Large investment universes are usually fatal to portfolio strategies optimizing higher moments because of computational and estimation issues resulting from the number of parameters involved. In this paper, we introduce a parsimonious method to estimate higher moments that consists of projecting asset returns onto a small set of maximally independent factors found via independent component analysis (ICA). In contrast to principal component analysis (PCA), we show that ICA resolves the curse of dimensionality affecting the comoment tensors of asset returns. The method is easy to implement, computationally efficient, and makes portfolio strategies optimizing higher moments appealing in large investment universes. Considering the value-at-risk as a risk measure, an investment universe of up to 500 stocks and adjusting for transaction costs, we show that our ICA approach leads to superior out-of-sample risk-adjusted performance compared with PCA, equally weighted, and minimum-variance portfolios.

We introduce a method to determine the investor’s optimal portfolio size that maximizes the expected out-of-sample utility under parameter uncertainty. This portfolio size trades off between accessing investment opportunities and limiting the number of estimated parameters. Unlike sparse methods such as lasso, which exclude assets during the optimization step, our approach fixes the optimal number of assets before optimizing the portfolio weights, which improves robustness and provides greater flexibility in practical implementations. Empirically, our size-optimized portfolios outperform their counterparts applied to all available assets. Our methodology renders portfolio theory valuable even when the data-set dimension and sample size are comparable.

Previous studies identify default rate as the main systematic determinant of bond recovery rates. We revisit this paradigm by investigating the impact of another factor, economic uncertainty. Based on a wide sample of American default issues and relying on beta regression models, well-suited for the bounded, heteroskedastic and skewed sample of recovery rates, we analyze the determinants of recovery rate distributions. We find economic uncertainty to be of paramount importance, as it proves to be the most important systematic determinant of recovery rate distributions, significant for both their mean and dispersion. By contrast, default rate remains a key determinant of the dispersion of these distributions, but not for their means. Considering this evidence is critical to the sound implementation of stochastic recovery rate models used by financial institutions for the computation of regulatory capital.

We introduce two novel indices built from CDS market data capturing the level and uncertainty information embedded in credit spreads aggregated by industry, and study their role in predicting bonds recovery rates. Analyzing 613 defaulted U.S. corporate bond issues from 2006 to 2019 and using a beta regression model, we find the cross-sectional mean and approximate entropy of CDS spreads aggregated at the sector level to be important predictors of the recovery rates distributions. In the classical beta regression model, both regressors are statistically significant and enhance the pseudo-R2 by up to 4%. Notably, a forward model selection procedure includes the sector-level regressor before well-known variables such as the bonds’ coupon rate or the American default rate. In addition, our sector-uncertainty regressor is the only significant uncertainty variable. These findings offer valuable insights for improving credit risk assessment methodologies and identifying key risk indicators of recovery rates before running prediction models.