Cryptocurrency Modeling and Price Prediction Using Markov Switching Autoregressive Model

Patricia Juan Aurellia; David Kaluge

doi:10.15294/jejak.v17i2.2149

Jurnal

Cryptocurrency Modeling and Price Prediction Using Markov Switching Autoregressive Model

JEJAK · Vol. 17 No. 2 (2025) · hlm. 52–64

DOI: 10.15294/jejak.v17i2.2149
ISSN: 2460-5123, 1979-715X
Terbit: 2025-02-02
Penerbit: Universitas Negeri Semarang
Bahasa: eng

Abstrak

The emergence of cryptocurrency investment trends has brought the number of registered customers for crypto assets in Indonesia to surpass the number of investors in the capital market. Despite the continuous increase in the number of cryptocurrency investors, a different scenario is depicted by the declining transaction values. This decrease is attributed to the high volatile nature of cryptocurrency coins, which impacts investors’ investment decisions. This research aims to obtain the best model and forecasts related to cryptocurrency prices in order to minimize concerns and potential losses experienced by investors. This research uses the closing price of the five largest market capitalized cryptocurrency coins. The research utilized the Markov Switching Autoregressive method to capture structural changes in the data, allowing it to be used for forecasting. The research findings indicate that the best model for BTC is MS(3)AR(1), the model for BNB is MS(3)AR(1), the model for ETH is MS(3)AR(1), the model for XRP is MS(3)AR(2), and the model for ADA is MS(3)AR(2). The RMSE values indicate that BTC is the coin with the most accurate price prediction compared to other coins.

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University of Brawijaya
Universitas Tribhuwana Tunggadewi
Universitas Muhammadiyah Malang

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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Muhammad Sri Wahyudi Suliswanto	Universitas Muhammadiyah Malang	1
Agnes Quartina Pudjiastuti	Universitas Tribhuwana Tunggadewi	1
Widowati Widowati	Universitas Tribhuwana Tunggadewi	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jejak/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en">Cryptocurrency Modeling and Price Prediction Using Markov Switching Autoregressive Model</dc:title>
	<dc:creator xml:lang="en">Patricia Juan Aurellia</dc:creator>
	<dc:creator xml:lang="en">David Kaluge</dc:creator>
	<dc:subject xml:lang="en">Crytocurrency</dc:subject>
	<dc:subject xml:lang="en">Markov Switching</dc:subject>
	<dc:subject xml:lang="en">Investment</dc:subject>
	<dc:subject xml:lang="en">Autoregressive</dc:subject>
	<dc:subject xml:lang="en">Forcasting</dc:subject>
	<dc:description xml:lang="en">The emergence of cryptocurrency investment trends has brought the number of registered customers for crypto assets in Indonesia to surpass the number of investors in the capital market. Despite the continuous increase in the number of cryptocurrency investors, a different scenario is depicted by the declining transaction values. This decrease is attributed to the high volatile nature of cryptocurrency coins, which impacts investors’ investment decisions. This research aims to obtain the best model and forecasts related to cryptocurrency prices in order to minimize concerns and potential losses experienced by investors. This research uses the closing price of the five largest market capitalized cryptocurrency coins. The research utilized the Markov Switching Autoregressive method to capture structural changes in the data, allowing it to be used for forecasting. The research findings indicate that the best model for BTC is MS(3)AR(1), the model for BNB is MS(3)AR(1), the model for ETH is MS(3)AR(1), the model for XRP is MS(3)AR(2), and the model for ADA is MS(3)AR(2). The RMSE values indicate that BTC is the coin with the most accurate price prediction compared to other coins.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-02-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jejak/article/view/2149</dc:identifier>
	<dc:identifier>10.15294/jejak.v17i2.2149</dc:identifier>
	<dc:source xml:lang="en">JEJAK; Vol. 17 No. 2 (2024): September 2024; 52-64</dc:source>
	<dc:source>2460-5123</dc:source>
	<dc:source>1979-715X</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jejak/article/view/2149/2957</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 JEJAK: Jurnal Ekonomi dan Kebijakan</dc:rights>
</oai_dc:dc>