Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

Authors

  • Irwan Kasse Departement Mathematics, Alauddin State Islamic University Makassar, Indonesia
  • Andi Mariani Departement Mathematics, Alauddin State Islamic University Makassar, Indonesia
  • Serly Utari Departement Mathematics, Alauddin State Islamic University Makassar, Indonesia
  • Didiharyono D. Andi Djemma University

DOI:

https://doi.org/10.31764/jtam.v5i1.3220

Keywords:

Investment risk, Time series, Heteroscedasticity, VaR-APARCH.

Abstract

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

References

Ali, G. (2013). EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites. Journal of Statistical and Econometric Methods, 2(3), 57–73.

Bakar, N. A., & Rosbi, S. (2017). Autoregressive Integrated Moving Average (ARIMA) Model For Forecasting Cryptocurrency Exchange Rate In High Volatility Environment: A New Insight Of Bitcoin Transaction. International Journal of Advanced Engineering Research and Science, 4(11), 237311.

Chu, D. (2018). Broker-Dealers for Virtual Currency: Regulating Cryptocurrency Wallets and Exchanges. Columbia Law Review, 118(8), 2323–2360.

Conrad, C., Karanasos, M., & Zeng, N. (2011). Multivariate Fractionally Integrated APARCH modeling of Stock Market Volatility: A Multi-Country Study. Journal of Empirical Finance, 18(1), 147–159.

Didiharyono, D., & Syukri, M. (2020). Forecasting With ARIMA Model in Anticipating Open Unemployment Rates in South Sulawesi. International Journal of Scientific and Technology Research, 9(3), 3838–3841.

Elvitra, C. W., Warsito, B., & Hoyyi, A. (2013). Metode Peramalan dengan Menggunakan Model Volatilitas Asymmetric Power ARCH (APARCH). Jurnal Gaussian, 2(4), 289–300.

Epaphra, M. (2016). Modeling exchange rate volatility: Application of the GARCH and EGARCH models. Journal of Mathematical Finance, 7(1), 121–143.

Giot, P., & Laurent, S. (2013). Market Risk In Commodity Markets: a VaR approach. Energy Economics, 25(5), 435–457.

Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio Diversification with Virtual Currency: Evidence from Bitcoin. International Review of Financial Analysis, 63(1), 431–437.

Gunay, S., & Khaki, A. R. (2018). Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models. Journal of Risk and Financial Management, 11(2), 30–45.

Hidayatullah, S., & Qudratullah, M. F. (2017). Analisis Risiko Investasi Saham Syariah Dengan Model Value AT Risk-Asymmetric Power Autoregressive Conditional Heterocedasticity (VaR-APARCH). Jurnal Fourier, 6(1), 37–43.

Ilupeju, Y. E. (2016). Modelling South Africa’s Market Risk Using the APARCH Model and Heavy-Tailed Distributions. Doctoral Dissertation University of KwaZulu-Natal Durban.

Irene, Y., Wijaya, M. Y., & Muhayani, A. (2020). World Gold Price Forecast using APARCH, EGARCH and TGARCH Model. InPrime: Indonesian Journal of Pure and Applied Mathematics, 2(2), 8–17.

Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 158(1), 3–6.

Nugroho, D. B., & Susanto, B. (2017). Volatility modeling for IDR Exchange Rate Through APARCH Model With Student-t Distribution. In AIP Conference Proceedings 1868 (1) (p. 040005).

Raymaekers, W. (2015). Cryptocurrency Bitcoin: Disruption, challenges and opportunities. Journal of Payments Strategy & Systems, 9(1), 30–46.

Sovbetov, Y. (2018). Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero. Journal of Economics and Financial Analysis, 2(2), 1–27.

Stavroyiannis, S. (2016). Value-at-Risk and backtesting with the APARCH Model and the Standardized Pearson Type IV Distribution. Available at SSRN 2734058. School of Management and Economics, Technological Educational Institute of Peloponnese.

Syarif, A. (2020). Forecasting the Development of Islamic Bank in Indonesia: Adopting ARIMA Model. JTAM (Jurnal Teori Dan Aplikasi Matematika), 4(2), 190–203.

Thorlie, M. A., Song, L., Amin, M., & Wang, X. (2015). Modeling and Forecasting of Stock Index Volatility with APARCH Models under Ordered Restriction. Statistica Neerlandica, 69(3), 329–356.

Thorlie, M. A., Song, L., Wang, X., & Amin, M. (2014). Modelling Exchange Rate Volatility Using Asymmetric GARCH Models (Evidence From Sierra Leone). International Journal of Science and Research, 3(11), 1206–1214.

Tsay, R. S. (2014). Analysis of Financial Time Series. Canada: John Wiley and Sons, Inc.

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Published

2021-04-17

Issue

Vol. 5 No. 1 (2021): April

Section

Articles

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