Printer Friendly

Asset Pricing.

The NBER's Program on Asset Pricing met in Chicago on March 31. Program Director John H. Cochrane and Research Associate Lars P. Hansen, both of the University of Chicago, organized this agenda:

Luca Benzoni, University of Minnesota; Robert S. Goldstein, University of Minnesota and NBER; and Pierre Collin-Dufresne, University of California, Berkeley and NBER, "Can Standard Preferences Explain the Prices of Out-of-the-Money S&P 500 Put Options?"

Discussant: George Constantinides, University of Chicago and NBER

Riccardo Colacito and Mariano M. Croce, New York University, "Risk for the Long Run and the Real Exchange Rate"

Discussant: Adrien Verdelhan, Boston University

Ravi Jagannathan, Northwestern University and NBER; Alexey Malakhov, University of North Carolina; and Dmitry Novikov, Goldman Sachs, "Do Hot Hands Persist Among Hedge Fund Managers ? An Empirical Evaluation"

Discussant: David Hsieh, Duke University

Stavros Panageas and Jiangeng Yu, University of Pennsylvania, "Technological Growth, Asset Pricing, and Consumption Risk Over Long Horizons"

Discussant: Tano Santos, Columbia University and NBER

Torben G. Andersen, Northwestern University and NBER, and Luca Benzoni, "Can Bonds Hedge Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models"

Discussant: Jun Pan, HIT and NBER

Brad Barber and Ning Zhu, University of California, Davis, and Terrance Odean, University of California, Berkeley, "Do Noise Traders Move Markets ?"

Discussant: Sheridan Titman, University of Texas and NBER

Before the stock market crash of 1987, the Black-Scholes model implied that volatilities of S&P 500 index options were relatively constant. Since the crash, though, deep out-of-the money S&P 500 put options have become "expensive" relative to the Black-Scholes benchmark. Many researchers have argued that such prices cannot be justified in a general equilibrium setting if the representative agent has "standard preferences." However, Benzoni, Goldstein, and Collin-Dufresne demonstrate that the "volatility smirk" can be rationalized if the agent is endowed with Epstein-Zin preferences and if the aggregate dividend and consumption processes are driven by a persistent stochastic growth variable that can jump. They identify a realistic calibration of the model that simultaneously matches the empirical properties of dividends, the equity premium, the prices of both at-the-money and deep out-of-the-money puts, and the level of the risk-free rate. A more challenging question (that apparently has not been previously investigated) is whether one can explain within a standard preference framework the stark regime change in the volatility smirk that has existed since the 1987 market crash. To this end, the authors extend their model to a Bayesian setting in which the agents update their beliefs about the average jump size in the event of a jump. Such beliefs only update at crash dates, and hence can explain why the volatility smirk has not diminished over the last 18 years. The authors find that the model can capture the shape of the implied volatility curve both pre- and post-crash while maintaining reasonable estimates for expected returns, price-dividend ratios, and risk-free rates.

Brandt, Cochrane, and Santa-Clara (2004) pointed out that the implicit stochastic discount factors computed using prices, on the one hand, and consumption growth, on the other hand, have very different implications for their cross-country correlation. They leave this as an unresolved puzzle. Colacito and Croce explain it by combining Epstein and Zin (1989) preferences with a model of predictable returns and by positing a very correlated long-run component. They also assume that the intertemporal elasticity of substitution is larger than one. This setup brings the stochastic discount factors computed using prices and quantities close together, by keeping the volatility of the depreciation rate in the order of 12 percent and the cross-country correlation of consumption growth around 30 percent.

Jagannathan, Malakhov, and Novikov empirically demonstrate that both hot and cold hands among hedge fund managers tend to persist. To measure performance, they use statistical model-selection methods for identifying style benchmarks for a given hedge fund, and they allow for the possibility that hedge fund net asset values may be based on stale prices for illiquid assets. They are able to eliminate the backfill bias by deleting all of the backfill observations in their dataset. They also take into account the self-selection bias introduced by the fact that both successful and unsuccessful hedge funds stop reporting information to the database provider. The former stop accepting new money and the latter get liquidated. The authors find statistically as well as economically significant persistence in the performance of funds relative to their style benchmarks. It appears that half of the superior or inferior performance during a three-year interval will spill over into the following three-year interval.

Panageas and Jianfeng develop a theoretical model in order to understand comovements between asset returns and consumption over longer horizons. They develop an intertemporal general equilibrium model featuring two types of shocks: "small," frequent, and disembodied shocks to productivity and "large" technological innovations, which are embodied in new vintages of the capital stock. The latter affect the economy with significant lags, because firms need to make irreversible investments in the new types of capital and there is an option value to waiting. The model produces endogenous cycles, countercyclical variation in risk premia, and only a very modest degree of predictability in consumption and dividend growth as observed in the data. The authors then use their model as a laboratory to show that, in their simulated data, the unconditional consumption Capital Asset Pricing Model performs badly, while its "long-horizon" version performs significantly better.

Andersen and Benzoni investigate whether bonds can hedge volatility risk in the U.S. Treasury market, as predicted by most "affine" term structure models. To this end, they construct powerful and model-free empirical measures of the quadratic yield variation for a cross-section of fixed-maturity zero-coupon bonds ("realized yield volatility") over daily, weekly, and monthly maturities through the use of high-frequency data. They find that the yield curve fails to span yield volatility, as the systematic volatility factors appear largely unrelated to the cross-section of yields. They conclude that a broad class of affine diffusive, quadratic diffusive, and affine jump-diffusive models is incapable of accommodating the observed yield volatility dynamics at daily, weekly, and monthly horizons. Hence, yield volatility risk per se cannot be hedged by taking positions in the Treasury bond market. The authors also advocate using these empirical yield volatility measures more broadly as a basis for specification testing and (parametric) model selection within the term structure literature.

Barber, Ning, and Odean study the trading behavior of individual investors using the Trade and Quotes (TAQ) and Institute for the Study of Security Markets (ISSM) transaction data for the period 1983 to 2001. They document three results: First, order imbalance based on buyer- and seller-initiated small trades from the TAQ/ISSM data correlates well with the order imbalance based on trades of individual investors from brokerage firm data. This indicates that trade size is a reasonable proxy for the trading of individual investors. Second, order imbalance based on TAQ/ISSM data indicates strong herding by individual investors. Individual investors predominantly contemporaneously buy (sell) the same stocks as each other. Furthermore, they predominantly buy (sell) the same stocks in one week (month) that they did the previous week (month). Third, when measured over one year, the imbalance between purchases and sales of each stock by individual investors forecasts cross-sectional stock returns the next year. Stocks heavily bought by individuals one year underperform stocks heavily sold by 4.4 percentage points in the following year. The spread in returns of stocks bought and stocks sold is greater for small stocks and stocks heavily traded by individual investors. Among stocks heavily traded by individual investors, the spread in returns between stocks bought and stocks sold is 13.5 percentage points the following year. Over shorter periods, such as a week or a month, a different pattern emerges. Stocks heavily bought by individual investors one week earn strong returns in the subsequent week, while stocks heavily sold one week earn poor returns in the subsequent week. This pattern persists for a total of three to four weeks and then reverses for the subsequent several weeks. In addition to examining the ability of small trades to forecast returns, the authors look at the predictive value of large trades. In striking contrast to their small trade results, they find that stocks heavily purchased with large trades one week earn poor returns in the subsequent week, while stocks heavily sold one week earn strong returns in the subsequent week.
COPYRIGHT 2006 National Bureau of Economic Research, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2006 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Bureau News
Publication:NBER Reporter
Geographic Code:1USA
Date:Jun 22, 2006
Previous Article:International Finance and Macroeconomics.
Next Article:Corporate Finance.

Related Articles
Dow Jones Newswires wins bid for Bridge Info. Systems assets.
Asset Pricing.
Asset Pricing.

Terms of use | Copyright © 2017 Farlex, Inc. | Feedback | For webmasters