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Time series analysis of Cryptocurrency returns and volatilities

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Abstract

There is a significant interest in the growth and development of cryptocurrencies, the most notable ones being Bitcoin and Ripple. Global trading in these cryptocurrencies has led to highly speculative and “bubble-like” price movements. Since these cryptocurrencies trade like stocks, provide a feasible alternative to gold and appreciate during uncertain times, it can be hypothesized that their prices are partly determined by the global stock indices, gold prices, and fear gauges such as the VIX and the US Economic Policy Uncertainty Index. In this paper, we test this hypothesis by conducting a time series analysis of returns and volatilities of Bitcoin and of Ripple. We use the Autoregressive-moving-average model with exogenous inputs model (ARMAX), Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model, Vector Autoregression (VAR) model, and Granger causality tests to determine linkages between returns and volatilities of Bitcoin and of Ripple. We find that the Bitcoin crash of 2018 could have been explained using these time series methods. We also find that returns of global stock markets and of gold do not have a causal effect on Bitcoin returns, but we do find returns on Ripple have a causal effect on Bitcoin prices.

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Notes

  1. What is Bitcoin? https://Bitcoin.org/en/faq#what-is-Bitcoin

  2. Blockchain by the numbers, https://www.blockchain.com/about/index.html

  3. Bitcoin Market Capitalization, https://coinmarketcap.com/

  4. Estimated USD transaction value, https://blockchain.info/charts/estimated-transaction-volume-usd

  5. USD in circulation, https://fred.stlouisfed.org/series/WCURCIR

  6. List of major cryptocurrencies by market capitalization, https://coinmarketcap.com/all/views/all/

  7. BVOL computations, https://www.bitmex.com/app/index/.BVOL

  8. Bitcoin (BTC) Prices, https://www.coindesk.com/price/bitcoin

  9. Ripple (XRP) prices, https://www.coindesk.com/price/xrp

  10. VIX historical data, http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data

  11. Gold historical data, https://www.gold.org/data/gold-price

  12. https://fred.stlouisfed.org/series/USEPUINDXD/

  13. BVOL computations, https://www.bitmex.com/app/index/.BVOL

  14. VAR model order selection: https://sccn.ucsd.edu/wiki/Chapter_3.5._Model_order_selection

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Acknowledgement

We thank Pallavi Malladi for providing us with research and editorial support.

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Authors and Affiliations

  1. Department of Accounting, Finance, and Economics, College of Business and Public Policy (CBAPP), California State University, Dominguez Hills, 1000, E Victoria Street, SBS C 315, Carson, CA, 90747, USA

    Rama K. Malladi & Prakash L. Dheeriya

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  1. Rama K. Malladi
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  2. Prakash L. Dheeriya
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Correspondence to Rama K. Malladi.

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Rama K. Malladi is with California State University, Dominguez Hills, in California. He is a CFA Charter holder and a past president of the CFA Society Los Angeles.

Prakash L. Dheeriya is with California State University, Dominguez Hills, in California. He is a past chair of the Department of Finance.

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Malladi, R.K., Dheeriya, P.L. Time series analysis of Cryptocurrency returns and volatilities. J Econ Finan 45, 75–94 (2021). https://doi.org/10.1007/s12197-020-09526-4

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