The tracking error is a ubiquitous tool among active and passive portfolio managers, used widely for fund selection, risk management, and manager compensation. In this paper we show that traditional measures of tracking error are incapable of detecting variations in higher order moments (e.g....

The application of Structural Equation Modeling (SEM) in the business research is growing. The second-generation multivariate data analysis technique, SEM is easy to use and provides a high quality statistical analysis. Many visual SEM software programs help in a quick design of the theoretical...

Kritzman (2006) asserts that the widely cited Michaud (1989) study characterizing the impact of estimation error on Markowitz (1952) mean-variance (MV) optimization as “error maximization” is hype. His paper consists of two examples of MV portfolio optimizations: an eight-country index asset...

The solution to dynamic portfolio choice models can be formulated in terms of a value function by the Bellman principle of optimality, which reduces the multi-period optimal policy choice problem to a sequence of one-period maximization problems. For two adjacent periods, economists compute the...

I show that ambiguity-averse decision functionals matched with the multiple-prior learning model are more robust to model misspecification than the standard expected utility with Bayesian learning. However, these criteria may fail to deliver robust decisions because the multiple-prior learning...

There is a divergence in the returns of top-performing (star) firms and the rest of the economy, especially in industries that rely on a skilled labor force, raising concerns about their market power. We show that the divergence is explained by the mis-measurement of intangible capital. While...

We study implications of unpriced "granular measurement errors" -- idiosyncratic shocks to large firms that aren't well-diversified in market indices -- for asset pricing tests and propose alternative tests insensitive to them. We find stronger evidence of an intertemporal relation between the...

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed by the inclusion of those...

Motivated by the increasing use of external instruments to identify structural vector autoregressions SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy SVARs. Our algorithms make independent draws from the...

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of...