GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

Fahad Mostafa, Pritam Saha, Mohammad Rafiqul Islam and Nguyet Nguyen
Additional contact information
Fahad Mostafa: Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
Pritam Saha: Rawls College of Business, Texas Tech University, Lubbock, TX 79409, USA
Mohammad Rafiqul Islam: Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA
Nguyet Nguyen: Department of Mathematics and Statistics, Youngstown State University, Youngstown, OH 44555, USA

JRFM, 2021, vol. 14, issue 9, 1-22

Abstract: 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 GJR-GARCH over the GARCH 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 GJR-GARCH 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 ( ANN ) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors ( MSEs ) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.

Keywords: artificial neural network; cryptocurrency; GJR-GARCH; NIG; Monte Carlo simulation; value at risk backtesting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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