Review on Models of Measuring Volatility of Cryptocurrencies

Abstract

The price of cryptocurrency is always volatile and is influenced by various factors like market returns, prices of stocks, gold, and correlation of prices of cryptocurrency. Modeling and forecasting the prices of cryptocurrencies and measuring the volatility with the GARCH specification (Engle, 1982) has become standard among researchers. Several applications and extensions of GARCH model is proposed by Bollerslev (1986). Later, an integrated GARCH model (Engle & Bollerslev, 1986) states that the persistence parameter is equal to one. A combination of short and long memory conditional models for the mean and the volatility to analyze crypto returns is done with the help of ARFIMA (Autoregressive Fractionally Integrated Moving Average) and FIGARCH (Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedastic) Model. This paper intended to understand various mathematical models for volatility of crypto currencies and also to find research gaps in the existing literature. A comprehensive overview is the need of the study.