Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin
Keywords:
cryptocurrency, Bitcoin, ARMA, ARCH, GARCH, EGARCH, TGARCH, Volatility
Abstract
Cryptocurrencies, especially Bitcoin, is a hot commodity today. High volatility is a common feature of almost all the cryptocurrencies in the world. A systematic exploration and examination of the volatility of cryptos enables the investor to earn more on their investments. After the outbreak of the COVID-19 pandemic, the crypto market witnessed a highly volatile situation with a huge increase in price. The pandemic also affected the volatility and return of Bitcoin. This research aims to analyse and compare the risk and volatility characteristics of Bitcoin after the outbreak of the COVID-19 pandemic. The study further tests the capacity of several autoregressive models, such as ARMA, GARCH, EGARCH, and TARCH in estimating and evaluating the return and volatility associated with Bitcoin. Identified models were tested and compared with the help of Akaike information criteria (AIC) and Schwarz information criteria (SIC). For this article, the data of daily adjusted closing price of Bitcoin INR (BTC-INR) were collected from Yahoo Finance during the period January, 2017 to December, 2021. We witnessed a huge change in the daily average return of Bitcoin after the COVID-19 outbreak. Also, we identified TARCH (1, 1) as the best model in the ARCH family for evaluating and estimating volatility and ARMA (10, 10) as the best model for predicting the return of Bitcoin.
References
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Le, L. T., Yarovaya, L., & Nasir, M. A. (2021). Did COVID-19 change spillover patterns between Fintech and other asset classes? RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 58. https://doi.org/10.1016/j.ribaf.2021.101441 WE - Social Science Citation Index (SSCI)
Liu, W., Semeyutin, A., Lau, C. K. M., & Gozgor, G. (2020). Forecasting Value-at-Risk of Cryptocurrencies with RiskMetrics type models. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 54. https://doi.org/10.1016/j.ribaf.2020.101259 WE - Social Science Citation Index (SSCI)
Lopez-Cabarcos, M. A., Perez-Pico, A. M., Pineiro-Chousa, J., & Sevic, A. (2021). Bitcoin volatility, stock market and investor sentiment. Are they connected? FINANCE RESEARCH LETTERS, 38. https://doi.org/10.1016/j.frl.2019.101399
Mahendra, A., Mohanty, S. P., & Sudalaimuthu, S. (2021). Financial Astrology and Behavioral Bias: Evidence from India. Asia-Pacific Financial Markets, 28(1), 3–17. https://doi.org/10.1007/s10690-020-09310-8
Matkovskyy, R. (2019). Centralized and decentralized bitcoin markets: Euro vs USD vs GBP. QUARTERLY REVIEW OF ECONOMICS AND FINANCE, 71, 270–279. https://doi.org/10.1016/j.qref.2018.09.005
Mia, S., & Rahman, S. (2019). Evaluating the Forecast Accuracy of Exchange Rate Volatility in Bangladesh Using ARCH Family of Models. 9(5), 183–190. https://doi.org/10.5923/j.ajms.20190905.01
Miglietti, C., Kubosova, Z., & Skulanova, N. (2020). Bitcoin, Litecoin, and the Euro: an annualized volatility analysis. Studies in Economics and Finance, 37(2), 229–242. https://doi.org/10.1108/SEF-02-2019-0050
Naik, N., Mohan, B. R., & Jha, R. A. (2020). GARCH-Model Identification based on Performance of Information Criteria. Procedia Computer Science, 171, 1935–1942. https://doi.org/10.1016/j.procs.2020.04.207
Paramanik, R. N., & Singhal, V. (2020). Sentiment analysis of Indian stock market volatility. Procedia Computer Science, 176, 330–338. https://doi.org/10.1016/j.procs.2020.08.035
Park, B. J. (2022). The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market. Research in International Business and Finance, 59(July 2020), 101519. https://doi.org/10.1016/j.ribaf.2021.101519
Pele, D. T., Wesselhofft, N., Hardle, W. K., Kolossiatis, M., & Yatracos, Y. G. (n.d.). Are cryptos becoming alternative assets? EUROPEAN JOURNAL OF FINANCE. https://doi.org/10.1080/1351847X.2021.1960403 WE - Social Science Citation Index (SSCI)
Phillip, A., Chan, J. S. K., & Peiris, S. (2018). A new look at Cryptocurrencies. ECONOMICS LETTERS, 163, 6–9. https://doi.org/10.1016/j.econlet.2017.11.020
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Sahoo, M. (2021). COVID-19 impact on stock market: Evidence from the Indian stock market. Journal of Public Affairs, January. https://doi.org/10.1002/pa.2621
Sebastiao, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. FINANCIAL INNOVATION, 7(1). https://doi.org/10.1186/s40854-020-00217-x WE - Social Science Citation Index (SSCI)
Sifat, I. M., Mohamad, A., & Shariff, M. S. B. (2019). Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 50, 306–321. https://doi.org/10.1016/j.ribaf.2019.06.012
Tschorsch, F., & Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys and Tutorials, 18(3), 2084–2123. https://doi.org/10.1109/COMST.2016.2535718
Urquhart, A. (2018). What causes the attention of Bitcoin? ECONOMICS LETTERS, 166, 40–44. https://doi.org/10.1016/j.econlet.2018.02.017 WE - Social Science Citation Index (SSCI)
Yarovaya, L., Matkovskyy, R., & Jalan, A. (2021). The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets. JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 75. https://doi.org/10.1016/j.intfin.2021.101321 WE - Social Science Citation Index (SSCI)
Yousaf, I., & Ali, S. (2020). The COVID-19 outbreak and high frequency information transmission between major cryptocurrencies: Evidence from the VAR-DCC-GARCH approach. BORSA ISTANBUL REVIEW, 20, S1–S10. https://doi.org/10.1016/j.bir.2020.10.003 WE - Social Science Citation Index (SSCI)
Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/https://doi.org/10.1016/0165-1889(94)90039-6
Zhang, H. W., Hong, H. J., Guo, Y. Q., & Yang, C. (2022). Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains. INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 78, 267–285. https://doi.org/10.1016/j.iref.2021.12.005 WE - Social Science Citation Index (SSCI)
Ali, M., Alam, N., & Rizvi, S. A. R. (2020). Coronavirus (COVID-19) - An epidemic or pandemic for financial markets. JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE, 27. https://doi.org/10.1016/j.jbef.2020.100341 WE - Social Science Citation Index (SSCI)
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), 130–137. https://doi.org/10.22161/ijaers.4.11.20
Baur, D. G., & Dimpfl, T. (2021). The volatility of Bitcoin and its role as a medium of exchange and a store of value. EMPIRICAL ECONOMICS, 61(5), 2663–2683. https://doi.org/10.1007/s00181-020-01990-5 WE - Social Science Citation Index (SSCI)
Bergsli, L. O., Lind, A. F., Molnar, P., & Polasik, M. (2022). Forecasting volatility of Bitcoin. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 59. https://doi.org/10.1016/j.ribaf.2021.101540 WE - Social Science Citation Index (SSCI)
Bouri, E., Gkillas, K., Gupta, R., & Pierdzioch, C. (2021). Forecasting Realized Volatility of Bitcoin: The Role of the Trade War. COMPUTATIONAL ECONOMICS, 57(1), 29–53. https://doi.org/10.1007/s10614-020-10022-4 WE - Science Citation Index Expanded (SCI-EXPANDED) WE - Social Science Citation Index (SSCI)
Caporale, G. M., & Zekokh, T. (2019). Modelling volatility of cryptocurrencies using Markov-Switching GARCH models. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 48, 143–155. https://doi.org/10.1016/j.ribaf.2018.12.009 WE - Social Science Citation Index (SSCI)
Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH Modelling of Cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17. https://doi.org/10.3390/jrfm10040017
Conlon, T., Corbet, S., & Mcgee, R. J. (2020). Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 54. https://doi.org/10.1016/j.ribaf.2020.101248
Corbet, S., Lucey, B., Peat, M., & Vigne, S. (2018). Bitcoin Futures-What use are they? ECONOMICS LETTERS, 172, 23–27. https://doi.org/10.1016/j.econlet.2018.07.031 WE - Social Science Citation Index (SSCI)
Dong, H., Chen, L., Zhang, X., Failler, P., & Xu, S. (2020). The Asymmetric Effect of Volatility Spillover in Global Virtual Financial Asset Markets: The Case of Bitcoin. Emerging Markets Finance and Trade, 56(6), 1293–1311. https://doi.org/10.1080/1540496X.2019.1671819
Fang, L. B., Bouri, E., Gupta, R., & Roubaud, D. (2019). Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin? INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 61, 29–36. https://doi.org/10.1016/j.irfa.2018.12.010 WE - Social Science Citation Index (SSCI)
Garcia-Jorcano, L., & Benito, S. (2020). Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 54. https://doi.org/10.1016/j.ribaf.2020.101300 WE - Social Science Citation Index (SSCI)
John, E. I. (2019). Effect of Macroeconomic Variables on Stock Market Performance in Nigeria. Journal of Economics, Management and Trade, 2016, 1–14. https://doi.org/10.9734/jemt/2019/v22i630110
Karanasos, M., Karanassou, M., Mary, Q., & Fountas, S. (2004). Analyzing US In fl ation by a GARCH Model with Simultaneous Feedback. January.
Karp, A., & Van Vuuren, G. (2017). The Capital Asset Pricing Model And Fama-French Three Factor Model In An Emerging Market Environment. International Business & Economics Research Journal (IBER), 16(4), 231–256. https://doi.org/10.19030/iber.v16i4.10040
Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. ECONOMICS LETTERS, 158, 3–6. https://doi.org/10.1016/j.econlet.2017.06.023
Kim, J. M., Jun, C., & Lee, J. (2021). Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility. MATHEMATICS, 9(14). https://doi.org/10.3390/math9141614 WE - Science Citation Index Expanded (SCI-EXPANDED) WE - Social Science Citation Index (SSCI)
Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the New Gold - A comparison of volatility, correlation, and portfolio performance. INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 59, 105–116. https://doi.org/10.1016/j.irfa.2018.07.010
Le, L. T., Yarovaya, L., & Nasir, M. A. (2021). Did COVID-19 change spillover patterns between Fintech and other asset classes? RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 58. https://doi.org/10.1016/j.ribaf.2021.101441 WE - Social Science Citation Index (SSCI)
Liu, W., Semeyutin, A., Lau, C. K. M., & Gozgor, G. (2020). Forecasting Value-at-Risk of Cryptocurrencies with RiskMetrics type models. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 54. https://doi.org/10.1016/j.ribaf.2020.101259 WE - Social Science Citation Index (SSCI)
Lopez-Cabarcos, M. A., Perez-Pico, A. M., Pineiro-Chousa, J., & Sevic, A. (2021). Bitcoin volatility, stock market and investor sentiment. Are they connected? FINANCE RESEARCH LETTERS, 38. https://doi.org/10.1016/j.frl.2019.101399
Mahendra, A., Mohanty, S. P., & Sudalaimuthu, S. (2021). Financial Astrology and Behavioral Bias: Evidence from India. Asia-Pacific Financial Markets, 28(1), 3–17. https://doi.org/10.1007/s10690-020-09310-8
Matkovskyy, R. (2019). Centralized and decentralized bitcoin markets: Euro vs USD vs GBP. QUARTERLY REVIEW OF ECONOMICS AND FINANCE, 71, 270–279. https://doi.org/10.1016/j.qref.2018.09.005
Mia, S., & Rahman, S. (2019). Evaluating the Forecast Accuracy of Exchange Rate Volatility in Bangladesh Using ARCH Family of Models. 9(5), 183–190. https://doi.org/10.5923/j.ajms.20190905.01
Miglietti, C., Kubosova, Z., & Skulanova, N. (2020). Bitcoin, Litecoin, and the Euro: an annualized volatility analysis. Studies in Economics and Finance, 37(2), 229–242. https://doi.org/10.1108/SEF-02-2019-0050
Naik, N., Mohan, B. R., & Jha, R. A. (2020). GARCH-Model Identification based on Performance of Information Criteria. Procedia Computer Science, 171, 1935–1942. https://doi.org/10.1016/j.procs.2020.04.207
Paramanik, R. N., & Singhal, V. (2020). Sentiment analysis of Indian stock market volatility. Procedia Computer Science, 176, 330–338. https://doi.org/10.1016/j.procs.2020.08.035
Park, B. J. (2022). The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market. Research in International Business and Finance, 59(July 2020), 101519. https://doi.org/10.1016/j.ribaf.2021.101519
Pele, D. T., Wesselhofft, N., Hardle, W. K., Kolossiatis, M., & Yatracos, Y. G. (n.d.). Are cryptos becoming alternative assets? EUROPEAN JOURNAL OF FINANCE. https://doi.org/10.1080/1351847X.2021.1960403 WE - Social Science Citation Index (SSCI)
Phillip, A., Chan, J. S. K., & Peiris, S. (2018). A new look at Cryptocurrencies. ECONOMICS LETTERS, 163, 6–9. https://doi.org/10.1016/j.econlet.2017.11.020
Qarni, M. O., Gulzar, S., Fatima, S. T., Khan, M. J., & Shafi, K. (2019). Inter-markets volatility spillover in u.S. bitcoin and financial markets. Journal of Business Economics and Management, 20(4), 694–714. https://doi.org/10.3846/jbem.2019.8316
Sahoo, M. (2021). COVID-19 impact on stock market: Evidence from the Indian stock market. Journal of Public Affairs, January. https://doi.org/10.1002/pa.2621
Sebastiao, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. FINANCIAL INNOVATION, 7(1). https://doi.org/10.1186/s40854-020-00217-x WE - Social Science Citation Index (SSCI)
Sifat, I. M., Mohamad, A., & Shariff, M. S. B. (2019). Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 50, 306–321. https://doi.org/10.1016/j.ribaf.2019.06.012
Tschorsch, F., & Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys and Tutorials, 18(3), 2084–2123. https://doi.org/10.1109/COMST.2016.2535718
Urquhart, A. (2018). What causes the attention of Bitcoin? ECONOMICS LETTERS, 166, 40–44. https://doi.org/10.1016/j.econlet.2018.02.017 WE - Social Science Citation Index (SSCI)
Yarovaya, L., Matkovskyy, R., & Jalan, A. (2021). The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets. JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 75. https://doi.org/10.1016/j.intfin.2021.101321 WE - Social Science Citation Index (SSCI)
Yousaf, I., & Ali, S. (2020). The COVID-19 outbreak and high frequency information transmission between major cryptocurrencies: Evidence from the VAR-DCC-GARCH approach. BORSA ISTANBUL REVIEW, 20, S1–S10. https://doi.org/10.1016/j.bir.2020.10.003 WE - Social Science Citation Index (SSCI)
Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/https://doi.org/10.1016/0165-1889(94)90039-6
Zhang, H. W., Hong, H. J., Guo, Y. Q., & Yang, C. (2022). Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains. INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 78, 267–285. https://doi.org/10.1016/j.iref.2021.12.005 WE - Social Science Citation Index (SSCI)
