GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement <i>GJR-GARCH</i> over the <i>GARC...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Journal of Risk and Financial Management |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1911-8074/14/9/421 |
| Summary: | Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement <i>GJR-GARCH</i> over the <i>GARCH</i> model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the <i>GJR-GARCH</i> model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (<i>ANN</i>) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (<i>MSEs</i>) from the <i>ANN</i> models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the <i>ANN</i> models perform better than traditional <i>ARIMA</i> models. |
|---|---|
| ISSN: | 1911-8066 1911-8074 |