BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES

Dilek Lebleci Teker; Suat Teker; Esin Demirel Gümüştepe

doi:10.17261/Pressacademia.2024.1918

BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES

Abstract

Purpose- The primary purpose of this study is to model Bitcoin price volatility and forecast its future price returns using advanced econometric models such as ARCH and GARCH. The study aims to enhance risk management strategies and support informed investment decisions by addressing the time-varying nature of Bitcoin’s volatility. The research explores the persistence of volatility shocks and the clustering of price movements to provide insights into market dynamics. Methodology- This research examines daily Bitcoin closing prices over the period from January 2020 to October 2024. The data was preprocessed to ensure reliability, including applying logarithmic transformations to standardize the data and eliminate trends. Stationarity tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests, were conducted to confirm the series' stationarity. The ARCH-LM test was utilized to detect volatility clustering which is essential for validating the use of ARCH and GARCH models. Following this, ARIMA models were employed to define mean equations and GARCH models were used to estimate conditional variance and capture volatility dynamics. The dataset was split into training and validation subsets with data from July to October 2024 reserved for validation. Findings- The findings demonstrate that Bitcoin’s price movements exhibit significant volatility clustering and persistence of shocks which are key characteristics effectively captured by ARCH and GARCH models. These models provide valuable insights into the volatility patterns of Bitcoin, supporting their application in cryptocurrency analysis. Despite their robustness, the models face limitations in precise return forecasting during highly volatile periods, suggesting the need for further refinement or integration with advanced approaches. Conclusion- The research concludes that ARCH and GARCH models are effective tools for understanding and forecasting Bitcoin’s volatility. The study underscores the importance of acknowledging volatility persistence and clustering effects when analyzing cryptocurrency price behavior. However, it also highlights areas for improvement in econometric modelling by including the exploration of hybrid models and the integration of macroeconomic factors to enhance forecasting accuracy.

Keywords

References

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Engel C, Frankel JA, Froot KA, Rodrigues AP. (1995). Tests of conditional mean-variance efficiency of the U.S. stock market. Journal of Empirical Finance, 2, 3–18.
Engle, Robert F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50(4), 987-10.
Engle, Robert F. (1983). Estimates of the variance of U.S. inflation based upon the ARCH model. Journal of Money, Credit, and Banking, 15(3), 286-3.
Gujurati, D. N. (1999). Basic Econometrics Mc Graw Hill. İstanbul: Literatür Yayıncılık.
Gunawan, D., & Febrianti, I. (2023). Ethereum value forecasting model using Autoregressive Integrated Moving Average (ARIMA). International Journal of Advances in Social Sciences and Humanities, 2(1), 29-35.
Köse, N., Yildirim, H., Ünal, E., & Lin, B. (2024). The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility. Journal of Futures Markets, 44(4), 673-695.
Loureiro, C. V. (2023). Forecasting Bitcoin returns volatility using GARCH methods (Master’s thesis, ISCTE – Instituto Universitário de Lisboa).

Details

Primary Language

English

Subjects

Labor Economics, Microeconomics (Other), Finance, Business Administration

Journal Section

Research Article

Authors

Dilek Lebleci Teker ^*
0000-0002-3893-4015
Türkiye

Suat Teker
0000-0002-7981-3121
Türkiye

Esin Demirel Gümüştepe
0000-0003-4257-6780
Türkiye

Publication Date

December 31, 2024

Submission Date

October 5, 2024

Acceptance Date

November 10, 2024

Published in Issue

Year 2024 Volume: 20 Number: 1

DOI

https://doi.org/10.17261/Pressacademia.2024.1918

IZ

https://izlik.org/JA39PC54UH

Cite

RIS / Bibtex

APA

Lebleci Teker, D., Teker, S., & Demirel Gümüştepe, E. (2024). BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES. PressAcademia Procedia, 20(1), 14-16. https://doi.org/10.17261/Pressacademia.2024.1918

AMA

1.Lebleci Teker D, Teker S, Demirel Gümüştepe E. BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES. PAP. 2024;20(1):14-16. doi:10.17261/Pressacademia.2024.1918

Chicago

Lebleci Teker, Dilek, Suat Teker, and Esin Demirel Gümüştepe. 2024. “BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES”. PressAcademia Procedia 20 (1): 14-16. https://doi.org/10.17261/Pressacademia.2024.1918.

EndNote

Lebleci Teker D, Teker S, Demirel Gümüştepe E (December 1, 2024) BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES. PressAcademia Procedia 20 1 14–16.

IEEE

[1]D. Lebleci Teker, S. Teker, and E. Demirel Gümüştepe, “BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES”, PAP, vol. 20, no. 1, pp. 14–16, Dec. 2024, doi: 10.17261/Pressacademia.2024.1918.

ISNAD

Lebleci Teker, Dilek - Teker, Suat - Demirel Gümüştepe, Esin. “BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES”. PressAcademia Procedia 20/1 (December 1, 2024): 14-16. https://doi.org/10.17261/Pressacademia.2024.1918.

JAMA

1.Lebleci Teker D, Teker S, Demirel Gümüştepe E. BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES. PAP. 2024;20:14–16.

MLA

Lebleci Teker, Dilek, et al. “BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES”. PressAcademia Procedia, vol. 20, no. 1, Dec. 2024, pp. 14-16, doi:10.17261/Pressacademia.2024.1918.

Vancouver

1.Dilek Lebleci Teker, Suat Teker, Esin Demirel Gümüştepe. BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES. PAP. 2024 Dec. 1;20(1):14-6. doi:10.17261/Pressacademia.2024.1918