Two basic solutions have been proposed to fix the well-documented incompatibility of the sample covariance matrix with Markowitz mean-variance portfolio optimization: first, restrict leverage so much that no short sales are allowed; or, second, linearly shrink the sample covariance matrix towards...

This article questions the empirical usefulness of leverage effects to describe the dynamics of equity returns. Relying on both in and out of sample tests we consistently find a weak contribution of leverage effects over the past 25 years of S&P 500 returns. The skewness in the conditional...

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013)such that the estimated matrix is positive definite. Using this approach we can disentangle the...

Using the most comprehensive dataset of leveraged funds known to the literature, we measure the market-wide shadow cost of leverage constraints and examine its pricing implications. The shadow cost averages 0.56% per annum from 2006 to 2016, spikes upon quarter-ends when banks face tighter...

In a one-period economy, Martin (2017) and Chabi-Yo and Loudis (2020) derive bounds for the equity risk premium that use options of the same maturity as the horizon at which the premium is measured. In contrast, we provide an expression and an empirical methodology to measure the premium at a...

Stock return volatility significantly predicts active leverage adjustment, consistent with the trade-off theory. Firms respond asymmetrically to rising volatility instead of falling volatility, more with debt reduction than equity issuance. The forecasting power of stock return volatility mostly...

This paper examines the relationship between capital structure and shareholder returns in the UK between 1980 and 2008. Expanding on Modigliani and Miller's (1958) Proposition 2, returns are estimated using the asset pricing models of CAPM, Fama and French and of Carhart. The analysis shows that...

Yes, they can! Machine learning models that exploit big data identify leverage determinants and predict leverage better than classical methods. By allowing for nonlinearities and complex interactions, machine learning boosts the out-of-sample R-squared from 36% to 56% over linear methods such as...

We investigate the informational content of credit default swap (CDS) spreads for future volatility of (firm) assets and equity. In the cross-section, CDS spreads are significantly more informative about future asset than equity volatility. The informational content of historical and option...

In this paper, we develop a theoretical model of fund of hedge fund net leverage and alpha where the cost of borrowing is increasing with net leverage, thereby impacting the performance. We use this model to determine the conditions under which the leverage has a negative or a positive impact on...