Estimating the volatility of Bitcoin using GARCH models

TL;DR

This paper compares various GARCH models with different distributions to accurately estimate Bitcoin's volatility, finding that tGARCH with NIG distribution performs best due to its ability to model asymmetry and fat tails.

Contribution

The study introduces a comprehensive comparison of GARCH models with Student t, GED, and NIG distributions for Bitcoin volatility estimation, highlighting the superior performance of tGARCH-NIG.

Findings

tGARCH-NIG best captures Bitcoin return volatility.

NIG distribution effectively models leptokurtic cryptocurrency data.

tGARCH models reveal asymmetric shock responses in Bitcoin market.

Abstract

In this paper, an application of three GARCH-type models (sGARCH, iGARCH, and tGARCH) with Student t-distribution, Generalized Error distribution (GED), and Normal Inverse Gaussian (NIG) distribution are examined. The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distributions in the return series of Bitcoin. Comparative to the two distributions, the normal inverse Gaussian distribution captured adequately the fat tails and skewness in all the GARCH type models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.