Comparative Forecasting of Major Cryptocurrencies: An Empirical Study Using Four Timeseries Forecasting Models

Meghna Jayasankar ()
Additional contact information
Meghna Jayasankar: Gulati Institute of Finance and Taxation (Affiliated to Cochin University of Science and Technology)

Chapter Chapter 3 in Financial Markets and Corporate Finance, 2024, pp 41-57 from Springer

Abstract: Abstract This study analyzes the in-sample forecasting power of four univariate time-series models—Naive, Autoregressive Moving Average (ARMA), Exponential Smoothing, and Autoregressive Fractionally Integrated Moving Average (ARFIMA)—for predicting 5-day returns of Bitcoin, Ether, Tether tokens, and Binance Coin (BNB). Each model's suitability varies due to the unique consensus systems and market dynamics of each cryptocurrency, suggesting no single model is universally optimal. While the Naive model proved unsuitable for most cases, ARMA demonstrated better accuracy for several cryptocurrencies, highlighting the importance of tailored forecasting approaches for diverse cryptocurrencies.

Keywords: Cryptocurrency; ARFIMA; ARMA; In sample forecasting; G10; G11; G17 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-97-6242-2_3

Ordering information: This item can be ordered from
http://www.springer.com/9789819762422

DOI: 10.1007/978-981-97-6242-2_3

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().