Araştırma Makalesi
Alternatif Bir Finansal Varlık Sınıfı Olarak Bitcoin: Jeopolitik Risk, Küresel Ekonomik Politik Belirsizlik ve Enerji Tüketimi Arasındaki İlişkiler
Öz
Bu araştırmanın amacı, Küresel Ekonomik Politik Belirsizlik (GEPU) ile Jeopolitik Risk (GPRT) ve Bitcoin Enerji Tüketimi (BTCE) arasındaki nedenselliği araştırmaktır. Değişkenlerin durağanlığını test etmek için yapısal kırılmaları dikkate alan Lee-Strazich birim kök testi kullanılmış ve değişkenler arasındaki nedensellik ilişkisi Hatemi-J (2012) nedensellik testi ile analiz edilmiştir. Araştırmada Mayıs 2011 ile Şubat 2022 arasındaki aylık veriler kullanılmıştır. Araştırmadan elde edilen sonuçlara göre, jeopolitik risk ve küresel ekonomik politika belirsizliği bitcoin enerji tüketimi üzerinde etkilidir. Ayrıca jeopolitik risk ve küresel belirsizliklerin negatif etkilerinin daha baskın olduğu tespit edilmiştir. Sonuçlar, alternatif bir finansal varlık sınıfı olarak kabul edilen bitcoin talebinin ve buna bağlı olarak bitcoin enerji tüketiminin küresel riskler ve ekonomik belirsizlikler durumunda arttığını göstermektedir.
Anahtar Kelimeler
Bitcoin Finansal varlık Küresel ekonomik politika belirsizliği Jeopolitik risk Geopolitical risk Energy consumption
Destekleyen Kurum
Bulunmamaktadır.
Proje Numarası
Bulunmamaktadır.
Kaynakça
- Abbasi, K. R., Hussain, K., Abbas, J., Adedoyin, F. F., Shaikh, P. A., Yousaf, H., & Muhammad, F. (2021). Analyzing the Role of Industrial Sector's Electricity Consumption, Prices, and GDP: A Modified Empirical Evidence from Pakistan. Aims Energy, 9(1), 29-49.
- Antonakakis, N., Gupta, R., Kollias, C., & Papadamou, S. (2017). Geopolitical risks and the oil-stock nexus over 1899–2016. Finance Research Letters, 23, 165-173.
- Aysan, A. F., Demir, E., Gozgor, G., & Lau, C. K. M. (2019). Effects of the geopolitical risks on Bitcoin returns and volatility. Research in International Business and Finance, 47, 511-518.
- Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636. doi:10.1093/qje/qjw024.
- Bal, D. P., Patra, S. K., & Mohanty, S. (2022). Impact of Sectoral Decompositions of Electricity Consumption on Economic Growth in India: Evidence from SVAR Framework. Environmental Science and Pollution Research, 1-13.
- BinanceAcademy. (2021). An Overview of Bitcoin Price History. Retrieved from https://academy.binance.com/en/articles/an-overview-of-bitcoin-s-price-history. Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95.
- D. Caldara, M. Iacoviello, Measuring geopolitical risk. working paper, 2016, https://www2.bc.edu/matteo-iacoviello/gpr_files/GPR_PAPER.pdf.
- Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194-1225. doi: 10.1257/aer.20191823.
- Corbet, S., Lucey, B. M., & Yarovaya, L. (2019). The financial market effects of cryptocurrency energy usage. Available at SSRN. https://doi.org/10.2139/ssrn.3412194.
- Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
- Das, D., & Dutta, A. (2020). Bitcoin’s energy consumption: Is it the Achilles heel to miner’s revenue? Economics Letters, 186, 108530.
- Das, D., Le Roux, C. L., Jana, R. K., & Dutta, A. (2020). Does Bitcoin hedge crude oil implied volatility and structural shocks? A comparison with gold, commodity and the US Dollar. Finance Research Letters, 36, 101335.
- Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 49(4), 1057-1072. doi:10.2307/1912517.
- Dyhrberg, A.H., Foley, S. & Svec, J. (2018). How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets. Economic Letters, 171, 140-143.
- Gallersdörfer, U., Klaaßen, L., & Stoll, C. (2020). Energy consumption of cryptocurrencies beyond bitcoin. Joule, 4(9), 1843-1846. doi: 10.1016/j.joule.2020.07.013.
- Granger, C.W. (1969), “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods”, Econometrica: Journal of the Econometric Society, 37(3), 424-438.
- Gurdgiev, C., & O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of Behavioral and Experimental Finance, 25, 100271. doi: 10.1016/j.jbef.2020.100271.
- Gürsoy, S. (2021). Küresel Ekonomik Politik Belirsizliğin (GEPU) Döviz Kuru, Enflasyon ve Borsa Etkisi: Türkiyeden Kanıtlar. Türkiye Mesleki ve Sosyal Bilimler Dergisi, (5), 120-131. Doi: 10.46236/jovosst.877608
- Hatemi-J, A. (2012). Asymmetric Causality Tests With An Application. Empirical Economics, 43(1), 447–456. doi:10.1007/s00181-011-0484-x.
- Huh, S., Cho, S., & Kim, S. (2017, February). Managing IoT devices using blockchain platform. In 2017 19th international conference on advanced communication technology (ICACT) (pp. 464-467). IEEE.
- Huynh, A. N. Q., Duong, D., Burggraf, T., Luong, H. T. T., & Bui, N. H. (2022). Energy consumption and Bitcoin market. Asia-Pacific Financial Markets, 29(1), 79-93.
- Kartal, M. T. (2022). The Role of Consumption of Energy, Fossil Sources, Nuclear Energy, and Renewable Energy on Environmental Degradation in Top-Five Carbon Producing Countries. Renewable Energy, 184, 871-880.
- Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future generation computer systems, 82, 395-411. doi.org/10.1016/j.future.2017.11.022.
- Krause, M. J., & Tolaymat, T. (2018). Quantification of energy and carbon costs for mining cryptocurrencies, Nat. Sustain., 1, 711–718.
- Küfeoğlu, S., & Özkuran, M. (2019). Bitcoin mining: A global review of energy and power demand. Energy Research & Social Science, 58, 101273.
- Kyriazis, Ν. A. (2021). The effects of geopolitical uncertainty on cryptocurrencies and other financial assets. SN Business & Economics, 1(1), 1-14.
- Lee, J. ve Strazicich, M. C. (2003). Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks. Review of Economics and Statistics, 85(4), 1082–1089. doi:10.1162/003465303772815961
- Lee, J. ve Strazicich, M. C. (2004). Minimum LM Unit Root Test with One Structural Break. Appalachian State University Working Papers, 4(17), 1–15.
- Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system. Technical report, Manubot.
- Ng, S. ve Perron, P. (2001). LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519–1554. doi:10.1111/1468-0262.00256
- Phillips, P. ve Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. doi:10.1093/biomet/75.2.335.
- Rahaman, M., Hossain, M., & Chen, S. (2022). The Impact of Foreign Direct Investment, Tourism, Electricity Consumption, and Economic Development on CO2 Emissions in Bangladesh. Environmental Science and Pollution Research, 29(25), 37344-37358.
- Soytaş, M. A., Ertuğrul, H. M., & Ulussever, T. (2020). Nonlinear Excess Demand Model for Electricity Price Prediction. Economic Research Forum Working Papers, No. 1449.
- Toda, H.Y. ve Yamamoto, T. (1995), “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes”, Journal of Econometrics, 66(1), 225-250.
- Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. doi: 10.1016/j.econlet.2016.09.019.
- https://digiconomist.net/bitcoin-energy-consumption (05.05.2022)
Bitcoin as an Alternative Financial Asset Class: Relations Between Geopolitical Risk, Global Economic Political Uncertainty, and Energy Consumption
Öz
The aim of this research is to investigate the causality between Global Economic Political Uncertainty (GEPU) and Geopolitical Risk (GPRT) and Bitcoin Energy Consumption (BTCE). In order to test the stationarity of the variables, the Lee-Strazich unit root test, which takes into account the structural breaks, was used, and the causality relationship between the variables was analyzed with the Hatemi-J (2012) causality test. Monthly data between May 2011 and February 2022 were used in the research. According to the results obtained from the research, geopolitical risk and global economic policy uncertainity are effective on bitcoin energy consumption. In addition, it has been determined that the negative effects of geopolitical risk and global uncertainties are more dominant. The results show that the demand for bitcoin, which is considered an alternative financial asset class, and accordingly bitcoin energy consumption, increases in case of global risks and economic uncertainties.
Anahtar Kelimeler
Proje Numarası
Bulunmamaktadır.
Kaynakça
- Abbasi, K. R., Hussain, K., Abbas, J., Adedoyin, F. F., Shaikh, P. A., Yousaf, H., & Muhammad, F. (2021). Analyzing the Role of Industrial Sector's Electricity Consumption, Prices, and GDP: A Modified Empirical Evidence from Pakistan. Aims Energy, 9(1), 29-49.
- Antonakakis, N., Gupta, R., Kollias, C., & Papadamou, S. (2017). Geopolitical risks and the oil-stock nexus over 1899–2016. Finance Research Letters, 23, 165-173.
- Aysan, A. F., Demir, E., Gozgor, G., & Lau, C. K. M. (2019). Effects of the geopolitical risks on Bitcoin returns and volatility. Research in International Business and Finance, 47, 511-518.
- Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636. doi:10.1093/qje/qjw024.
- Bal, D. P., Patra, S. K., & Mohanty, S. (2022). Impact of Sectoral Decompositions of Electricity Consumption on Economic Growth in India: Evidence from SVAR Framework. Environmental Science and Pollution Research, 1-13.
- BinanceAcademy. (2021). An Overview of Bitcoin Price History. Retrieved from https://academy.binance.com/en/articles/an-overview-of-bitcoin-s-price-history. Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95.
- D. Caldara, M. Iacoviello, Measuring geopolitical risk. working paper, 2016, https://www2.bc.edu/matteo-iacoviello/gpr_files/GPR_PAPER.pdf.
- Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194-1225. doi: 10.1257/aer.20191823.
- Corbet, S., Lucey, B. M., & Yarovaya, L. (2019). The financial market effects of cryptocurrency energy usage. Available at SSRN. https://doi.org/10.2139/ssrn.3412194.
- Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
- Das, D., & Dutta, A. (2020). Bitcoin’s energy consumption: Is it the Achilles heel to miner’s revenue? Economics Letters, 186, 108530.
- Das, D., Le Roux, C. L., Jana, R. K., & Dutta, A. (2020). Does Bitcoin hedge crude oil implied volatility and structural shocks? A comparison with gold, commodity and the US Dollar. Finance Research Letters, 36, 101335.
- Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 49(4), 1057-1072. doi:10.2307/1912517.
- Dyhrberg, A.H., Foley, S. & Svec, J. (2018). How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets. Economic Letters, 171, 140-143.
- Gallersdörfer, U., Klaaßen, L., & Stoll, C. (2020). Energy consumption of cryptocurrencies beyond bitcoin. Joule, 4(9), 1843-1846. doi: 10.1016/j.joule.2020.07.013.
- Granger, C.W. (1969), “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods”, Econometrica: Journal of the Econometric Society, 37(3), 424-438.
- Gurdgiev, C., & O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of Behavioral and Experimental Finance, 25, 100271. doi: 10.1016/j.jbef.2020.100271.
- Gürsoy, S. (2021). Küresel Ekonomik Politik Belirsizliğin (GEPU) Döviz Kuru, Enflasyon ve Borsa Etkisi: Türkiyeden Kanıtlar. Türkiye Mesleki ve Sosyal Bilimler Dergisi, (5), 120-131. Doi: 10.46236/jovosst.877608
- Hatemi-J, A. (2012). Asymmetric Causality Tests With An Application. Empirical Economics, 43(1), 447–456. doi:10.1007/s00181-011-0484-x.
- Huh, S., Cho, S., & Kim, S. (2017, February). Managing IoT devices using blockchain platform. In 2017 19th international conference on advanced communication technology (ICACT) (pp. 464-467). IEEE.
- Huynh, A. N. Q., Duong, D., Burggraf, T., Luong, H. T. T., & Bui, N. H. (2022). Energy consumption and Bitcoin market. Asia-Pacific Financial Markets, 29(1), 79-93.
- Kartal, M. T. (2022). The Role of Consumption of Energy, Fossil Sources, Nuclear Energy, and Renewable Energy on Environmental Degradation in Top-Five Carbon Producing Countries. Renewable Energy, 184, 871-880.
- Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future generation computer systems, 82, 395-411. doi.org/10.1016/j.future.2017.11.022.
- Krause, M. J., & Tolaymat, T. (2018). Quantification of energy and carbon costs for mining cryptocurrencies, Nat. Sustain., 1, 711–718.
- Küfeoğlu, S., & Özkuran, M. (2019). Bitcoin mining: A global review of energy and power demand. Energy Research & Social Science, 58, 101273.
- Kyriazis, Ν. A. (2021). The effects of geopolitical uncertainty on cryptocurrencies and other financial assets. SN Business & Economics, 1(1), 1-14.
- Lee, J. ve Strazicich, M. C. (2003). Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks. Review of Economics and Statistics, 85(4), 1082–1089. doi:10.1162/003465303772815961
- Lee, J. ve Strazicich, M. C. (2004). Minimum LM Unit Root Test with One Structural Break. Appalachian State University Working Papers, 4(17), 1–15.
- Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system. Technical report, Manubot.
- Ng, S. ve Perron, P. (2001). LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519–1554. doi:10.1111/1468-0262.00256
- Phillips, P. ve Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. doi:10.1093/biomet/75.2.335.
- Rahaman, M., Hossain, M., & Chen, S. (2022). The Impact of Foreign Direct Investment, Tourism, Electricity Consumption, and Economic Development on CO2 Emissions in Bangladesh. Environmental Science and Pollution Research, 29(25), 37344-37358.
- Soytaş, M. A., Ertuğrul, H. M., & Ulussever, T. (2020). Nonlinear Excess Demand Model for Electricity Price Prediction. Economic Research Forum Working Papers, No. 1449.
- Toda, H.Y. ve Yamamoto, T. (1995), “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes”, Journal of Econometrics, 66(1), 225-250.
- Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. doi: 10.1016/j.econlet.2016.09.019.
- https://digiconomist.net/bitcoin-energy-consumption (05.05.2022)
Toplam 36 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | İşletme |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | Bulunmamaktadır. |
| Yayımlanma Tarihi | 31 Aralık 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 9 Sayı: 2 |
Kaynak Göster
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