Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR)

  • Amalia Listiani Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera
  • Dani Al Mahkya Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Abstract

Blockchain is a technology that is used for recording digital transactions that are interconnected and cannot be changed. Cryptocurrencies use blockchain technology, which has advantages due to a high level of security, low fees, and a high return on investment. One of the most popular cryptocurrencies and one that has a high market cap is Bitcoin. High volatility carries the risk of large losses. So it is necessary to analyze the risk of loss and forecast Bitcoin. Forecasting is carried out using the Autoregressive Integrated Moving Average (ARIMA) model, which is then carried out by risk analysis using Value at Risk (VaR) using the Historical Data method. Based on the research results, ARIMA [4,1,2] was great for predicting Bitcoin, with a Mean Absolute Percentage Error (MAPE) of 6%. Based on the results of research with Value at Risk (VaR), investors have a maximum loss tolerance of 5.86% and there is a 5% possibility that the losses will exceed 5.85%.

Downloads

Download data is not yet available.

Author Biographies

Amalia Listiani, Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Dani Al Mahkya, Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

References

[1]Bakar, N., A., Rosbi., S., “Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction”, Internasional Journal of Advanced Engineering Research and Science (IJAERS), Vol. 4, Issue 11, pp. 130-137, 2017.
[2]Bhiantara, I, B, P., “Teknologi Blockchain Cryptocurrency Di Era Revolusi Digital”, Prociding Seminar Nasional Pendidikan Teknik Informatika (SENAPATI) Ke-9 Bali, 08 September, 2018.
[3]Coinmarketcap., Diakses pada Juli 2021 dari https://coinmarketcap.com/currencies/Bitcoin/historical-data/ , 2021.
[4]Fauzi, M., A., Paiman, N., Othman, Z., “Bitcoin and Cryptocurrency: Challenges, Opportunities and Future Works”, Journal of Asian Finance, Economics and Business, Vol 7 No 8, Pg. 695-704, 2020.
[5]Hrytsiuk, P., Babych, T., Bachyshyna, L., “Cryptocurrency Portofolio Optimization Using Value-At-Risk Measure” in 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019), Vol. 95, pp. 385-389, 2019.
[6]I. M. Wirawan, T. Widiyaningtyas and M.M Hasan, “Short Term Prediction on Bitcoin Price using ARIMA Method” in International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 260-265, 2019.
[7]Khairunnisa, D, E., Purwanto, B., Ermawati, W, J., “Cryptocurrency Risk Analysis During the Covid-19 Pandemic: Value at Risk (VaR) Approach”, Jurnal Manajemen dan Organisasi (JMO), Vol. 13 No.1 pp. 61-73, 2022.
[8]Likitratcharoen, D., Ranong, T, N., Chuengsuksomboon, R., Sritanee, N., Pansriwong, A., “Value at Risk Performance in Cryptocurrencies”, The Journal of Risk Management and Insurance, Vol 22 No.1, 2018.
[9]Linsmeier, T., J., Pearson, N., D., “Value at Risk” in Financial Analysts Journal, Vol 56:2, pp.47-67, 2000.
[10]Mahkya, D, A., Anggraini, D., Fitriawati, D., Siahaan., R, M.H., “Pemodelan dan Prediksi Jumlah Penumpang Pelabuhan Bakauheni selama Periode Tsunami Selat Sunda menggunakan Autoregressive Integrated Moving Average” Journal of Science and Applicative Technology, Vol. 4(1), 2020, pp. 32-37.
[11]Mittal, R., Gehi, R., Bhatia, M.P.S, “Forecasting the Price of Cryptocurrencies and Validating Using ARIMA” in Proceedings of 4th International Conference on Computers & Management (ICCM), 2018.
[12]Salwa, N., Tatsara, N., Amalia, R., Zohra, A., “F. Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)” Journal of Data Analysis, Vol 1(1), pp. 21-21, 2018.
[13]Syafitri, N., Putri, A, I., Utami, D, C., Dery, D., Perkasa, S, B., Mahkya, A, D., “Pemodelan Pergerakan Harga Saham Bakrie Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average), Indonesian Journal of Applied Mathematics, vol.1 (1), pp. 19-24, 2020.
[14]Tamara, D., Ryabtsev, G., “Value at Risk (VAR) Application at Hypothetical Portofolios in Jakarta Islamic Index” Journal of Applied Finance and Accounting, Vol. 3(2) Pg. 153-18, 2021.
[15]Yamak, P., T., Yujian, L., Gadosey., P., K.,: “A comparison between ARIMA, LSTM, and GRU for Time Series Forecasting” in Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, Pg. 49-55, 2019.
Published
2022-12-29
How to Cite
LISTIANI, Amalia; MAHKYA, Dani Al. Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR). Journal of Science and Applicative Technology, [S.l.], v. 6, n. 2, p. 85-91, dec. 2022. ISSN 2581-0545. Available at: <https://journal.itera.ac.id/index.php/jsat/article/view/904>. Date accessed: 24 feb. 2024. doi: https://doi.org/10.35472/jsat.v6i2.904.