Average and Risk-Return Analysis of Cryptocurrencies Using ARMA-GARCH Models

Audrey Ariij Sya’imaa.HS, Kankan Parmikanti, Riaman Riaman


Cryptocurrency is a digital currency that is created through encrypted cryptography with complex algorithm and connected to each other on the blockchain system. Cryptocurrencies are widely used as investment instruments for financial assets like stocks. Similar to stocks, cryptocurrencies have a high risk – high returns characteristic, but the fluctuation of cryptocurrencies are more dynamic. Professional investors would do a volatility analysis of cryptocurrencies that potentially give the best returns. Returns assessment usually refers to the average value or expected return, while the estimated investment risk can be seen and analyzed from the volatility value. The study aimed to analyze the average and volatility of cryptocurrencies. This research was a case study done on five cryptocurrencies that are included at Top Gainers of 30 days update lists, in September 2022. The period is January 1, 2019 – September 30, 2022. The ARMA-GARCH models using three types of GARCH models, those are SGARCH(1,1), IGARCH(1,1), and TGARCH(1,1) were used for analysis. Based on the results of this research, the best ARMA-GARCH model for cryptocurrency Quant, XRP, Stellar, Monero, and Decred is ARMA(1,0)-SGARCH(1,1), ARMA(32,0)-TGARCH(1,1), ARMA(0,14)-SGARCH(1,1), ARMA(1,4)-TGARCH(1,1), and ARMA(1,0)-SGARCH(1,1). Best expected return with the lowest volatility value is owned by Monero (XMR). The research can be used by investors as a consideration in investing decision-making to cryptocurrencies.


Investment, Cryptocurrencies, ARMA, GARCH

Full Text:



Francq, C., & Zakoian, J. M. (2010). GARCH models: structure, statistical inference and financial applications. John Wiley & Sons.

Ghani, I. M., & Rahim, H. A. (2019). Modeling and forecasting of volatility using arma-garch: Case study on malaysia natural rubber prices. In IOP Conference Series: Materials Science and Engineering, vol. 548, pp. 012023.

Gujarati, D. N. (2004). Basic Econometrics (Fourth Edition). New York: Mcgraw-Hill Companies.

Gyamerah, S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quant Financ Econ, vol. 3, pp 739-753.

Panna, M. (2017). Note on simple and logarithmic return. APSTRACT: applied studies in agribusiness and commerce, vol. 11, pp 127-136.

Rohman, M. N. (2021). Tinjauan Yuridis Normatif Terhadap Regulasi Mata Uang Kripto (Cryptocurrency) di Indonesia. Jurnal Supremasi, vol. 11, pp 1-10.

Tsay, R.S. (2005). Analysis of financial time series, Second Edition, Chicago: John Wiley & Sons, Inc, University of Chicago.

DOI: https://doi.org/10.47194/ijgor.v4i4.214

Article Metrics

Abstract view : 28 times
PDF - 22 times


  • There are currently no refbacks.

Copyright (c) 2023 International Journal of Global Operations Research

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Published By: 

Iora Journal
Jl. Merkuri Timur VI No. 1, RT. 007, RW. 004, Manjahlega, Rancasari, Kota Bandung, Jawa Barat, INDONESIA Phone: +62 85841953112; +62 811

IJGOR Indexed By: 

width=width=width=  width= width=width=  



Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.