We model consumption and dividend growth rates as containing (1) a small long‐run predictable component, and (2) fluctuating economic uncertainty (consumption volatility). These dynamics, for which we provide empirical support, in conjunction with Epstein and Zin's (1989) preferences, can explain key asset markets phenomena. In our economy, financial markets dislike economic uncertainty and better long‐run growth prospects raise equity prices. The model can justify the equity premium, the risk‐free rate, and the volatility of the market return, risk‐free rate, and the price–dividend ratio. As in the data, dividend yields predict returns and the volatility of returns is time‐varying.

Long-Run Risk (LRR) model which emphasizes the role of long-run risks, that is, low-frequency movements in consumption growth rates and volatility, in accounting for a wide-range of asset pricing puzzles. In this article we present a generalized LRR model, which allows us to study the role of cyclical fluctuations and macroeconomic-crisis on asset prices and expected returns. The Bansal and Yaron (2004) LRR model contains (i) a persistent expected consumption growth component, (ii) long-run variation in consumption volatility, and (iii) preference for early resolution of uncertainty. To evaluate the role of cyclical risks, we incorporate a cyclical component in consumption growth — this component is stationary in levels. To study financial market crisis, we also entertain jumps in consumption growth and consumptionvolatility. We find that the magnitude of risk compensation for cyclical risks in consumption critically depends on the magnitude of the inter-temporal elasticity of substitution (IES). When the IES is larger than one, cyclical risks carry a very small risk-premium and the compensation for long-run risks is large. When IES is close to zero, the risk compensation for cyclical risks is large, however, in this case the risk-free rate is implausibly high (in excess of 10 percent). Given this, it seems unlikely that the compensation for cyclical risk is of economically significant magnitude. This implication is also consistent with Robert E. Jr. Lucas (1987), who argues that economic costs of transient shocks are small and those for trend shocks are large. Moreover, Ravi Bansal, Robert F. Dittmar and Dana Kiku (2009) provide evidence from equity markets that the compensation for long-run risks is large and that for cyclical risk is quite small.

Asset price movements in many cases seem de-linked from aggregate economic fundamentals. Forexample, RaviBansal andIvanShaliastovich (2008a) show that frequent large moves in asset prices, i.e. jumps, on average are not correlated with movements in macro-variables (see Table 1 below). Motivated by this, we present a general equilibrium model in which variation in investor confidence about expected growth determines risk premia and hence asset prices. This confidence risk channel can account for (i) the lack of connection between large asset-price moves and macro-variables such as consumption, (ii)large declinesinassetprices, thatis, the left tail of the return distribution, and (iii) observed predictability of equity returns and consumption growth by the price to dividend ratio. In essence, we present a model in which behaviorally motivated shifts in expectations play an important role for the asset prices. Our economy set-up follows a standard longrun risks specification of Ravi Bansal and Amir Yaron (2004), and features Gaussian consumption growth process with time-varying expected growth and volatility; there are no large moves orjumpsintheunderlyingconsumptionanddividenddynamics. Expectedgrowth isnotdirectly observable, and investors learn about it using the cross-section of signals. The time-varying cross-sectional varianceof thesignals determines the quality of the information, and therefore the confidence that investors place in their growth forecast. In the long-run risks framework, the fluctuations in confidence risk determines risk premia and asset prices. We model investors as being recency-biased in their expectation formation, that is, they overweigh recent observations as in Werner De Bondt and Richard Thaler (1990). This is important, as in the standard Kalman-Filter based expectation formation, periods of low information quality get down-weighted, which diminishes the role of the confidence risk channel.

We develop a term structure model where the short interest rate and the market price of risks are subject to discrete regime shifts. Empirical evidence from efficient method of moments estimation provides considerable support for the regime shifts model. Standard models, which include affine specifications with up to three factors, are sharply rejected in the data. Our diagnostics show that only the regime shifts model can account for the well‐documented violations of the expectations hypothesis, the observed conditional volatility, and the conditional correlation across yields. We find that regimes are intimately related to business cycles.