Ravi Jagannathan

Implications of Security Market Data for Models of Dynamic Economies

We show how to use security market data to restrict the admissible region for means and standard deviations of intertemporal marginal rates of substitution (IMRSs) of consumers. Our approach (i) is nonparametric and applies to a rich class of models of dynamic economies, (ii) characterizes the duality between the mean--standard deviation frontier for IMRSs and the familiear mean- standard deviation frontier for asset returns, and (iii) exploits the restriction that IMRSs are positive random variables. The region provides a convenient summary of the sense in which asset market data are anaomalous from the vantage point of intertemporal asset pricing theory.

Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps

Mean-variance efficient portfolios constructed using sample moments often involve taking extreme long and short positions. Hence practitioners often impose portfolio weight constraints when constructing efficient portfolios. Green and Hollifield (1992) argue that the presence of a single dominant factor in the covariance matrix of returns is why we observe extreme positive and negative weights. If this were the case then imposing the weight constraint should hurt whereas the empirical evidence is often to the contrary. We reconcile this apparent contradiction. We show that constraining portfolio weights to be nonnegative is equivalent to using the sample covariance matrix after reducing its large elements and then form the optimal portfolio without any restrictions on portfolio weights. This shrinkage helps reduce the risk in estimated optimal portfolios even when they have negative weights in the population. Surprisingly, we also find that once the nonnegativity constraint is imposed, minimum variance portfolios constructed using the monthly sample covariance matrix perform as well as those constructed using covariance matrices estimated using factor models, shrinkage estimators, and daily data. When minimizing tracking error is the criterion, using daily data instead of monthly data helps. However, the sample covariance matrix without any correction for microstructure effects performs the best.

Assessing the Market Timing Performance of Managed Portfolios

A number of techniques have been proposed to measure portfolio performance and to distinguish between performance due to forecasting security-specific returns and performance due to forecasting market-wide events. We show theoretically and empirically that it is possible to construct portfolios that show artificial timing ability when no true timing ability exists. In particular, investing in options or levered securities will show spurious market timing. These types of securities will also induce the negative correlation between measured selectivity and timing ability found by others. We suggest specification tests to help distinguish between spurious and true timing ability. In addition, the tests can be used to distinguish between different models of the managers reaction function.

Seasonalities in security returns: The case of earnings announcements

An examination of the behavior of stock returns around quarterly earnings announcement dates finds a seasonal pattern: small firms show large positive abnormal returns and a sizable increase in the variability of returns around these dates. Only part of the large abnormal returns can be accounted for by the fact that firms with good news tend to announce early. Large firms show no abnormal returns around announcement dates and a much smaller increase in variability.

Empirical evaluation of asset-pricing models: A comparison of the SDF and beta methods

The stochastic discount factor (SDF) method provides a unified general framework for econometric analysis of asset pricing models. It has recently been pointed out that the generality of the SDF method may come at the cost of estimation efficiency. We show that there is no need for this concern. The SDF method is as efficient as the classical beta method for estimating risk premia. In addition, the SDF method has an advantage -- the classical beta method, unlike the SDF method, substantially understates the effect of sampling errors when the estimated unanticipated changes in macroeconomic variables are used as pervasive factors.

Call options and the risk of underlying securities

Abstract Merton (1973) in his seminal article ‘Theory of Rational Option Pricing’ showed that the rationally determined price of a call option is a non-decreasing function of the ‘riskness’ of its associated common stock. In deriving his results, Merton made restrictive assumptions about the way the market prices payoff distributions, and used the Rothschild-Stiglitz (1970) measure to compare the riskiness of securities. I show by means of an example that the Merton result will not in general be true. I then derive a sufficient condition for the option on one stock to have higher market value than the option on another stock, when both the stocks have the same price, and explain why the Merton result is valid in the Black-Scholes environment.

Generalized method of moments: Applications in finance

We provide a brief overview of applications of generalized method of moments in finance. The models examined in the empirical finance literature, especially in the asset pricing area, often imply moment conditions that can be used in a straight forward way to estimate the model parameters without making strong assumptions regarding the stochastic properties of variables observed by the econometrician. Typically the number of moment conditions available to the econometrician would exceed the number of model parameters. This gives rise to overidentifying restrictions that can be used to test the validity of the model specifications. These advantages have led to the widespread use of the generalized method of moments in the empirical finance literature.We provide a brief overview of applications of generalized method of moments in finance. The models examined in the empirical finance literature, especially in the asset pricing area, often imply moment conditions that can be used in a straight forward way to estimate the model parameters without making strong assumptions regarding the stochastic properties of variables observed by the econometrician. Typically the number of moment conditions available to the econometrician would exceed the number of model parameters. This gives rise to overidentifying restrictions that can be used to test the validity of the model specifications. These advantages have led to the widespread use of the generalized method of moments in the empirical finance literature.

A Direct Test For The Mean Variance Efficiency Of A Portfolio

We develop a direct test for examining the mean-variance efficiency of a given benchmark asset return. Unlike traditional tests for mean-variance efficiency, this test allows for the possibility that short positions in the primitive assets may not be possible. Using this test, we can not reject the hypothesis that the value weighted return on exchange traded stocks is mean-variance efficient with reference to the mean-variance frontier generated by the 25 stock portfolios constructed by Fama and French (1993), when short selling is not allowed.

Does Product Market Competition Reduce Agency Costs

The folk wisdom is that competition reduces agency costs. We provide indirect empirical support for this view. We argue that the temptation to retain cash and engage in less productive activities is more severe for firms in less competitive industries. Hence an unanticipated increase in cashflow due to higher past returns is more likely to lead to a reduction in leverage as well as a lowering of future returns for firms in less competitive environments. Current leverage will therefore be negatively related to past returns and positively related to future returns for such firms. In contrast, for firms in more competitive industries, the negative relation between past returns and current leverage will be attenuated. Theory suggests that the relation between current leverage and future returns for such firms will be zero or negative. Using a proxy to distinguish firms in less competitive industries, and data for 165 single business firms in the U.S.A., we provide empirical support for our arguments.

Why Do IPO Auctions Fail

We document a somewhat surprising regularity: of the many countries that have used IPO auctions, virtually all have abandoned them. The common explanations given for the lack of popularity of the auction method in the US, viz., issuer reluctance to try a new experimental method, and underwriter pressure towards methods that lead to higher fees, do not fit the evidence. We examine why auctions have failed and verify, to the extent possible, that they are consistent with what academic theory predicts. Both uniform price and discriminatory auctions are plagued by unexpectedly large fluctuations in the number of participants. The free rider problem and the winners curse hamper price discovery and discourage investors from participating in auctions. Calculating the optimal bids in large multi-unit common value auctions with endogenous entry imposes a huge computational burden. With IPOs taking place sporadically, and each firm being different, auctions are likely to end up being unstable.