rates comes from sampling the heterogeneous trading decisions of dealers in an equilibrium distribution that, under normal market conditions, changes comparatively slowly. (ii) In contrast to the assumptions of traditional macro models, public news is rarely the predominant source of exchange rate movements over any horizon.

This paper proposes a new approach to modeling financial transactions data. A new model for discrete valued time series is proposed in the context of generalized linear models. Since the model is specified conditional on both the previous state, as well as the historic distribution, we call the model the Autoregressive Conditional Multinomial (ACM) model. When the data are viewed as a marked point process, the ACD model proposed in Engle and Russell (1998) allows for joint modeling of the price transition probabilities and the arrival times of the transactions. In this marked point process context, the transition probabilities vary continuously through time and are therefore duration dependent. Finally, variations of the model allow for volume and spreads to impact the conditional distribution of price changes. Impulse response studies show the long run price impact of a transaction can be very sensitive to volume but is less sensitive to the spread and transaction rate.

This paper studies the impact of trading on government bond prices surrounding the release of macroeconomic news. The results show a significant increase in the informational role of trading following economic announcements, which suggests the release of public information increases the level of information asymmetry in the government bond market. The informational role of trading is greater after announcements with a larger initial price impact, and the relation is associated with the surprise component of the announcement and the precision of the public information. The results provide evidence that government bond order flow reveals fundamental information about riskless rates.

This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Cross-market dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bid-ask spreads and depth), returns, volatility, and order flow in the stock and bond markets. We find that a shock to quoted spreads in one market affects the spreads in both markets, and that return volatility is an important driver of liquidity. Innovations to stock and bond market liquidity and volatility prove to be significantly correlated, suggesting that common factors drive liquidity and volatility in both markets. Monetary expansion increases equity market liquidity during periods of financial crises, and unexpected increases (decreases) in the federal funds rate lead to decreases (increases) in liquidity and increases (decreases) in stock and bond volatility. Finally, we find that flows to the stock and government bond sectors play an important role in forecasting stock and bond liquidity. The results establish a link between “macro ” liquidity, or money flows, and “micro ” or transactions liquidity.

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