[In the presence of nonnormally distributed asset returns, optimal portfolio selection techniques require estimates for variance-covariance parameters, along with estimates for higher-order moments and comoments of the return distribution. This is a formidable challenge that severely exacerbates the dimensionality problem already present with meanvariance analysis. This article extends the existing literature, which has mostly focused on the covariance matrix, by introducing improved estimators for the coskewness and cokurtosis parameters. We find that the use of these enhanced estimates generates a significant improvement in investors' welfare. We also find that it is only when improved estimators are used that portfolio selection with higher-order moments dominates mean-variance analysis from an out-of-sample perspective.]

We derive a closed-form solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the alphas or abnormal returns of risky securities. We show that the hedging component induced by learning about the expected return can be a substantial part of the demand. Using our methodology, we perform an "ex ante" empirical exercise, which shows that the utility gains resulting from optimal allocation are substantial in general, especially for long horizons, and an "ex post" empirical exercise, which shows that analysts' recommendations are not very useful.

In the presence of nonnormally distributed asset returns, optimal portfolio selection techniques require estimates for variance-covariance parameters, along with estimates for higher-order moments and comoments of the return distribution. This is a formidable challenge that severely exacerbates the dimensionality problem already present with mean-variance analysis. This article extends the existing literature, which has mostly focused on the covariance matrix, by introducing improved estimators for the coskewness and cokurtosis parameters. We find that the use of these enhanced estimates generates a significant improvement in investors' welfare. We also find that it is only when improved estimators are used that portfolio selection with higher-order moments dominates mean-variance analysis from an out-of-sample perspective. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

This paper provides a joint quantitative analysis of capital structure decisions (debt versus equity) and debt structure decisions (fixed-rate debt versus floating-rate debt or inflation-linked debt) in a continuous-time setting. We show that optimizing the debt structure has an impact on capital structure decisions, and leads to increases in leverage ratios compared to a pure fixed-rate debt program. We also find that for realistic parameter values, jointly optimizing the debt and capital structures generates a significant increase in firm value with respect to a situation where only the capital structure is optimized.

In this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: It is model free and observable at any frequency. Previous approaches have used monthly model-based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross section of expected returns.

We derive a closed-form solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the alphas or abnormal returns of risky securities. We show that the hedging component induced by learning about the expected return can be a substantial part of the demand. Using our methodology, we perform an “ex ante” empirical exercise, which shows that the utility gains resulting from optimal allocation are substantial in general, especially for long horizons, and an “ex post” empirical exercise, which shows that analysts’ recommendations are not very useful.

Effective risk management in pension funds requires the use of appropriate long-term risk and performance indicators. However, Defined Contribution (DC) pension plans currently rely on short-term metrics that don't align with the retirement income goals of beneficiaries. Against this backdrop, we introduce a funding ratio measure for DC plans defined as the plan assets divided by accrued benefits derived from any given (defined) stream of contributions. The funding ratio's denominator is the present value of the total retirement income achievable if all contributions had been invested in fully amortizing fixed-income portfolios called retirement bonds. We also use the retirement bonds to introduce a class of target-income strategies that can effectively reduce income risk as retirement approaches, but also secure minimum funding ratio levels. These simple asset allocation rules strongly dominate the standard target-date fund strategies routinely used by DC plans, in terms of retirement outcomes for beneficiaries.