Copula methods for evaluating relative tail forecasting performance. Issue 5 (20th September 2021)

• Record Type: Journal Article
• Authors: León, Ángel; Ñíguez, Trino-Manuel
• Is Part Of: Journal of risk finance. Volume 22:Issue 5(2021)
• Page Start: 332
• Page End: 344
• Publication Date: 2021-09-20
• Subjects: Conditional copula -- Conditional performance measures -- Equity-screening -- GJR -- SNP distribution; C22 -- G11; Risk management -- Periodicals; Risk (Insurance) -- Periodicals; Risk assessment -- Periodicals; 658.155
• DOI: 10.1108/JRF-10-2020-0222
• Languages: English
• ISSNs: 1526-5943

Abstract

Abstract : 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.