Jiménez, I.; Mora-Valencia, A.; Ñíguez, T.-M.; Perote, J. Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies. Mathematics2020, 8, 2110.
Jiménez, I.; Mora-Valencia, A.; Ñíguez, T.-M.; Perote, J. Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies. Mathematics 2020, 8, 2110.
Jiménez, I.; Mora-Valencia, A.; Ñíguez, T.-M.; Perote, J. Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies. Mathematics2020, 8, 2110.
Jiménez, I.; Mora-Valencia, A.; Ñíguez, T.-M.; Perote, J. Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies. Mathematics 2020, 8, 2110.
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 on 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 a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.
Business, Economics and Management, Accounting and Taxation
Copyright:
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