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Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models

Author

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  • Hakan Yıldırım

    (Istanbul Gelisim University)

  • Festus Victor Bekun

    (Istanbul Gelisim University)

Abstract

The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.

Suggested Citation

  • Hakan Yıldırım & Festus Victor Bekun, 2023. "Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models," Future Business Journal, Springer, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:spr:futbus:v:9:y:2023:i:1:d:10.1186_s43093-023-00255-8
    DOI: 10.1186/s43093-023-00255-8
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    References listed on IDEAS

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    1. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
    2. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Rasheed O. Alao & Abdulkareem Alhassan & Saheed Alao & Ifedolapo O. Olanipekun & Godwin O. Olasehinde-Williams & Ojonugwa Usman, 2023. "Symmetric and asymmetric GARCH estimations of the impact of oil price uncertainty on output growth: evidence from the G7," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-14, December.
    5. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    6. Mapa, Dennis S., 2004. "A Forecast Comparison of Financial Volatility Models: GARCH (1,1) is not Enough," MPRA Paper 21028, University Library of Munich, Germany.
    7. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Clement Kweku Kyei, 2021. "High-Frequency Predictability of Housing Market Movements of the United States: The Role of Economic Sentiment," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(4), pages 490-498, October.
    8. Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2018. "Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 74-87, January.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    10. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    11. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    12. Bernard Ben Sita, 2019. "Crude oil and gasoline volatility risk into a Realized-EGARCH model," Review of Quantitative Finance and Accounting, Springer, vol. 53(3), pages 701-720, October.
    13. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    14. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    15. Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019. "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 150-163.
    16. Ashish Kumar, 2015. "Impact of Currency Futures on Volatility in Exchange Rate," Paradigm, , vol. 19(1), pages 95-108, June.
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