The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

Abstract

This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

Are cryptos becoming alternative assets?

“…This result confirms the findings from Naimy et al (2021), who showed that the most stable cryptocurrency is ten times more volatile than the most unstable fiat currency.…”

An analysis of investors’ behavior in Bitcoin market

“…Mostly, the flourishing interest of academia in this relatively new asset class was driven by its unique nature (see [ 2 , 3 ] for a comprehensive review). To all appearances, among the empirically documented inherent features of these synthetic currencies, it seems that so far several are indisputable, to wit, high volatility [ 4 – 6 ], clustering and long memory of volatility [ 7 – 10 ], the presence of jumps [ 11 – 13 ], high correlation within the crypto market [ 14 – 16 ] but relative isolation from other asset classes in normal times [ 17 – 19 ] and increased contagion in severe turbulent times [ 20 – 22 ], etc. The most disputable characteristics of this atypical asset refer to the investment or currency potential and its ability to act or not as a safe haven.…”

Deep learning-based methods for forecasting Brent Crude Oil return considering COVID-19 pandemic effect

“…Sadik et al (2019) presented the NA-GARCH model for forecasting the stock price volatility, which is the combination of the GARCH model by examining the effects of quantified news sentiment on the movement of stock prices. Finally, Naimy et al (2021) compared the accuracy of GARCH models in assessing the cryptocurrencies volatility. Many financial time series data show a nonlinear dependency structure, but in GARCH models, a linear correlation structure is generally assumed between time-series data; hence, these models do not usually record the nonlinear patterns.…”