The Journal of Finance

An Investigation of Commodity Futures Prices Using the Consumption‐Based Intertemporal Capital Asset Pricing Model

Published: 03/01/1985   |   DOI: 10.1111/j.1540-6261.1985.tb04943.x

RAVI JAGANNATHAN

In this paper we extend the multigood futures pricing model of Grauer and Litzenberger [9] to a dynamic discrete time setting. We then test the model using data on futures prices for corn, wheat, and soybeans. The parameter estimates we obtain are similar to those obtained by other researchers using stock return data. The model itself is rejected and we offer some suggestions as to which assumption may be violated. We also give an interpretation to the Hansen‐Singleton nonlinear instrumental variables estimation technique used in our empirical work.

A Note on the Asymptotic Covariance in Fama‐MacBeth Regression

Published: 12/17/2002   |   DOI: 10.1111/0022-1082.334095

Ravi Jagannathan, Zhenyu Wang

Empirical Evaluation of Asset‐Pricing Models: A Comparison of the SDF and Beta Methods

Published: 12/17/2002   |   DOI: 10.1111/1540-6261.00498

Ravi Jagannathan, Zhenyu Wang

The stochastic discount factor (SDF) method provides a unified general framework for econometric analysis of asset‐pricing models. There have been concerns that, compared to the classical beta method, the generality of the SDF method comes at the cost of efficiency in parameter estimation and power in specification tests. We establish the correct framework for comparing the two methods and show that the SDF method is as efficient as the beta method for estimating risk premiums. Also, the specification test based on the SDF method is as powerful as the one based on the beta method.

Lazy Investors, Discretionary Consumption, and the Cross‐Section of Stock Returns

Published: 08/14/2007   |   DOI: 10.1111/j.1540-6261.2007.01253.x

RAVI JAGANNATHAN, YONG WANG

When consumption betas of stocks are computed using year‐over‐year consumption growth based upon the fourth quarter, the consumption‐based asset pricing model (CCAPM) explains the cross‐section of stock returns as well as the Fama and French (1993) three‐factor model. The CCAPM's performance deteriorates substantially when consumption growth is measured based upon other quarters. For the CCAPM to hold at any given point in time, investors must make their consumption and investment decisions simultaneously at that point in time. We suspect that this is more likely to happen during the fourth quarter, given investors' tax year ends in December.

The Conditional CAPM and the Cross‐Section of Expected Returns

Published: 03/01/1996   |   DOI: 10.1111/j.1540-6261.1996.tb05201.x

RAVI JAGANNATHAN, ZHENYU WANG

Most empirical studies of the static CAPM assume that betas remain constant over time and that the return on the value‐weighted portfolio of all stocks is a proxy for the return on aggregate wealth. The general consensus is that the static CAPM is unable to explain satisfactorily the cross‐section of average returns on stocks. We assume that the CAPM holds in a conditional sense, i.e., betas and the market risk premium vary over time. We include the return on human capital when measuring the return on aggregate wealth. Our specification performs well in explaining the cross‐section of average returns.

An Asymptotic Theory for Estimating Beta‐Pricing Models Using Cross‐Sectional Regression

Published: 12/17/2002   |   DOI: 10.1111/0022-1082.00053

Ravi Jagannathan, Zhenyu Wang

Without the assumption of conditional homoskedasticity, a general asymptotic distribution theory for the two‐stage cross‐sectional regression method shows that the standard errors produced by the Fama–MacBeth procedure do not necessarily overstate the precision of the risk premium estimates. When factors are misspecified, estimators for risk premiums can be biased, and the t‐value of a premium may converge to infinity in probability even when the true premium is zero. However, when a beta‐pricing model is misspecified, the t‐values for firm characteristics generally converge to infinity in probability, which supports the use of firm characteristics in cross‐sectional regressions for detecting model misspecification.

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

Published: 07/15/2003   |   DOI: 10.1111/1540-6261.00580

Ravi Jagannathan, Tongshu Ma

Green and Hollifield (1992) argue that the presence of a dominant factor would result in extreme negative weights in mean‐variance efficient portfolios even in the absence of estimation errors. In that case, imposing no‐short‐sale constraints should hurt, whereas empirical evidence is often to the contrary. We reconcile this apparent contradiction. We explain why constraining portfolio weights to be nonnegative can reduce the risk in estimated optimal portfolios even when the constraints are wrong. Surprisingly, with no‐short‐sale constraints in place, the sample covariance matrix performs as well as covariance matrix estimates based on factor models, shrinkage estimators, and daily data.

Assessing Specification Errors in Stochastic Discount Factor Models

Published: 04/18/2012   |   DOI: 10.1111/j.1540-6261.1997.tb04813.x

LARS PETER HANSEN, RAVI JAGANNATHAN

In this article we develop alternative ways to compare asset pricing models when it is understood that their implied stochastic discount factors do not price all portfolios correctly. Unlike comparisons based on χ2 statistics associated with null hypotheses that models are correct, our measures of model performance do not reward variability of discount factor proxies. One of our measures is designed to exploit fully the implications of arbitrage‐free pricing of derivative claims. We demonstrate empirically the usefulness of our methods in assessing some alternative stochastic factor models that have been proposed in asset pricing literature.

Dividend Dynamics, Learning, and Expected Stock Index Returns

Published: 10/08/2018   |   DOI: 10.1111/jofi.12731

RAVI JAGANNATHAN, BINYING LIU

We present a latent variable model of dividends that predicts, out‐of‐sample, 39.5% to 41.3% of the variation in annual dividend growth rates between 1975 and 2016. Further, when learning about dividend dynamics is incorporated into a long‐run risks model, the model predicts, out‐of‐sample, 25.3% to 27.1% of the variation in annual stock index returns over the same time horizon, with learning contributing approximately half of the predictability in returns. These findings support the view that investors' aversion to long‐run risks and their learning about these risks are important in determining stock index prices and expected returns.

Adverse Selection in a Model of Real Estate Lending

Published: 06/01/1989   |   DOI: 10.1111/j.1540-6261.1989.tb05069.x

V. V. CHARI, RAVI JAGANNATHAN

We provide a rationale for the presence of points in mortgage loan contracts. Our analysis builds on two key features. First, insurance markets are unavailable for labor income. Second, the “due‐on‐sale” clause allows banks to offer loan contracts which partially insure against fluctuations in labor income. If explicit prepayment penalties are prohibited by law, points serve effectively as prepayment penalties. We also examine environments where such penalties are not prohibited and show that points will be used if interest rates cannot depend on the size of the loan.

A Note on “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps”

Published: 07/02/2019   |   DOI: 10.1111/jofi.12824

RAVI JAGANNATHAN, TONGSHU MA, JIAQI ZHANG

This note corrects an error in the proof of Proposition 2 of “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraint Helps” that appeared in the Journal of Finance, August 2003.

Banking Panics, Information, and Rational Expectations Equilibrium

Published: 07/01/1988   |   DOI: 10.1111/j.1540-6261.1988.tb04606.x

V. V. CHARI, RAVI JAGANNATHAN

This paper shows that bank runs can be modeled as an equilibrium phenomenon. We demonstrate that some aspects of the intuitive “story” that bank runs start with fears of insolvency of banks can be rigorously modeled. If individuals observe long “lines” at the bank, they correctly infer that there is a possibility that the bank is about to fail and precipitate a bank run. However, bank runs occur even when no one has any adverse information. Extra market constraints such as suspension of convertibility can prevent bank runs and result in superior allocations.

Do Hot Hands Exist among Hedge Fund Managers? An Empirical Evaluation

Published: 01/13/2010   |   DOI: 10.1111/j.1540-6261.2009.01528.x

RAVI JAGANNATHAN, ALEXEY MALAKHOV, DMITRY NOVIKOV

In measuring performance persistence, we use hedge fund style benchmarks. This allows us to identify managers with valuable skills, and also to control for option‐like features inherent in returns from hedge fund strategies. We take into account the possibility that reported asset values may be based on stale prices. We develop a statistical model that relates a hedge fund's performance to its decision to liquidate or close in order to infer the performance of a hedge fund that left the database. Although we find significant performance persistence among superior funds, we find little evidence of persistence among inferior funds.

Corrigendum for Dividend Dynamics, Learning, and Expected Stock Index Returns

Published: 05/30/2019   |   DOI: 10.1111/jofi.12786

RAVI JAGANNATHAN, BINYING LIU, JIAQI ZHANG

The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks

Published: 03/02/2005   |   DOI: 10.1111/j.1540-6261.2005.00742.x

JOHN H. BOYD, JIAN HU, RAVI JAGANNATHAN

We find that on average, an announcement of rising unemployment is good news for stocks during economic expansions and bad news during economic contractions. Unemployment news bundles three types of primitive information relevant for valuing stocks: information about future interest rates, the equity risk premium, and corporate earnings and dividends. The nature of the information bundle, and hence the relative importance of the three effects, changes over time depending on the state of the economy. For stocks as a group, information about interest rates dominates during expansions and information about future corporate dividends dominates during contractions.

Economic Significance of Predictable Variations in Stock Index Returns

Published: 12/01/1989   |   DOI: 10.1111/j.1540-6261.1989.tb02649.x

WILLIAM BREEN, LAWRENCE R. GLOSTEN, RAVI JAGANNATHAN

Knowledge of the one‐month interest rate is useful in forecasting the sign as well as the variance of the excess return on stocks. The services of a portfolio manager who makes use of the forecasting model to shift funds between bills and stocks would be worth an annual management fee of 2% of the value of the assets managed. During 1954:4 to 1986:12, the variance of monthly returns on the managed portfolio was about 60% of the variance of the returns on the value weighted index, whereas the average return was two basis points higher.

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks

Published: 12/01/1993   |   DOI: 10.1111/j.1540-6261.1993.tb05128.x

LAWRENCE R. GLOSTEN, RAVI JAGANNATHAN, DAVID E. RUNKLE

We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH‐M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH‐M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility.