Title | Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies |
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Type | Journal article |
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Authors | Jiménez, I., Mora-Valencia, A., Ñíguez, T.M. and Perote, J. |
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Abstract | The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of dynamic equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio of cryptocurrencies by implementing backtesting techniques and for different risk measures: value-at-risk, expected shortfall and median shortfall. The results support our proposal showing that the SNP-DCC model has better performance for lower confidence levels than the SNP-DECO model and is more appropriate for portfolio diversification purposes. |
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Keywords | Gram–Charlier series |
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| DCC |
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| DECO |
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| backtesting |
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| cryptocurrencies |
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Article number | e2110 |
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Journal | Mathematics |
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Journal citation | 8 (12) |
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ISSN | 2227-7390 |
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Year | 2020 |
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Publisher | MDPI |
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Publisher's version | File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.3390/math8122110 |
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Web address (URL) | https://www.mdpi.com/2227-7390/8/12/2110 |
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Publication dates |
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Published online | 26 Nov 2020 |
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License | CC BY 4.0 |
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