Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Version 1 : Received: 21 October 2020 / Approved: 22 October 2020 / Online: 22 October 2020 (21:50:55 CEST)

How to cite: Jiménez, I.; Mora-Valencia, A.; Ñíguez, T.; Perote, J. Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies. Preprints 2020, 2020100468 (doi: 10.20944/preprints202010.0468.v1). Jiménez, I.; Mora-Valencia, A.; Ñíguez, T.; Perote, J. Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies. Preprints 2020, 2020100468 (doi: 10.20944/preprints202010.0468.v1).

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.

Subject Areas

Gram-Charlier series; DCC; DECO; backtesting; cryptocurrencies

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