ABSTRACT Exact small sample population moments of the standard serial covariance and variance estimators are derived under the assumptions of the Roll bid/ask spread model. Noise explains why serial covariance estimates are often positive in annual samples of daily and weekly returns. Small sample estimator bias partially explains why weekly estimates are more negative than daily estimates. Noise causes the Roll spread estimator to be severely biased by Jensen's inequality. The French‐Roll adjusted variance estimator is unbiased but noisy. Empirical tests confirm the major implications.

Exact small sample population moments of the standard serial covariance and variance estimators are derived under the assumptions of the Roll bid/ask spread models. Noise explains why serial covariance estimates are often positive in annual samples of daily and weekly returns. Small sample estimator bias partially explains why weekly estimates are more negative than daily estimates. Noise causes the Roll spread estimator to be severely biased by Jensen's inequality. The French-Roll adjusted variance estimator is unbiased but noisy. Empirical tests confirm the major implications.

ABSTRACT S&P 500 stock return volatilities are compared to the volatilities of a matched set of stocks, after controlling for cross‐sectional differences in firm attributes known to affect volatility. No significant difference in volatility is observed between 1975 and 1983—before the start of trade in index futures and index options. Since then, S&P 500 stocks have been relatively more volatile. The difference is statistically, but not economically, significant. The relative increase occurs primarily in daily returns and only to a lesser extent in longer interval returns. Other factors besides the start of derivative trade could be responsible for the small increase in volatility.

ABSTRACT Five‐minute changes in the S&P 500 index and futures contract are examined over a ten‐day period surrounding the October 1987 stock market crash. Since nonsynchronous trading problems are severe in these data, new index estimators are derived and used. The estimators use the complete transaction history of all 500 stocks. Nonsynchronous trading explains part of the large absolute futures‐cash basis observed during the crash. The remainder may be due to disintegration of the two markets. Even after adjustment for nonsynchronous trading, the index displays more autocorrelation than does the futures and the futures leads the index.

Lawrence Harris, Predicting Contemporary Volume with Historic Volume at Differential Price Levels: Evidence Supporting the Disposition Effect: Discussion, The Journal of Finance, Vol. 43, No. 3, Papers and Proceedings of the Forty-Seventh Annual Meeting of the American Finance Association, Chicago, Illinois, December 28-30, 1987 (Jul., 1988), pp. 698-699

ABSTRACT Attempts to identify price pressures caused by large transactions may be inconclusive if the transactions convey new information to the market. This problem is addressed in an examination of prices and volume surrounding changes in the composition of the S&P 500. Since these changes cause some investors to adjust their holdings of the affected securities and since it is unlikely that the changes convey information about the future prospects of these securities, they provide an excellent opportunity to study price pressures. The results are consistent with the price‐pressure hypothesis: immediately after an addition is announced, prices increase by more than 3 percent. This increase is nearly fully reversed after 2 weeks.

This paper develops and implements a technique for estimating a model of the bid/ask spread. The spread is decomposed into two components, one due to asymmetric information and one due to inventory costs, specialist monopoly power, and clearing costs. The model is estimated using NYSE common stock transaction prices in the period 1981–1983. Cross-sectional regression analysis is then used to relate time-series estimated spread components to other stock characteristics. The results cannot reject the hypothesis that significant amounts of NYSE common stock spreads are due to asymmetric information.

[Program trading and intraday changes in the S&P 500 Index are correlated. Futures prices and, to a lesser extent, cash prices lead program trades. Index arbitrage trades are followed by an immediate change in the cash index, which ultimately reverses slightly. No reversal follows nonarbitrage trades. The cumulative index changes associated with buy-and-sell trades and with arbitrage and nonarbitrage trades all are similar. Price decompositions suggest that the results are not due to microstructure effects. Program trades in this 1989--1990 sample do not seem to have created major short-term liquidity problems. The results are stable within the sample.]

Lawrence Harris, Joel Hasbrouck, Market vs. Limit Orders: The SuperDOT Evidence on Order Submission Strategy, The Journal of Financial and Quantitative Analysis, Vol. 31, No. 2 (Jun., 1996), pp. 213-231