Available Publications

• ``Microstructure with Multiple Assets: An Experimental Investigation into Direct and Indirect Dealer Competition,'' (with C. Schnitzlein), Journal of Financial Markets, Vol. 7, No. 2, (February 2004), pp. 117--143.
  • I have thought of the relationship between market microstructure experiments and theory as analogous to the relationship between Monte Carlo and statistics. In this paper, we explore the nature of the equilibrium in dealer markets. Chuck Schnitzlein and I were shocked to find competing dealers making such large profits in a pure-dealer market in our 1997 paper. Since these dealers could not communicate how were they able to collude so effectively? So in this paper, we ask whether the externality of price discovery can explain part of this phenomenon. If I post bid and ask quotes that are inside my competitors' for the same asset, then I bear all of the risk of trading with the insider, and my competitors receive all the benefits of updating their beliefs about the asset's true value. We operationalize this by building a market where dealers compete for order flow in uncorrelated assets.

    We do find that dealers do compete more aggressively when their competitors cannot benefit from their own price discovery.

• ``Empirical Analysis of the Yield Curve: The Information in the Data Viewed Through the Window of Cox, Ingersoll, and Ross,'' (with D. Witte), Journal of Finance, Vol. 57, No. 3, (June, 2002), pp. 1479--1520.
  • While most of our theory is cast in continuous time, much of our empirical analysis does not fully explore the implications of the continuous time models, or the restrictions that the models place on the data. In this paper we do this for the CIR 1-, 2-, and 3-factor models. Arbitrage-free models such as affine term-structure models have the feature of stochastic singularity. In this paper we treat all of the bonds in our sample symmetrically by allowing all to have (independent) pricing errors.

    The question that motivated the Bayesian approach in this paper is what are the sizes of the posterior bands around the yields under the model, to an agent who does not know precisely what the current factor(s) is (are). Under the model, we find these are fairly tight, and the data and the model behave differently in important ways that cannot be explained by parameter and factor uncertainty.

• ``Forecasting Stock Return Variance: Toward an Understanding of Stochastic Implied Volatilities,'' (with B. Lastrapes), Review of Financial Studies, Vol. 6, No. 2, (1993), pp. 293--326
  • This paper looks at the question of forecasting the volatility of individual stocks. There are two ways to look at this paper. First as a classical asset pricing model test. Under the null hypothesis that the option pricing model is ``correct,'' then the the model's forecast of volatility should orthogonalize any information that is available at the time the price is recorded. This is an interesting point because it is otherwise not obvious how we can test an arbitrage model with stochastic singularity. In this vein we reject the Hull and White model of option pricing. Furthermore, the rejection looks as though it can be explained by a volatility risk premium.

    The second way, which has been more common in the literature, is to look at this as a volatility forecasting horserace. We have two interesting applications in this vein. First, we put the implied volatility on the right-hand side in a GARCH specfication. Despite the maturity mis-match, it is generally statistically significant, with a positive coefficient. Second, we conduct out-of-sample tests--comparing RMSE's of forecasts and using encompassing regressions. We provide further evidence that GARCH does fairly poorly for forecast horizons of 90 - 180 calendar days, as it tends to overstate the importance and persistence of recent volatility shocks. The historical variance has a lower RMSE than the implied volatility for 9 of the 10 individual stocks that we analyze.

• ``Temporary Components of Stock Returns: What Do the Data Tell Us?'' (with G. Zhou), Review of Financial Studies, Vol. 9, No. 4, (1996), pp. 1033--1059.

  • The question of whether stock market returns are predictable is a fascinating one, and has naturally been studied from a variety of perspectives. Most of the attention on this question looks at the use of conditioning information such as interest rates and dividend yields to forecast returns. But there was also interest in whether past returns themselves could predict future returns. We decompose returns into white noise and a predictable (autoregressive) process to ask how much predictability there is. The answer: not much. We can't disentangle whether there is an infinitessimally small predictable component, or a large predictable component with virtually no predictability--but both imply no predictability of returns.

    As Chris Sims had pointed out, frequentist tests of unit roots have peculiar properties. This paper provides another example of this fact.

• ``When It's Not the Only Game in Town: The Effect of Bilateral Search on the Quality of a Dealer Market,'' (with C. Schnitzlein), Journal of Finance, Vol. 52, No. 2 (June 1997), pp. 683-712. Paper (on line)
• ``Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects,'' (with W. Lastrapes), Journal of Finance, Vol. 45, No. 1, (March 1990), pp. 221-229. Paper (on line)
• ``Firm Size and Turn-of-the-Year Effects in the OTC/NASDAQ Market,'' (with G. Sanger), Journal of Finance, Vol. 44, No. 5, (December 1989), pp. 1219-1245. Paper (on line)
• This is one of the first papers to look at Nasdaq data. An important feature of the early papers that documented a small-firm effect is that small listed firms are stocks that have performed poorly relative to the market. This is because when firms list on the NY and American stock exchanges, they tend to be median-sized. Thus, using listed stocks only, it is almost impossible to disentangle size and return performance (small stocks are also ``losers''). We find that the small firm effect was as large on Nasdaq--if not even larger--than on the exchanges.

  But we also look at bid-ask spreads and trading activity, and we find surprisingly large spreads for the smallest stocks as well as low trading activity.

• ``The Market Reaction to Stock Splits,'' (with P. Poon), Journal of Finance, Vol. 42, No. 5, (December 1987), pp. 1347-1370. Paper (on line)
• ``The Relevance of the Distributional Form of Common Stock Returns to the Construction of Optimal Portfolios,'' (with G. Frankfurter), Journal of Financial and Quantitative Analysis, Vol. 22, No. 4, (December 1987), pp. 505-511. Paper (on line)
• ``Estimation of Stable-Law Parameters: A Comparative Study,'' (with Vedat Akgiray), Journal of Business and Economic Statistics, Vol. 7, No. 1, (January, 1989), pp. 85-93. Paper (on line at JSTOR)
• ``Persistence in Variance, Structural Change, and the GARCH Model,'' (with Bill Lastrapes), Journal of Business and Economic Statistics, Vol. 8, No. 2, (April, 1990), pp. 225-234. Paper (on line at JSTOR)
• ``Endogenous Trading Volume and Momentum in Stock-Return Volatility,'' (with Bill Lastrapes), Journal of Business and Economic Statistics, Vol. 12, No. 2, (April 1994), pp. 253-260. Paper (on line at JSTOR)
• ''Dividends, Taxes, and Normative Portfolio Theory,'' Journal of Economics and Business, Vol. 42, No. 2, (May 1990), pp. 121-131.
• ``The Pricing of When-Issued Securities,'' (with Jim Wansley), Financial Review, Vol. 24, No. 2, (May, 1989), pp. 183--198.
• ``Market Effects of Changes in the Standard & Poor's 500 Index,'' (with Jim Wansley), Financial Review, Vol. 22, No. 1, (February, 1987), pp. 53--69.
• ``Insignificant Betas and the Efficacy of the Sharpe Diagonal Model for Portfolio Selection,'' (with G. Frankfurter), Decision Sciences, Vol. 21, No. 4, (Fall, 1990), pp. 853--861.
• Estimation and Selection Bias in Mean-Variance Portfolio Selection,'' (with G. Frankfurter), Journal of Financial Research, Vol. 12, No. 2, (Summer, 1989), pp. 173--181.
• ``Stock Selection and Timing--A New Look at Market Efficiency,'' (with G. Frankfurter), Journal of Business Finance & Accounting Vol. 15, No. 3, (Autumn, 1988), pp. 385--400.

Other Material:

• Book Review of Experimental Methods: A Primer, by Daniel Friedman and Shyam Sunder, Journal of Finance, Vol. 50, No. 4 (Sept. 1995), pp.1341-1345. Review (on line).

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