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.

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.

ABSTRACT We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range‐based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range‐based Gaussian quasi‐maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two‐factor models with one highly persistent factor and one quickly mean‐reverting factor.

Do bond investors demand credit quality or liquidity? The answer is both, but at different<br/>times and for different reasons. Using data on the Euro-area government bond market,<br/>which features a unique negative correlation between credit quality and liquidity across<br/>countries, we show that the bulk of sovereign yield spreads is explained by differences<br/>in credit quality, though liquidity plays a nontrivial role, especially for low credit risk<br/>countries and during times of heightened market uncertainty. In contrast, the destination of<br/>large flows into the bond market is determined almost exclusively by liquidity.We conclude<br/>that credit quality matters for bond valuation but that, in times of market stress, investors<br/>chase liquidity, not credit quality. (JEL G10, G12)

ABSTRACT We present a novel approach to dynamic portfolio selection that is as easy to implement as the static Markowitz paradigm. We expand the set of assets to include mechanically managed portfolios and optimize statically in this extended asset space. We consider “conditional” portfolios, which invest in each asset an amount proportional to conditioning variables, and “timing” portfolios, which invest in each asset for a single period and in the risk‐free asset for all other periods. The static choice of these managed portfolios represents a dynamic strategy that closely approximates the optimal dynamic strategy for horizons up to 5 years.

ABSTRACT We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26% of the day‐to‐day variation in yields on days without major macroeconomic announcements. The effect of orderflow on yields is permanent and strongest when liquidity is low. All of the evidence points toward an important role of price discovery in understanding the behavior of the yield curve.

ABSTRACT We study an institutional investment problem in which a centralized decision maker, the Chief Investment Officer (CIO), for example, employs multiple asset managers to implement investment strategies in separate asset classes. The CIO allocates capital to the managers who, in turn, allocate these funds to the assets in their asset class. This two‐step investment process causes several misalignments of objectives between the CIO and his managers and can lead to large utility costs for the CIO. We focus on (1) loss of diversification, (2) unobservable managerial appetite for risk, and (3) different investment horizons. We derive an optimal unconditional linear performance benchmark and show that this benchmark can be used to better align incentives within the firm. We find that the CIO's uncertainty about the managers' risk appetites increases both the costs of decentralized investment management and the value of an optimally designed benchmark.

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values. Copyright 2005, Oxford University Press.