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Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies

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  • Jie Cheng

    (Keele University)

Abstract

In this paper, we investigate the co-dependence and portfolio value-at-risk of cryptocurrencies, with the Bitcoin, Ethereum, Litecoin and Ripple price series from January 2016 to December 2021, covering the crypto crash and pandemic period, using the generalized autoregressive score (GAS) model. We find evidence of strong dependence among the virtual currencies with a dynamic structure. The empirical analysis shows that the GAS model smoothly handles volatility and correlation changes, especially during more volatile periods in the markets. We perform a comprehensive comparison of out-of-sample probabilistic forecasts for a range of financial assets and backtests and the GAS model outperforms the classic DCC (dynamic conditional correlation) GARCH model and provides new insights into multivariate risk measures.

Suggested Citation

  • Jie Cheng, 2023. "Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies," Empirical Economics, Springer, vol. 65(2), pages 899-924, August.
    • Handle: RePEc:spr:empeco:v:65:y:2023:i:2:d:10.1007_s00181-023-02360-7
    DOI: 10.1007/s00181-023-02360-7
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    Cited by:

    1. Bouteska, Ahmed & Abedin, Mohammad Zoynul & Hajek, Petr & Yuan, Kunpeng, 2024. "Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods," International Review of Financial Analysis, Elsevier, vol. 92(C).
    2. Jie Cheng, 2024. "Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3617-3643, December.
    3. Khreshna Syuhada & Venansius Tjahjono & Arief Hakim, 2023. "Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning," Risks, MDPI, vol. 11(2), pages 1-25, February.

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    More about this item

    Keywords

    Cryptocurrencies; Generalized autoregressive score (GAS) model; Multivariate probabilistic forecasts; Portfolio management;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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