Minimum-variance hedging of a contingent claim in discrete time is suboptimal when the contingent claim is hedged for multiple periods and the objective is to maximize the expected utility of cumulative hedging errors. This is because the hedging errors are not independent. The difference between a minimum-variance hedge and the optimal multiperiod hedge is called the hedging demand and depends on the hedger's preferences, the characteristics of the contingent claim, the trading frequency and horizon, and most importantly the joint distribution of the contingent claim and the underlying security prices. Since modeling this joint distribution is empirically controversial, I examine nonparametrically the economic importance of hedging demands in the case of hedging Standard & Poor's 500 index options.

This paper develops a nonparametric approach to examine how portfolio and consumption choice depends on variables that forecast time‐varying investment opportunities. I estimate single‐period and multiperiod portfolio and consumption rules of an investor with constant relative risk aversion and a one‐month to 20‐year horizon. The investor allocates wealth to the NYSE index and a 30‐day Treasury bill. I find that the portfolio choice varies significantly with the dividend yield, default premium, term premium, and lagged excess return. Furthermore, the optimal decisions depend on the investor's horizon and rebalancing frequency.

Abstract If a nonlinear risk premium in a conditional asset pricing model is approximated with a linear function, as is commonly done in empirical research, the fitted model is misspecified. We use a generic reduced-form model economy with moderate risk premium nonlinearity to examine the size of the resulting misspecification-induced pricing errors. Pricing errors from moderate nonlinearity can be large, and a version of a test for nonlinearity based on risk premiums rather than pricing errors has reasonable power properties after properly controlling for the size of the test. We conclude by examining the importance of moderate nonlinearity in the context of the investment-specific technology shock models of Papanikolaou (2011) and Kogan and Papanikolaou (2014).

We present an econometric method for estimating the parameters of a diffusion model from discretely sampled data. The estimator is transparent, adaptive, and inherits the asymptotic properties of the generally unattainable maximum likelihood estimator. We use this method to estimate a new continuous-time model of the joint dynamics of interest rates in two countries and the exchange rate between the two currencies. The model allows financial markets to be incomplete and specifies the degree of incompleteness as a stochastic process. Our empirical results offer several new insights into the dynamics of exchange rates.

We propose a simple cross-sectional technique to extract daily factors from economic news released at different times and frequencies. Our approach can effectively handle the large number of different announcements that are relevant for tracking current economic conditions. We apply the technique to extract real-time measures of inflation, output, employment, and macroeconomic sentiment, as well as corresponding measures of disagreement among economists about these indices. We find that our procedure provides more timely and accurate forecasts of future changes in economic conditions than other real-time forecasting approaches.

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP–Compustat data set, exploiting the size, value, and momentum anomalies.

We present evidence on the term structure of the equity premium. We recover prices of dividend strips, which are short-term assets that pay dividends on the stock index every period up to period T and nothing thereafter. It is short-term relative to the index because the index pays dividends in perpetuity. We find that expected returns, Sharpe ratios, and volatilities on short-term assets are higher than on the index, while their CAPM betas are below one. Short-term assets are more volatile than their realizations, leading to excess volatility and return predictability. Our findings are inconsistent with many leading theories.

We model the conditional mean and volatility of stock returns as a latent VAR process to study their contemporaneous and intertemporal relationships in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments leading to pronounced countercyclical variation in the Sharpe ratio. We document significant lead-lag correlations between the moments that also appear related to business cycles. Finally, we show that although the conditional correlation between the mean and volatility is negative, the unconditional correlation is positive due to these lead-lag correlations.

Investors rebalance their portfolios as their views about expected returns and risk change. We use empirical measures of portfolio rebalancing to back out investors' views, specifically, their views about the state of the economy. We show that aggregate portfolio rebalancing across equity sectors is consistent with sector rotation, an investment strategy that exploits perceived differences in the relative performance of sectors at different stages of the business cycle. The empirical footprint of sector rotation has predictive power for the evolution of the economy and future bond market returns, even after controlling for relative sector returns. Contrary to many theories of price formation, trading activity, therefore, contains information that is not entirely revealed by resulting relative price changes. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

Campbell, Lettau, Malkiel, and Xu (2001) document a positive trend in idiosyncratic volatility during the 1962–1997 period. We show that by 2003 volatility falls back to pre-1990s levels. Furthermore, we show that the increase and subsequent reversal is concentrated among firms with low stock prices and high retail ownership. This evidence suggests that the increase in idiosyncratic volatility through the 1990s was not a time trend but, rather, an episodic phenomenon, at least partially associated with retail investors. Results from cross-sectional regressions, conditional trend estimation, stock-split events, and “attention-grabbing” events are consistent with a retail trading effect.