Robust general equilibrium under stochastic volatility model

This paper studies the implications of model uncertainty under stochastic volatility model for equilibrium asset pricing. We derive the equilibrium equity premium and risk-free rate in a pure-exchange economy with one representative agent who is averse not only to risk but also to model uncertainty. The results show that robustness increases the equilibrium equity premium while lowers the risk-free rate.     

Stock Market Volatility and Learning

We show that consumption‐based asset pricing models with time‐separable preferences generate realistic amounts of stock price volatility if one allows for small deviations from rational expectations. Rational investors with subjective beliefs about price behavior optimally learn from past price observations. This imparts momentum and mean reversion into stock prices. The model quantitatively accounts for the volatility of returns, the volatility and persistence of the price‐dividend ratio, and the predictability of long‐horizon returns. It passes a formal statistical test for the overall fit of a set of moments provided one excludes the equity premium.     

Hidden persistent disasters and asset prices

This study analyzes the effects of agents’ learning about hidden persistent economic disasters on asset prices. In this study, it is assumed that aggregate consumption follows a hidden Markov regime-switching process and a representative agent infers the current regime, normal regime, or disaster regime, sequentially from the realized path of the past consumption process. In this setting, the fluctuation in the agent’s posterior probabilities of the disaster regime augments the volatility of equity returns. By utilizing the stochastic differential utility, this study demonstrates that the current model can help resolve many asset pricing puzzles including the equity premium puzzle, equity volatility puzzle, and risk-free rate puzzle simultaneously. Further, the current model predicts the counter-cyclical pattern in the equity premium and equity-return volatility on the normal regime, although asset returns are negative and highly volatile during disasters. The study also demonstrates that, if the agent’s preferences are restricted to time-additive power utility, the consideration of hidden persistent disasters deepens the asset pricing puzzles.     

Valuation Risk and Asset Pricing

Standard representative‐agent models fail to account for the weak correlation between stock returns and measurable fundamentals, such as consumption and output growth. This failing, which underlies virtually all modern asset pricing puzzles, arises because these models load all uncertainty onto the supply side of the economy. We propose a simple theory of asset pricing in which demand shocks play a central role. These shocks give rise to valuation risk that allows the model to account for key asset pricing moments, such as the equity premium, the bond term premium, and the weak correlation between stock returns and fundamentals.     

Predictability in Consumption Growth and Equity Returns: A Bayesian Investigation

We use a Bayesian method to estimate a consumption-based asset pricing model featuring long-run risks. Although the model is generally consistent with consumption and dividend growth moments in annual data, the conditional mean of consumption growth (a latent process) is not persistent enough to satisfy the restriction that the price-dividend ratio be an affine function of the latent process. The model also requires relatively high intertemporal elasticity of substitution to match the low volatility of the risk-free return. These two restrictions lead to the equity volatility puzzle. The model accounts for only 50% of the total variation in asset returns.     

Information Quality and Long‐Run Risk: Asset Pricing Implications

I study the asset pricing implications of the quality of public information about persistent productivity shocks in a general equilibrium model with Kreps–Porteus preferences. Low information quality is associated with a high equity premium, a low volatility of consumption growth, and a low volatility of the risk‐free interest rate. The relationship between information quality and the equity premium differs from that in endowment economies. My calibration improves substantially upon the Bansal–Yaron model in terms of the moments of the wealth–consumption ratio and the return on aggregate wealth.     

The pricing of capital assets in a multiperiod world

This paper proposes a general framework for the pricing of capital assets in a multiperiod world. Under quite general conditions, the analysis shows that the equilibrium expected nominal return on any asset can always be expressed as the sum of the risk-free rate and various risk premiums. The first risk premium is identical to the usual risk premium in the Sharpe-Lintner-Mossin capital asset pricing model. The mathematical forms of all the remaining risk premiums are identical even though each individual risk premium may be present for a different reason.     

Habit-based asset pricing with limited participation consumption

We calibrate and estimate a consumption-based asset pricing model with habit formation using limited participation consumption data. Based on survey data of a representative sample of American households, we distinguish between assetholder and non-assetholder consumption, as well as the standard aggregate consumption series commonly used in the CCAPM literature. We show that assetholder consumption outperforms non-assetholder and aggregate consumption data in explaining bond returns, bond yields, and the volatility of bond yields. We further show that the high volatility of assetholder consumption enables the model to explain the equity premium puzzle and the risk-free rate puzzle simultaneously for a reasonable value of relative risk aversion.     

Equity Risk Premium

Abstract

The equity risk premium (ERP) is an essential building block of the market value of risk. In theory, the collective action of all investors results in an equilibrium expectation for the return on the market portfolio excess of the risk-free return, the ERP. The ability of the valuation actuary to choose a sensible value for the ERP, whether as a required input to capital asset pricing model valuation, or any of its descendants, is as important as choosing risk-free rates and risk relatives (betas) to the ERP for the asset at hand.

The historical realized ERP for the stock market appears to be at odds with pricing theory parameters for risk aversion. Since 1985, there has been a constant stream of research, each of which reviews theories of estimating market returns, examines historical data periods, or both. Those ERP value estimates vary widely from about ?1% to about 9%, based on a geometric or arithmetic averaging, short or long horizons, short- or long-run expectations, unconditional or conditional distributions, domestic or international data, data periods, and real or nominal returns.

This paper examines the principal strains of the recent research on the ERP and catalogues the empirical values of the ERP implied by that research. In addition, the paper supplies several time series analyses of the standard Ibbotson Associates 1926–2002 ERP data using short Treasuries for the risk-free rate. Recommendations for ERP values to use in common actuarial valuation problems also are offered.     

Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles

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.