This paper uses a highly disaggregated demand system to estimate the degree of substitutability among monetary assets and to address the issue of optimal monetary aggregation in the United States. We address the problems of dimensionality and nonlinearity, estimating a very detailed monetary...

We develop a methodology for estimating and testing the effect of anomalies in conditional asset pricing models when premia are time-varying. Our method, which builds on the two-pass methodology, is developed for ordinary and weighted least-squares estimation, considering both cases of correct...

This article reviews the literature on sparse high-dimensional models and discusses some applications in economics and finance. Recent developments in theory, methods, and implementations in penalized least-squares and penalized likelihood methods are highlighted. These variable selection...

We propose a jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate correlations over lower sampling frequencies, to...

We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance estimators and verify their reduced bias and mean...

Pre-averaging is a popular strategy for mitigating microstructure in high frequency financial data. As the term suggests, transaction or quote data are averaged over short time periods ranging from 30 seconds to five minutes, and the resulting averages approximate the efficient price process...

Markowitz portfolio selection is a cornerstone in finance, both in academia and in the industry. Most academic studies either ignore transaction costs or account for them in a way that is both unrealistic and suboptimal by (i) assuming transaction costs to be constant across stocks and (ii)...

Linear multifactor models are of great importance in portfolio construction and risk management since they provide market dimensionality reduction, which has numerous useful implications. In particular, factor models reduce the dimensionality of the asset covariance matrix, allowing for better...

An influential observation has the potential to render a model unsuitable for estimation with an OLS regression. This is well known in the statistics literature. However, the use of standard measures for detecting and managing influential observations is not common practice in empirical finance....

A successful long-term financial plan depends on the correspondence of projected returns and actual returns. Simulation results are subject to the effects of differences between implementation fund(s) attributes and asset class attributes used in the simulation. Thus, simulation outcomes and...