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Forecasting the Risk of Cryptocurrencies: Comparison and Combination of GARCH and Stochastic Volatility Models

Jan Prüser

Journal of Time Series Econometrics2024https://doi.org/10.1515/jtse-2023-0039article
AJG 2ABDC B
Weight
0.38

What the paper says

Abstract The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t -distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.

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https://doi.org/https://doi.org/10.1515/jtse-2023-0039

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@article{jan2024,
  title        = {{Forecasting the Risk of Cryptocurrencies: Comparison and Combination of GARCH and Stochastic Volatility Models}},
  author       = {Jan Prüser},
  journal      = {Journal of Time Series Econometrics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2023-0039},
}

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Evidence weight

0.38

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.19 × 0.4 = 0.07
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.