A new empirical model for intertemporal capital asset pricing is presented that allows both time-varying risk premia and betas where the latter are identified from the dynamics of the conditional covariance of returns. The model is more successful in explaining the predictable variations in excess returns when the returns on the stock market and corporate bonds are included as risk factors than when the stock market is the single factor. Although changes in the covariance of returns induce variations in the betas, most of the predictable movements in returns are attributed to changes in the risk premia.

A new empirical model for intertemporal capital asset pricing is presented that allows both time‐varying risk premia and betas where the latter are identified from the dynamics of the conditional covariance of returns. The model is more successful in explaining the predictable variations in excess returns when the returns on the stock market and corporate bonds are included as risk factors than when the stock market is the single factor. Although changes in the covariance of returns induce variations in the betas, most of the predictable movements in returns are attributed to changes in the risk premia.

A new empirical model for intertemporal capital asset pricing is presented that allows both time-varying risk premia and betas where the latter are identified from the dynamics of the conditional covariance of returns. The model is more successful in explaining the predictable variations in excess returns when the returns on the stock market and corporate bonds are included as risk factors than when the stock market is the single factor. Although changes in the covariance of returns induce variations in the betas, most of the predictable movements in returns are attributed to changes in the risk premia. Copyright 1994 by American Finance Association.(This abstract was borrowed from another version of this item.)

Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. The authors find evidence of significant bidirectional nonlinear causality between returns and volume. They also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by P. Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, the authors continue to find evidence of nonlinear causality from volume to returns.

Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. We find evidence of significant bidirectional nonlinear causality between returns and volume. We also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by Clark's (1973) latent common‐factor model. After controlling for volatility persistence in returns, we continue to find evidence of nonlinear causality from volume to returns.

The authors develop a measure of public information flow to financial markets and use it to document the patterns of information arrival, with an emphasis on the intraday flows. The measure is the number of news releases by Reuter's News Service per unit of time. The authors find that public information arrival is nonconstant, displaying seasonalities and distinct intraday patterns. Next they relate their measure of public information to aggregate measures of intraday market activity. The authors' results suggest a positive, moderate relationship between public information and trading volume but an insignificant relationship with price volatility.

The authors propose an empirical method that utilizes the conditional density of the state variables to estimate and test a term structure model with known price formulae using data on both discount and coupon bonds. The method is applied to an extension of a two-factor model due to J. C. Cox, J. E. Ingersoll, and S. A. Ross (1985). The authors' results show that estimates based on only bills imply unreasonably large price errors for longer maturities. They reject the original Cox, Ingersoll, and Ross model using a likelihood ratio test and conclude that the extended Cox, Ingersoll, and Ross model also fails to provide a good description of the Treasury market.