Risks of Large Portfolios

TL;DR

This paper introduces factor-based risk estimators and a high-confidence upper bound for assessing large portfolio risk, providing more accurate and insightful risk estimation compared to traditional methods.

Contribution

It proposes novel high-confidence bounds for large portfolio risk estimation and derives their limiting distribution in high dimensions.

Findings

Proposed upper bounds outperform traditional crude bounds.

Quantified the negligible relative error in risk estimation with 3-month daily data.

Applied methods successfully to empirical portfolio data.

Abstract

Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of such a risk estimator for large portfolios is largely unknown, and a simple inequality in the previous literature gives an infeasible upper bound for the estimation error. In addition, numerical studies illustrate that this upper bound is very crude. In this paper, we propose factor-based risk estimators under a large amount of assets, and introduce a high-confidence level upper bound (H-CLUB) to assess the accuracy of the risk estimation. The H-CLUB is constructed based on three different estimates of the volatility matrix: sample covariance, approximate factor model with known factors, and unknown factors (POET, Fan, Liao and Mincheva, 2013). For the first time in the literature, we derive the limiting distribution of the estimated risks in high dimensionality. Our numerical results demonstrate that the proposed upper bounds significantly outperform the traditional crude bounds, and provide insightful assessment of the estimation of the portfolio risks. In addition, our simulated results quantify the relative error in the risk estimation, which is usually negligible using 3-month daily data. Finally, the proposed methods are applied to an empirical study.