Implicit in the prices of traded financial assets are Arrow-Debreu prices or, with continuous states, the state-price density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and derive its asymptotic sampling theory. This estimator provides an arbitrage-free method of pricing new, complex, or illiquid securities while capturing those features of the data that are most relevant from an asset-pricing perspective, e.g., negative skewness and excess kurtosis for asset returns, volatility "smiles" for option prices. We perform Monte Carlo experiments and extract the SPD from actual S&P 500 option prices.

This paper explores the ability of conditional versions of the CAPM and the consumption CAPM—jointly the (C)CAPM—to explain the cross section of average stock returns. Central to our approach is the use of the log consumption–wealth ratio as a conditioning variable. We demonstrate that such conditional models perform far better than unconditional specifications and about as well as the Fama-French three-factor model on portfolios sorted by size and book-to-market characteristics. The conditional consumption CAPM can account for the difference in returns between low-book-to-market and high-bookto-market portfolios and exhibits little evidence of residual size or book-to-market effects. We are grateful to Eugene Fama and Kenneth French for graciously providing the

Central banks have several reasons for extracting information from asset prices. Asset prices may embody more accurate and more up-to-date macroeconomic data than what is currently published or directly available to policy makers. Aberrations in some asset prices may indicate

A relationship exists between aggregate risk-neutral and subjective probability distributions and risk aversion functions. Using a variation of the method developed by Jackwerth and Rubinstein (1996), we estimate risk-neutral probabilities reliably from option prices. Subjective probabilities are estimated from realized returns. This paper then introduces a technique to empirically derive risk aversion functions implied by option prices and realized returns simultaneously. These risk aversion functions dramatically change shapes around the 1987 crash: Precrash, they are positive and decreasing in wealth and thus consistent with standard economic theory. Postcrash, they are partially negative and increasing and irreconcilable with the theory. Overpricing of out-of-the-money puts is the most likely cause. A simulated trading strategy exploiting this overpricing shows excess returns even after accounting for the possibility of further crashes and transaction costs. * Jens Carsten Jackwerth is a visiting assistant professor at the London Business School. For helpful discussions I

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We characterize and measure a long-term risk-return trade-off for the valuation of cash flows exposed to fluctuations in macroeconomic growth. This trade-off features risk prices of cash flows that are realized far into the future but continue to be reflected in asset values. We apply this analysis to claims on aggregate cash flows and to cash flows from value and growth portfolios by imputing values to the long-run dynamic responses of cash flows to macroeconomic shocks. We explore the sensitivity of our results to features of the economic valuation model and of the model cash flow dynamics. I.

: This paper links the term structure to perceptions of monetary policy. Long-horizon forecasts of short rates required by no-arbitrage term structure models are heavily influenced by the endpoints, or limiting conditional forecasts, of the short rate process. Common assumptions that the short rate is mean-reverting or contains a unit root are shown to generate unrealistic yield predictions. Failures occur because these assumptions inadequately account for historical shifts in market perceptions of the policy target for inflation. This paper links endpoint shifts to agent learning about shifts in long-term policy goals. Shifting endpoints in short rate processes significantly improve yield predictions. Keywords: Expectations Hypothesis, changepoints, breakpoints, learning JEL classification: E43 a Federal Reserve Bank of Kansas City, 925 Grand Boulevard, Kansas City MO 64198, USA. b Faculty of Economics and Politics, University of Cambridge, Cambridge CB3 9DD, UK. We are grateful ...

We propose a general equilibrium model with multiple securities in which investors’ risk preferences and expectations of dividend growth are time varying. While time varying risk preferences induce the standard positive relation between the dividend yield and expected returns, time varying expected dividend growth induces a negative relation between them. These offsetting effects reduce the ability of the dividend yield to forecast returns and eliminate its ability to forecast dividend growth, as observed in the data. The model links the predictability of returns to that of dividend growth, suggesting specific changes to standard linear predictive regressions for both. The model’s predictions are con…rmed empirically.

We present a new approach for positioning, pricing, and hedging in incomplete markets that bridges standard arbitrage pricing and expected utility maximization. Our approach for determining whether an investor should undertake a particular position involves specifying a set of probability measures and associated °oors which expected payo®s must exceed in order for the investor to consider the hedged and ¯nanced investment to be acceptable. By assuming that the liquid assets are priced so that each portfolio of assets has negative expected return under at least one measure, we derive a counterpart to the ¯rst fundamental theorem of asset pricing. We also derive a counter-Pricing and Hedging in Incomplete Markets 2 part to the second fundamental theorem, which leads to unique derivative security pricing and hedging even though markets are incomplete. For products that are not spanned by the liquid assets of the economy, we show how our methodology provides more realistic bid-ask spreads.

This paper develops a closed-form option pricing formula for a spot asset whose variance follows a GARCH process. The model allows for correlation between returns of the spot asset and variance and also admits multiple lags in the dynamics of the GARCH process. The single-factor (one-lag) version of this model contains Heston’s (1993) stochastic volatility model as a diffusion limit and therefore unifies the discrete-time GARCH and continuous-time stochastic volatility literature of option pricing. The new model provides the first readily computed option formula for a random volatility model in which current volatility is easily estimated from historical asset prices observed at discrete intervals. Empirical analysis on S&P 500 index options shows the single-factor version of the GARCH model to be a substantial improvement over the Black-Scholes (1973) model. The GARCH model continues to substantially outperform the Black-Scholes model even when the Black-Scholes model is updated every period and uses implied volatilities from option prices, while the parameters of the GARCH model are held constant and volatility is filtered from the history of asset prices. The improvement is due largely to the ability of the GARCH model to describe the correlation of volatility with spot returns. This allows the GARCH model to capture strike-price biases in the Black-Scholes model that give rise to the skew in implied volatilities in the index options market.