Estimation noise is a well-known issue in empirical portfolio modelling. Estimated weights are known to have huge standard errors and bad predictive quality, which often results in an inferior out-of-sample portfolio performance compared to simple alternatives. Most of the recent literature...

Factor momentum has formed the basis of factor timing strategies. We propose an alternative approach for timing factor portfolio returns by exploiting the information from their portfolio characteristics. Different combinations of dimension reduction techniques are employed to independently...

Transaction cost variance introduces a risk often neglected in portfolio optimization. We define a mean-variance portfolio optimization problem and show that including a transaction cost variance term significantly impacts the performance of these portfolios. Transaction cost variance is...

Equal-weighted (EW) portfolios have outperformed their value-weighted (VW) counterparts over multiple decades in various investment universes. This paper investigates the long-term evidence or the EW–VW return spread in a broad U.S. equity universe across multiple factor models....

Investors face similar macroeconomic risks and opportunities regardless of their individual investment preferences. To best navigate growth and inflation concerns, we propose building macro factor-mimicking portfolios diversified across asset classes and style factors. We focus on the macro...

In previous studies, high-frequency data has been used to improve portfolio allocation by estimating the full realized covariance matrix. In this paper, we show that strategies using high-frequency data for measuring and forecasting univariate realized volatility alone can already generate...

We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, Copula-GARCH and dynamic GAS models. Utilizing a loss function that overcomes the lack of elicitability for Expected Shortfall, we propose a novel Expected...