The Journal of Finance

Estimating Portfolio and Consumption Choice: A Conditional Euler Equations Approach

Published: 12/17/2002   |   DOI: 10.1111/0022-1082.00162

Michael W. Brandt

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.

Price Discovery in the U.S. Treasury Market: The Impact of Orderflow and Liquidity on the Yield Curve

Published: 11/27/2005   |   DOI: 10.1111/j.1540-6261.2004.00711.x

MICHAEL W. BRANDT, KENNETH A. KAVAJECZ

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.

Dynamic Portfolio Selection by Augmenting the Asset Space

Published: 09/19/2006   |   DOI: 10.1111/j.1540-6261.2006.01055.x

MICHAEL W. BRANDT, PEDRO SANTA‐CLARA

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.

Variable Selection for Portfolio Choice

Published: 12/17/2002   |   DOI: 10.1111/0022-1082.00369

Yacine AÏT‐SAHALI, Michael W. Brandt

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.

Range‐Based Estimation of Stochastic Volatility Models

Published: 12/17/2002   |   DOI: 10.1111/1540-6261.00454

Sassan Alizadeh, Michael W. Brandt, Francis X. Diebold

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.

Optimal Decentralized Investment Management

Published: 07/19/2008   |   DOI: 10.1111/j.1540-6261.2008.01376.x

JULES H. Van BINSBERGEN, MICHAEL W. BRANDT, RALPH S. J. KOIJEN

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.