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Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning

Author

Listed:
  • Khreshna Syuhada

    (Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia)

  • Venansius Tjahjono

    (Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia)

  • Arief Hakim

    (Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia)

Abstract

Metaverses have been evolving following the popularity of blockchain technology. They build their own cryptocurrencies for transactions inside their platforms. These new cryptocurrencies are, however, still highly speculative, volatile, and risky, motivating us to manage their risk. In this paper, we aimed to forecast the risk of Decentraland’s MANA and Theta Network’s THETA. More specifically, we constructed an aggregate of these metaverse cryptocurrencies as well as their combination with Bitcoin. To measure their risk, we proposed a modified aggregate risk measure (AggM) defined as a convex combination of aggregate value-at-risk (AggVaR) and aggregate expected shortfall (AggES). To capture their dependence, we employed copulas that link their marginal models: heteroskedastic and ensemble learning-based models. Our empirical study showed that the latter outperformed the former when forecasting volatility and aggregate risk measures. In particular, the AggM forecast was more accurate and more valid than the AggVaR and AggES forecasts. These risk measures confirmed that an aggregate of the two metaverse cryptocurrencies exhibited the highest risk with evidence of lower tail dependence. These results are, thus, helpful for cryptocurrency investors, portfolio risk managers, and policy-makers to formulate appropriate cryptocurrency investment strategies, portfolio allocation, and decision-making, particularly during extremely negative shocks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:2:p:32-:d:1054142
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    References listed on IDEAS

    as
    1. Jie Cheng, 2023. "Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies," Empirical Economics, Springer, vol. 65(2), pages 899-924, August.
    2. Aharon, David Y. & Demir, Ender, 2022. "NFTs and asset class spillovers: Lessons from the period around the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 47(PA).
    3. Susanne Emmer & Marie Kratz & Dirk Tasche, 2013. "What is the best risk measure in practice? A comparison of standard measures," Papers 1312.1645, arXiv.org, revised Apr 2015.
    4. Yizhi Wang & Florian Horky & Lennart J. Baals & Brian M. Lucey & Samuel A. Vigne, 2022. "Bubbles all the way down? Detecting and date-stamping bubble behaviours in NFT and DeFi markets," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 20(4), pages 415-436, October.
    5. Cascos, Ignacio & Molchanov, Ilya, 2013. "Choosing a random distribution with prescribed risks," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 599-605.
    6. Karim, Sitara & Lucey, Brian M. & Naeem, Muhammad Abubakr & Uddin, Gazi Salah, 2022. "Examining the interrelatedness of NFTs, DeFi tokens and cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PB).
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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