Major determinants of Bitcoin price: Application of a vector error correction model
-
DOIhttp://dx.doi.org/10.21511/imfi.20(4).2023.21
-
Article InfoVolume 20 2023, Issue #4, pp. 257-271
- Cited by
- 321 Views
-
478 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Research in recent years has shown that Bitcoin is a virtual asset that is used as a medium of exchange and investment tool other than shares and bonds, the development of the digital era has opened up opportunities for Bitcoin to be chosen as part of an investor’s portfolio. The focus of this study is to examine the impact of nine key determinants on Bitcoin price. The data used in the study are daily data starting from January 1, 2018 to January 1, 2022. The main data source is taken from Investing.com, and the estimation method applied is the Vector Error Correction Model (VECM). The main finding shows that Bitcoin Volume impacts Bitcoin Price negatively, which is in line with the demand theory. Another finding is related to the substitute effect of Ethereum Volume, Litecoin Volume, and Gold Volume, each of which influences Bitcoin Price positively, suggesting that these three commodities are substitutes to Bitcoin. In contrast, whereas Oil Volume has an insignificant effect on Bitcoin price in the short term, it has a negative significant impact in the long term. In addition, LQ45 stock index Volume influences Bitcoin Price positively in the short term, suggesting that LQ45 stock index and Bitcoin substitute for each other. Moreover, Google Trends impacts Bitcoin price positively in the long term. In terms of the income effect, either the Indonesian GDP or US GDP has a strong positive effect on Bitcoin price in both the short and long term.
- Keywords
-
JEL Classification (Paper profile tab)G11, G15, G17, B22
-
References68
-
Tables9
-
Figures2
-
- Figure 1. AR roots graph
- Figure 2. Impulse response function graph
-
- Table 1. Descriptive statistics of the selected variables
- Table 2. Unit root test (1st difference)
- Table 3. Lag length criteria
- Table 4. Johansen’s co-integration test
- Table 5. Error correction terms
- Table 6. Estimation results of the short-run (Vector Error Correction Model)
- Table 7. Estimation results of the long-run relationship (Ordinary Least Squared)
- Table 8. Variance decomposition
- Table 9. Summary of hypothesis testing
-
- Alshehry, A. S., & Belloumi, M. (2015). Energy consumption, carbon dioxide emissions and economic growth: The case of Saudi Arabia. Renewable and Sustainable Energy Reviews, 41, 237-247.
- Angela, O., & Sun, Y. (2020). Factors affecting cryptocurrency prices: Evidence from ethereum. In 2020 International Conference on Information Management and Technology (ICIMTech) (pp. 318-323). IEEE.
- Bakas, D., Magkonis, G., & Oh, E. Y. (2022). What drives volatility in Bitcoin market?. Finance Research Letters, 50, 103237.
- Baumöhl, E. (2019). Are cryptocurrencies connected to forex? A quantile cross-spectral approach. Finance Research Letters, 29, 363-372.
- Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, 177-189.
- Ben Omrane, W., Houidi, F., & Savaser, T. (2023). Macroeconomic news and intraday seasonal volatility in the cryptocurrency markets. Applied Economics.
- Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219-227.
- Bhosale, J., & Mavale, S. (2018). Volatility of select crypto-currencies: A comparison of Bitcoin, Ethereum and Litecoin. SCMS, Pune, 6.
- Bjornland, H. C. (2006). PhD course: structural VAR models. Norwegian School of Management (BI).
- Boamah, K. B., Du, J., Adu, D., Mensah, C. N., Dauda, L., & Khan, M. A. S. (2021). Predicting the carbon dioxide emission of China using a novel augmented hypo-variance brain storm optimisation and the impulse response function. Environmental Technology, 42(27), 4342-4354.
- Bouri, E., Lucey, B., & Roubaud, D. (2020). Cryptocurrencies and the downside risk in equity investments. Finance Research Letters, 33, 101211.
- Bouteska, A., & Harasheh, M. (2023). Bitcoin volatility and the introduction of bitcoin futures: A portfolio construction approach. Finance Research Letters, 57, 104200.
- Charfeddine, L., Benlagha, N., & Maouchi, Y. (2020). Investigating the dynamic relationship between cryptocurrencies and conventional assets: implications for financial investors. Economic Modelling, 85, 198-217.
- Chen, Y. (2021). Empirical analysis of bitcoin price. Journal of Economics and Finance, 45(4), 692-715.
- Chuen, D. (2015). Handbook of Digital Currency :Bitcoin, Inovation, finantial instrument and big data. Elsevier Inc.
- Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of Bitcoin price formation. Applied Economics, 48(19), 1799-1815.
- Corbet, S., Larkin, C., Lucey, B. M., Meegan, A., & Yarovaya, L. (2020). The impact of macroeconomic news on Bitcoin returns. The European Journal of Finance, 26(14), 1396-1416.
- Diaconaşu, D. E., Mehdian, S., &Stoica, O. (2022). An analysis of investors’ behavior in Bitcoin market. PloS one, 17(3), e0264522.
- Duarte, A. P., Murta, F. S., da Silva, N. B., & Vieira, B. R. (2023). Flip the coin: Heads, tails or cryptocurrencies? Scientific Annals of Economics and Business, 70(SI), 1-18.
- El Alaoui, M., Bouri, E., & Roubaud, D. (2019). Bitcoin price–volume: A multifractal cross-correlation approach. Finance Research Letters, 31.
- Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The digital traces of bubbles: Feedback cycles between socio-economic signals in the bitcoin economy. Journal of the Royal Society Interface, 11, 1-8.
- Gemici, E., & Polat, M. (2019). Relationship between price and volume in the Bitcoin market. Journal of Risk Finance, 20(5), 435-446.
- Gemici, E., & Polat, M. (2021). Causality-in-mean and causality-in-variance among Bitcoin, Litecoin, and Ethereum. Studies in Economics and Finance, 38(4), 861-872.
- Ghorbel, A., Loukil, S., & Bahloul, W. (2022). Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic. European Journal of Management and Business Economics (ahead-of-print).
- Giudici, P., & Hashish, I. A. (2019). What determines Bitcoin exchange prices? A network VAR approach. Finance Research Letters, 28, 309-318.
- Guizani, S., & Nafti, I. K. (2019). The determinants of Bitcoin price volatility: An investigation with ardl model. Procedia Computer Science, 164, 233-238.
- Gul, M., Hashim, S., & Hayat, A. (2023). The impact of macroeconomic indicators on bitcoin: a case study on pakistan. Journal of Social Research Development, 4(2), 400-409.
- Gurrib, I., & Kamalov, F. (2022). Predicting bitcoin price movements using sentiment analysis: a machine learning approach. Studies in Economics and Finance, 39(3), 347-364.
- Harm, J.,Obregon, J., & Stubbendick, J. (2016). Ethereum vs. Bitcoin.
- Hileman, D., & Rauchs, M. (2017). Global cryptocurrency bencmarking study. England: Cambridge Center for Alternative Finance.
- Hung, N. T. (2022). Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin. Managerial Finance.
- Husaini, S. M., Ahmad, Z., & Lai, Y. W. (2011). The Role of Macroeconomic Variables on Stock Market Index in China and India. International Journal of Economics and Finance, 3, 6.
- Inshyn, M., Mohilevskyi, L., & Drozd, O. (2018). The issue of cryptocurrency legal regulation in Ukraine and all over the world: a comparative analysis. Baltic Journal of Economic Studies,4(1), 169-174.
- Ismail, S., & Basah, M. A. (2021). An Analysis On Cryptocurrencies And Macroeconomic Variables Using Vector Error Correction Model (Vecm). ASEAN Journal of Management and Business Studies, 3(1), 8-15.
- Jakub, B. (2015). Does Bitcoin follow the hypothesis of efficient market. International Journal of Economic Sciences, 4(2).
- Joo, M. H., Nishikawa, Y., & Dandapani, K. (2019). Cryptocurrency, a succesful application of blockchain technology. Managerial Finance, 46(6), 715-733.
- Kapar, B., & Olmo, J. (2021). Analysis of Bitcoin prices using market and sentiment variables. The World Economy, 44(1), 45-63.
- Karalevocious, V., Degrande, N., & Weerdt, J. D. (2018). Using sentiment analysis to predict interday Bitcoin price movements. The Journal of Risk Finance, 19(1), 56-75.
- Kim, T. (2017). On the transaction cost of Bitcoin. Finance Research Letters, 23, 300-305.
- Kjærland, F., Meland, M., Oust, A., & Øyen, V. (2018). How can Bitcoin Price Fluctuations be Explained? International Journal of Economics and Financial Issues, 8(3), 323-332.
- Kristoufek, L. (2018). On Bitcoin markets (in) efficiency and its evolution. Physica A: Statistical Mechanics and Its Applications, 503, 257-262.
- Lee, A. D., Li, M., & Zheng, H. (2020). Bitcoin: Speculative asset or innovative technology?. Journal of International Financial Markets, Institutions and Money, 67, 101209.
- Lestari, R. (2020). Analysis of stock market integration among ASEAN countries by using vector error correction model (VECM) approach. In Proceeding of Japan International Business and Management Research Conference, 1(1), 69-77.
- Liu, Y., Su, M., Zhao, J., Martin, S., Yuen, K. F., & Lee, C. B. (2022). The determinants of China’s outward foreign direct investment: a vector error correction model analysis of coastal and landlocked countries. Economic Change and Restructuring, 1-28.
- Lucking, D., & Aravind, V. (2019). Cryptocurrency as a commodity: The CFTC’s Regulatory Framework. Global Legal Insights.
- Luther, W. J., & Smith, S. S. (2020). Is Bitcoin a decentralized payment mechanism? Journal of Institutional Economics, 16(4), 433-444.
- Mensi, W., Gubareva, M., Ko, H. U., Vo, X. V., & Kang, S. H. (2023). Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets. Financial Innovation, 9(1).
- Mizerka, J.,Stróżyńska-Szajek, A., &Mizerka,P.(2020). The role of Bitcoin on developed and emerging markets – on the basis of a Bitcoin users graph analysis. Finance Research Letter, 02-08.
- Mohd Thas Thaker, H., & Ah Mand, A. (2021). Bitcoin and stock markets: A revisit of relationship. Journal of Derivatives and Quantitative Studies, 29(3), 234-256.
- Nakamoto, S. (2008). Bitcoin: Peer-to-Peer Electronic Cash System.
- Nugroho, W. S., Astuti, A. B., & Astutik, S. (2021, March). Vector Error Correction Model to Forecasting Spot Prices for Coffee Commodities During Covid-19 Pandemic. Journal of Physics: Conference Series, 1811(1), 012076.
- Nurwulandari, A., Hasanudin, H., & Budi, A. J. S. (2021). Analysis of the Influence of Interest Rate, Exchange Value, World Gold Prices, Dow Jones Index, AEX Index, DAX Index, and Shanghai Index on LQ45 Index in Indonesia Stock Exchange 2012–2018. JABE (Journal of Applied Business and Economic), 7(2), 135-147.
- Onur, C., & Yurdakul, A. (2022). ElectAnon: A Blockchain-Based, Anonymous, Robust and Scalable Ranked-Choice Voting Protocol. Cryptography and Security (cs.CR) of Cornell University.
- Platanakis, E., & Urquhart, A. (2019). Should investor include Bitcoin in their portfolio? A portfolio theory approach. The British Accounting Review, 5(2), 100837.
- Poyser, O. (2017). Exploring the determinants of Bitcoin’s price: an application of Bayesian Structural Time Series. General Economics (econ.GN) of Cornell University.
- Qudah, A. A., & Aloulou, M. (2020). Empirical Test for the Relationship between the Bitcoin Using Historical Data with (Inflation Rate, Foreign Trade And GDP) and the Possibility to Use the Bitcoin as Hedge against Inflation: Evidence from GCC Countries. International Journal of Scientific & Technology Research, 9(2), 2277-8616.
- Rudolf, K. O., Ajour El Zein, S., & Lansdowne, N. J. (2021). Bitcoin as an investment and hedge alternative. A DCC MGARCH model analysis. Risks, 9(9), 154.
- Shabbir, A., Kousar, S., & Batool, S. A. (2019). Impact of gold and oil prices on the stock market in Pakistan. Journal of Economics, Finance and Administrative Science, 25(50).
- Si, R., Aziz, N., & Raza, A. (2021). Short and long-run causal effects of agriculture, forestry, and other land use on greenhouse gas emissions: Evidence from China using VECM approach. Environmental Science and Pollution Research, 28(45), 64419-64430.
- Sifat, I. M., Mohammad, A., & Mohammed Shariff, M. B. (2019). Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data. International Business and Finance, 50, 306-321.
- Singh, A., & Krishna, P. V. (2022). A new investment opportunity: Bitcoin & ethereum cryptocurrency. International Journal of Scientific Research in Engineering and Management, 6(10).
- Tarchella, S., Khalfaoui, R., & Hammoudeh, S. (2023). The safe haven, hedging, and diversification properties of oil, gold, and cryptocurrency for the G7 equity markets: Evidence from the pre-and post-COVID-19 periods. Research in International Business and Finance, 102125.
- Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. (2019). Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy, 21(6), 589.
- Vejacka, M. (2014). Basic Aspecs of Cryptocurrency. Journal of Economy, Business and Financing, 4(2), 75-83.
- Vujičić, D., Jagodić, D., &Ranđić, S. (2018). Blockchain technology, Bitcoin, and Ethereum: A brief overview. In 2018 17th international symposium infoteh-jahorina (infoteh) (pp. 1-6). IEEE.
- Wang, J., Xue, Y., & Liu, M. (2016). An analysis of Bitcoin price based on VEC model. In 2016 International conference on economics and management innovations (pp. 180-186). Atlantis Press.
- Wong, W. S., Saerbeck, D., & Delgado Silva, D. (2018, January 29). Cryptocurrency: A new investment opportunity? An investigation of the hedging capability of cryptocurrencies and their influence on stock, bond and gold portfolios.
- Yang, C., Wang, X., & Gao, W. (2022). Is Bitcoin a better hedging and safe-haven investment than traditional assets against currencies? Evidence from the time-frequency domain approach. The North American Journal of Economics and Finance, 62, 101747.