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.

Abstract We examine empirically the response of bond returns and their volatility to good and bad macroeconomic news during expansions and recessions. We find that macroeconomic announcements are most important when they contain bad news for bond returns in expansions and, to a lesser extent, good news in contractions. In expansions, the bond market responds most strongly to bad news in non-farm payrolls, while in recessions good news about inflation is relatively more important. We also document that macroeconomic news impacts the volatility of bond returns at all maturities by increasing jump intensities and altering the jump size distribution.

Abstract We establish an empirical link between the ex-ante uncertainty about macroeconomic fundamentals and the ex-post resolution of this uncertainty in financial markets. We measure macroeconomic uncertainty using prices of economic derivatives and relate this measure to changes in implied volatilities of stock and bond options when the economic data is released. Higher macroeconomic uncertainty is associated with greater reduction in implied volatilities following the news release. It is also associated with increased volume and decreased open interest in option markets after the release, consistent with market participants using financial options to hedge or speculate on macroeconomic news.

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.

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 study asset allocation when the conditional moments of returns are partly predictable. Rather than first model the return distribution and subsequently characterize the portfolio choice, we determine directly the dependence of the optimal portfolio weights on the predictive variables. We combine the predictors into a single index that best captures time variations in investment opportunities. This index helps investors determine which economic variables they should track and, more importantly, in what combination. We consider investors with both expected utility (mean variance and CRRA) and nonexpected utility (ambiguity aversion and prospect theory) objectives and characterize their market timing, horizon effects, and hedging demands.

[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.]