https://w4.stern.nyu.edu/finance/docs/WP/2000/pdf/wpa00041.pdf

Illiquidity and stock returns: cross-section and time-series effects $

New tests are presented on the effects of stock illiquidity on stock return. Over time, expected market illiquidity positively affects ex ante stock excess return (usually called â¬Srisk premiumâ¬?). This complements the positive cross-sectional return-illiquidity relationship. The illiquidity measure here is the average daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firms stocks, suggesting an explanation for the changes â¬Ssmall firm effectâ¬? over time. The impact of market illiquidity on stock excess return suggests the existence of illiquidity premium and helps explain the equity premium puzzle.

Illiquidity and Stock Returns: Cross-Section and Time-Series Effects

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

This study investigates whether marketwide 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. From 1966 through 1999, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5 percent annually, adjusted for exposures to the market return as well as size, value, and momentum factors. Furthermore, a liquidity risk factor accounts for half of the profits to a momentum strategy over the same 34-year period.

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Liquidity Risk and Expected Stock Returns

We provide a model that links an asset's market liquidity - i.e., the ease with which it is traded - and traders' funding liquidity - i.e., the ease with which they can obtain funding. Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and the margins they are charged, depend on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to “flight to quality¶, and (v) comoves with the market, and it provides new testable predictions. Keywords: Liquidity Risk Management, Liquidity, Liquidation, Systemic Risk, Leverage, Margins, Haircuts, Value-at-Risk, Counterparty Credit Risk

Market liquidity and funding liquidity

This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE issues suggest: a trade's full price impact arrives only with a protracted lag; the impact is a positive and concave function of the trade size; large trades cause the spread to widen; trades occurring in the face of wide spreads have larger price impacts; and, information asymmetries are more significant for smaller firms. CENTRAL TO THE ANALYSIS of market microstructure is the notion that in a market with asymmetrically informed agents, trades convey information and therefore cause a persistent impact on the security price. The magnitude of the price effect for a given trade size is generally held to be a positive function of the proportion of potentially informed traders in the population, the probability that such a trader is in fact informed (i.e., the probability that a private information signal has in fact been observed), and the precision of the private information. The close dependence of the price impact on these factors, which may be referred to as the extent of the information asymmetry, provides a strong motivation for the empirical determination of this impact. This paper strives to achieve such a determination in a framework that is robust to deviations from the assumptions of the formal models. In the process, the framework establishes a rich characterization of the dynamics by which trades and quotes interact. The market considered here is a specialist market in which a designated market-maker exposes bid and ask quotes to the trading public. An extensive theory has evolved that analyzes the market-maker's exposure to traders with superior information.' Concerning the extent of the information asymmetry, this body of theory yields two important empirical predictions: first,

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Measuring the Information Content of Stock Trades

When homogeneous or closely-linked securities trade in multiple markets, it is often of interest to determine where price discovery (the incorporation of new information) occurs. This article suggests an econometric approach based on an implicit unobservable efficient price common to all markets. The information share associated with a particular market is defined as the proportional contribution of that market's innovations to the innovation in the common efficient price. Applied to quotes for the thirty Dow stocks, the technique suggests that the preponderance of the price discovery takes place at the New York Stock Exchange (NYSE) (a median 92.7 percent information share). ALTHOUGH MOST OF THE CLASSIC paradigms in market microstructure concern a security that trades in a single centralized market, such situations are becoming increasingly rare in practice. Fragmentation, the dispersal of trading in a security to multiple sites, has emerged as a dominant institutional trend. This process is of concern to financial economists and regulators because price information and price discovery (the impounding of new information into the security price), arguably the most important products of a security market, have many attributes of public goods. As the process of fragmentation accelerates, it may be important to determine where the price information and price discovery are being produced.1 This paper suggests a practical econometric approach to this problem. The technique is illustrated with an application to U.S. equity markets. Many stocks listed on the New York Stock Exchange (NYSE) trade concurrently on the regional exchanges and the National Association of Securities Dealers' National Market System (NASD NMS). Since a share of IBM stock is the same security whether purchased on the Midwest or Pacific Exchange, this is a particularly clear instance of multiple markets. Nevertheless, the expres

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One Security, Many Markets: Determining the Contributions to Price Discovery

The effective cost of trading is usually estimated from transaction-level data. This study proposes a Gibbs estimate that is based on daily closing prices. In a validation sample, the daily Gibbs estimate achieves a correlation of 0.965 with the transactionlevel estimate. When the Gibbs estimates are incorporated into asset pricing specifications over a long historical sample (1926 to 2006), the results suggest that effective cost (as a characteristic) is positively related to stock returns. The relation is strongest in January, but it appears to be distinct from size effects. INVESTIGATIONS INTO THE ROLE of liquidity and transaction costs in asset pricing must generally confront the fact that while many asset pricing tests make use of U.S. equity returns from 1926 onward, the high-frequency data used to estimate trading costs are usually not available prior to 1983. Accordingly, most studies either limit the sample to the post-1983 period of common coverage or use the longer historical sample with liquidity proxies estimated from daily data. This paper falls into the latter group. Specifically, I propose a new approach to estimating the effective cost of trading and the common variation in this cost. These estimates are then used in conventional asset pricing specifications with a view to ascertaining the role of trading costs as a characteristic in explaining

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Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data

How important are cross-stock common factors in the price discovery/liquidity provision process in equity markets? We investigate two aspects of this question for the thirty Dow stocks. First, using principal components and canonical correlation analyses we find that both returns and order flows are characterized by common factors. Commonality in the order flows explains roughly half of the commonality in returns. Second, we examine variation and common covariation in various liquidity proxies and market depth (trade impact) coefficients. Liquidity proxies such as the bid-ask spread and bid-ask quote sizes exhibit time variation which helps explain time variation in trade impacts. The common factors in these liquidity proxies are relatively small, however.

Common Factors in Prices, Order Flows and Liquidity

http://w4.stern.nyu.edu/finance/docs/WP/1999/pdf/wpa99011.pdf

Joel Hasbrouck

Papers

Measuring the Information Content of Stock Trades

One Security, Many Markets: Determining the Contributions to Price Discovery

Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data

Common Factors in Prices, Order Flows and Liquidity

Common factors in prices, order flows, and liquidity