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

Author

Listed:
  • Inés Jiménez

    (Department of Economics and Economic History and IME, Faculty of Economics and Business, University of Salamanca, Campus Miguel de Unamuno (Edif. F.E.S.), 37007 Salamanca, Spain)

  • Andrés Mora-Valencia

    (School of Management, Universidad de los Andes, Calle 21 No. 1-20, Bogotá 111711, Colombia)

  • Trino-Manuel Ñíguez

    (School of Organizations, Economy and Society, Westminster Business School, University of Westminster, 35 Marylebone Road, London NW1 5LS, UK)

  • Javier Perote

    (Department of Economics and Economic History and IME, Faculty of Economics and Business, University of Salamanca, Campus Miguel de Unamuno (Edif. F.E.S.), 37007 Salamanca, Spain)

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.

Suggested Citation

  • Inés Jiménez & Andrés Mora-Valencia & Trino-Manuel Ñíguez & Javier Perote, 2020. "Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies," Mathematics, MDPI, vol. 8(12), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2110-:d:451220
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    Cited by:

    1. Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2023. "Multivariate dynamics between emerging markets and digital asset markets: An application of the SNP-DCC model," Emerging Markets Review, Elsevier, vol. 56(C).

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