We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller...

We study whether investors can exploit stock return serial dependence to improve out-of- sample portfolio performance. To do this, we first show that a vector-autoregressive (VAR) model estimated with ridge regression captures daily stock return serial dependence in a stable manner. Second, we...

Shrinkage estimators is an area widely studied in statistics. In this paper, we contemplate the role of shrinkage estimators on the construction of the investor's portfolio. We study the performance of shrinking the sample moments to estimate portfolio weights as well as the performance of...

We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters—the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators...

Our objective in this paper is to examine whether one can use option-implied information to improve mean-variance portfolio selection with a large number of stocks, and to document which aspects of option-implied information are most useful for improving the out-of-sample performance of...

In this paper, we compare the out-of-sample performance of the rule allocating 1/N to each of the N available assets to several static and dynamic models of optimal asset-allocation for ten datasets. We devote particular attention to models the literature has proposed to account for estimation...

We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1-N rule in terms of...

Computing the optimal portfolio policy of an investor facing capital gains tax is a challenging problem: because the tax to be paid depends on the price at which the security was purchased (the tax basis), the optimal policy is path dependent and the size of the problem grows exponentially with...

In this paper, we focus on the estimation of a high-dimensional precision matrix. We propose a simple improvement of the graphical lasso framework (glasso) that is able to attain better statistical performance without sacrificing too much the computational cost. The proposed improvement is based...