3.8 Article

Copula methods for evaluating relative tail forecasting performance

Journal

JOURNAL OF RISK FINANCE
Volume 22, Issue 5, Pages 332-344

Publisher

EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/JRF-10-2020-0222

Keywords

Conditional copula; Conditional performance measures; Equity-screening; GJR; SNP distribution; C22; G11

Funding

  1. Spanish Ministry of Economy and Competitiveness [ECO2017-87069-P]
  2. Angel Leon

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The study applies conditional copulas to analyze differences in alternative portfolio performance strategies, highlighting that expected tail ratio and skewness-kurtosis ratio portfolios show remarkably low correlations with the Sharpe ratio portfolio under the Gaussian copula. Additionally, copulas focusing on the upper or lower tail render significant differences in performance. The copula analysis is useful for understanding the superiority of equity-screening strategies compared to the Sharpe ratio portfolio.
Purpose The authors apply their method to analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio (SR). Design/methodology/approach In this paper the authors illustrate the use of conditional copulas for identifying differences in alternative portfolio performance strategies. The authors analyze which portfolios are capable of providing superior performance to those based on the SR. Findings The results show that under the Gaussian copula, both expected tail ratio (ETR) and skewness-kurtosis ratio portfolios exhibit remarkably low correlations respecting the SR portfolio. This means that these two portfolios are different respecting the SR one. The authors also find that copulas which focus on either the upper tail (Gumbel) or the lower tail (Clayton) render significant differences. In short, the copula analysis is useful to understand what kind of equity-screening strategy based on its corresponding performance measure (PM) performs better in relation to the SR portfolio. Practical implications Copula methods for evaluating relative tail forecasting performance provide an alternative tool when forecast differences are very small or found non statistically significant through standard tests. Originality/value Our copula methods to evaluate models' performance differences are significant because when models' performance is rather similar, conclusions on statistical differences, can be defective as they may hinge on the subsample type or size used, leading to inefficient investment decisions. Our method based in copula is novel in this research topic.

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