Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning
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, w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/11/2/32 |
| _version_ | 1797618338518532096 |
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| author | Khreshna Syuhada Venansius Tjahjono Arief Hakim |
| author_facet | Khreshna Syuhada Venansius Tjahjono Arief Hakim |
| author_sort | Khreshna Syuhada |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:11:39Z |
| format | Article |
| id | doaj.art-2eaed0cffbce4cc98df368d66f585ca2 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-11T08:11:39Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-2eaed0cffbce4cc98df368d66f585ca22023-11-16T23:05:00ZengMDPI AGRisks2227-90912023-02-011123210.3390/risks11020032Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble LearningKhreshna Syuhada0Venansius Tjahjono1Arief Hakim2Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaMetaverses 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.https://www.mdpi.com/2227-9091/11/2/32metaverse cryptocurrencyconditional heteroskedasticityensemble learningcopulamodified aggregate risk measure |
| spellingShingle | Khreshna Syuhada Venansius Tjahjono Arief Hakim Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning Risks metaverse cryptocurrency conditional heteroskedasticity ensemble learning copula modified aggregate risk measure |
| title | Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning |
| title_full | Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning |
| title_fullStr | Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning |
| title_full_unstemmed | Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning |
| title_short | Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning |
| title_sort | dependent metaverse risk forecasts with heteroskedastic models and ensemble learning |
| topic | metaverse cryptocurrency conditional heteroskedasticity ensemble learning copula modified aggregate risk measure |
| url | https://www.mdpi.com/2227-9091/11/2/32 |
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