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.
Highlights
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way
We use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, and calculate the value at risk (VaR) of the simulations
We present the results of the GJR-GARCH and GARCH models for estimating the volatilities of the ten selected cryptocurrencies, simulated cryptocurrency volatility, and predicted cryptocurrency prices
Summary
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. 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. 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. A cryptocurrency is a digital currency and is used as a medium of exchange. It is a digital or virtual currency with no regulatory council and, to date, without any government interventions, leading to a risk factor for cryptocurrency investment. It is essential to understand the risk–return properties of cryptocurrency
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