arXiv 31 Dec 2024 · Econometrics
arXiv:2501.00634 · PDF · DOI · OpenAlex · Extracted main text
Financial crises are usually associated with increased cross-sectional dependence between asset returns, causing asymmetry between the lower and upper tail of return distribution. The detection of asymmetric dependence is now understood to be essential for market supervision, risk management, and portfolio allocation. I propose a non-parametric test procedure for the hypothesis of copula central symmetry based on the Cram\'er-von Mises distance of the empirical copula and its survival counterpart, deriving the asymptotic properties of the test under standard assumptions for stationary time series. I use the powerful tie-break bootstrap that, as the included simulation study implies, allows me to detect asymmetries with up to 25 series and the number of observations corresponding to one year of daily returns. Applying the procedure to US portfolio returns separately for each year shows that the amount of copula central asymmetry is time-varying and less present in the recent past. Asymmetry is more critical in portfolios based on size and less in portfolios based on book-to-market and momentum. In portfolios based on industry classification, asymmetry is present during market downturns, coherently with the financial contagion narrative.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Seo (2024) `Tie-break bootstrap for nonparametric rank statistics', Journal of Business & Economic Statistics 42(2), 615–627 | 1.000 | 7 | 3 | 100% |
| 2 | Ang \ Chen (2002) `Asymmetric correlations of equity portfolios', Journal of Financial Economics 63(3), 443–494 | 1.000 | 5 | 3 | 100% |
| 3 | Billio, Frattarolo \ Guégan (2021) `Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case', Dependence Modeling 9(1), 43–61 | 0.928 | 4 | 3 | 100% |
| 4 | Hong, Tu \ Zhou (2007) `Asymmetries in stock returns: Statistical tests and economic evaluation', Review of Financial Studies 20(5), 1547–1581 | 0.811 | 4 | 2 | 100% |
| 5 | Bücher \ Ruppert (2013) `Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique', J | 0.737 | 4 | 2 | 75% |
| 6 | Albuquerque (2012) `Skewness in Stock Returns: Reconciling the Evidence on Firm Versus Aggregate Returns', The Review of Financial Studies 25(5), 1… | 0.737 | 3 | 2 | 100% |
| 7 | Bormann \ Schienle (2020) `Detecting structural differences in tail dependence of financial time series', Journal of Business & Economic Statistics 38(2),… | 0.737 | 3 | 2 | 100% |
| 8 | Hong, Tu \ Zhou (2006) `Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation', The Review of Financial Studies 20(5), 1547–1581 | 0.737 | 3 | 2 | 100% |
| 9 | Joe (2014) Dependence Modeling with Copulas, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor & Francis | 0.737 | 3 | 2 | 100% |
| 10 | Billio, Frattarolo \ Guégan (2022) `High-dimensional radial symmetry of copula functions: Multiplier bootstrap vs | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.