Research Article
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Year 2022, Volume: 10 Issue: 2, 69 - 84, 31.12.2022
https://doi.org/10.17093/alphanumeric.941529

Abstract

References

  • Abioglu Y., (2021). “Volatility Spillovers and Correlations between Oil Prices and Stock Sectors in Turkey: Implications on Portfolio Hedging and Diversification Opportunities”, Sosyoekonomi, 29(47), 79-106.
  • Akcali, B.Y. (2020). “The Analysis of Volatility Spillovers Between Borsa Istanbul and Global Market Indicators By DCC-GARCH Method”, Eskisehir Osmangazi Universitesi IIBF Dergisi, Aralik 2020, 14 (3), 597-614.
  • Bhowmik, D. (2013). “Stock Market Volatility: An Evaluation”, International Journal of Scientific and Research Publications, 3(10), 1-18.
  • Bouri, E., Gabauer, D., Gupta, R., Tiwari, A.K. (2021). “Volatility Connectedness of Major Cryptocurrencies: The Role of Investor Happiness”, Elsevier, Journal of Behavioral and Experimental Finance, 30, 100463.
  • Burggraf, T. & Rudolf, M. (2021), “Cryptocurrencies and The Low Volatility Anomaly”, Elsevier, FinanceResearch Letters, 40, 101683.
  • Bohme, R., Christin, N., Edelman, B., Moore, T. (2015). “Bitcoin: Economics, Technology, and Governance”, Journal of Economic Perspectives, 29(2), 213-238.
  • Cappiello, L., R.F. Engle, K. Sheppard (2006), “Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns”, Journal of Financial Econometrics, 4(4), 537-572.
  • Ceylan, F., Ekinci, R., Tuzun, O., Kahyaoglu, H. (2018). “Determination of Bubbles in Cryptocurrencies Market: Bitcoin and Ethereum”, BMIJ, Business & Management Studies: An International Journal, 6(3), 263-274.
  • Chang, K., Ye, Z., Wang, W. (2019). “Volatility Spillover Effect and Dynamic Correlation Between Regional Emissions Allowances and Fossil Energy Markets: New Evidence from China’s Emissions Trading Scheme Pilots”, Energy, 185, 1314–1324.
  • Chu, J., Chan, S., Nadarajah, S., Osterrieder, J. (2017). “GARCH Modelling of Cryptocurrencies”, Journal of Risk and Financial Management, 10(17), 2-15.
  • Engle, R. (2002). “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models”, Journal of Business & Economic Statistics, 20(3), 339-350.
  • Ertugrul, M. (2019). “Investigation of Volatility Dynamics of Cryptocurrencies: An Applicatıon on GARCH Models”, Journal of Management and Economics Research, 17 (4), 59-71.
  • European Central Bank. (2019), “Crypto-Assets: Implications for Financial Stability, Monetary Policy, and Payments and Market Infrastructures”, ECB Occasional Paper Series, 223, 23-37.
  • Fama, E. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work”, The Journal of Finance, Vol.25, No.2, 383-417.
  • Hongsakulvasu, N., Khiewngamdee, C., Liammukda A., (2020). “Does COVID-19 Crisis Affects the Spillover of Oil Market’s Return and Risk on Thailand’s Sectoral Stock Return?: Evidence from Bivariate DCC GARCH-in- Mean Model” International Energy Journal, 20 (2020) 647 – 662
  • Im, Kyung So., Lee, Junsoon., Tieslau, Margie A. (2014). “More Powerful Unit Root Test With Non-normal Errors”, Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications, New York, Springer New York, 315-342.
  • Katsiampa, P., Corbet, S., Lucey, B. (2019). “High frequency volatility co-movements in cryptocurrency markets”, Elsevier, Journal of International Financial Markets, Institutions & Money, 62, 35-52.
  • Katsiampa, P. (2019). “An empirical investigation of volatility dynamics in the cryptocurrency market”, Elsevier, Research in International Business and Finance, 50, 322-335.
  • Katsiampa, P. (2017). “Volatility estimation for Bitcoin: A comparison of GARCH models”, Economics Letters, 158, 3-6.
  • Kumar, A.S., & Anandarao, S. (2019). “Volatility Spillover in Crypto-currency Markets: Some Evidences from GARCH and Wavelet Analysis”, Elsevier, Physica A, 524, 448-458.
  • Kayral, I.E. (2020). “Volatility Estimation For Three Cryptocurrencies With The Highest Market Cap”, Finansal Araştırmalar ve Çalışmalar Dergisi, 12(22), 152-168.
  • Palamalai, S., and Maity, B. (2019). “Return And Volatility Spillover Effects in Leading Cryptocurrencies”, Global Economy Journal, 19(3), 1-20.
  • Naeem, M. A., Bouri, E., Peng, Z., Shahzad, S. J. H., & Vo, X. V. (2021). “Asymmetric Efficiency of Cryptocurrencies During COVID19”, Physica A: Statistical Mechanics and Its Applications, 565, 1-12.
  • Naimy, V.Y. & Hayek, M.R. (2018). “Modelling and predicting the Bitcoin volatility using GARCH models”, 8(3), 197-215.
  • Nelson, D. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, Vol. 59, No. 2, 347-370.
  • Palamalai, S. & Maity, B. (2019). “Return and Volatility Spillover Effects in Leading Cryptocurrencies”, Global Economy Journal, 19(3), 1-20.
  • Soylemez, Y. (2020). “Analysis of Bitcoin Volatility with Generalized Autoregressive Conditional Heteroskedastic Models”, Journal of Business Research-Turk, 12(2), 1322-1333.
  • Wang, J. & Ngene, G.M. (2020). “Does Bitcoin still own the dominant power? An intraday analysis”, Elsevier, International Review of Financial Analysis, 71, 1-12.
  • https://tr.investing.com/crypto/currencies

The Characteristics of Cryptocurrency Market Volatility: Empirical Study For Five Cryptocurrency

Year 2022, Volume: 10 Issue: 2, 69 - 84, 31.12.2022
https://doi.org/10.17093/alphanumeric.941529

Abstract

In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions.
This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.

References

  • Abioglu Y., (2021). “Volatility Spillovers and Correlations between Oil Prices and Stock Sectors in Turkey: Implications on Portfolio Hedging and Diversification Opportunities”, Sosyoekonomi, 29(47), 79-106.
  • Akcali, B.Y. (2020). “The Analysis of Volatility Spillovers Between Borsa Istanbul and Global Market Indicators By DCC-GARCH Method”, Eskisehir Osmangazi Universitesi IIBF Dergisi, Aralik 2020, 14 (3), 597-614.
  • Bhowmik, D. (2013). “Stock Market Volatility: An Evaluation”, International Journal of Scientific and Research Publications, 3(10), 1-18.
  • Bouri, E., Gabauer, D., Gupta, R., Tiwari, A.K. (2021). “Volatility Connectedness of Major Cryptocurrencies: The Role of Investor Happiness”, Elsevier, Journal of Behavioral and Experimental Finance, 30, 100463.
  • Burggraf, T. & Rudolf, M. (2021), “Cryptocurrencies and The Low Volatility Anomaly”, Elsevier, FinanceResearch Letters, 40, 101683.
  • Bohme, R., Christin, N., Edelman, B., Moore, T. (2015). “Bitcoin: Economics, Technology, and Governance”, Journal of Economic Perspectives, 29(2), 213-238.
  • Cappiello, L., R.F. Engle, K. Sheppard (2006), “Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns”, Journal of Financial Econometrics, 4(4), 537-572.
  • Ceylan, F., Ekinci, R., Tuzun, O., Kahyaoglu, H. (2018). “Determination of Bubbles in Cryptocurrencies Market: Bitcoin and Ethereum”, BMIJ, Business & Management Studies: An International Journal, 6(3), 263-274.
  • Chang, K., Ye, Z., Wang, W. (2019). “Volatility Spillover Effect and Dynamic Correlation Between Regional Emissions Allowances and Fossil Energy Markets: New Evidence from China’s Emissions Trading Scheme Pilots”, Energy, 185, 1314–1324.
  • Chu, J., Chan, S., Nadarajah, S., Osterrieder, J. (2017). “GARCH Modelling of Cryptocurrencies”, Journal of Risk and Financial Management, 10(17), 2-15.
  • Engle, R. (2002). “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models”, Journal of Business & Economic Statistics, 20(3), 339-350.
  • Ertugrul, M. (2019). “Investigation of Volatility Dynamics of Cryptocurrencies: An Applicatıon on GARCH Models”, Journal of Management and Economics Research, 17 (4), 59-71.
  • European Central Bank. (2019), “Crypto-Assets: Implications for Financial Stability, Monetary Policy, and Payments and Market Infrastructures”, ECB Occasional Paper Series, 223, 23-37.
  • Fama, E. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work”, The Journal of Finance, Vol.25, No.2, 383-417.
  • Hongsakulvasu, N., Khiewngamdee, C., Liammukda A., (2020). “Does COVID-19 Crisis Affects the Spillover of Oil Market’s Return and Risk on Thailand’s Sectoral Stock Return?: Evidence from Bivariate DCC GARCH-in- Mean Model” International Energy Journal, 20 (2020) 647 – 662
  • Im, Kyung So., Lee, Junsoon., Tieslau, Margie A. (2014). “More Powerful Unit Root Test With Non-normal Errors”, Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications, New York, Springer New York, 315-342.
  • Katsiampa, P., Corbet, S., Lucey, B. (2019). “High frequency volatility co-movements in cryptocurrency markets”, Elsevier, Journal of International Financial Markets, Institutions & Money, 62, 35-52.
  • Katsiampa, P. (2019). “An empirical investigation of volatility dynamics in the cryptocurrency market”, Elsevier, Research in International Business and Finance, 50, 322-335.
  • Katsiampa, P. (2017). “Volatility estimation for Bitcoin: A comparison of GARCH models”, Economics Letters, 158, 3-6.
  • Kumar, A.S., & Anandarao, S. (2019). “Volatility Spillover in Crypto-currency Markets: Some Evidences from GARCH and Wavelet Analysis”, Elsevier, Physica A, 524, 448-458.
  • Kayral, I.E. (2020). “Volatility Estimation For Three Cryptocurrencies With The Highest Market Cap”, Finansal Araştırmalar ve Çalışmalar Dergisi, 12(22), 152-168.
  • Palamalai, S., and Maity, B. (2019). “Return And Volatility Spillover Effects in Leading Cryptocurrencies”, Global Economy Journal, 19(3), 1-20.
  • Naeem, M. A., Bouri, E., Peng, Z., Shahzad, S. J. H., & Vo, X. V. (2021). “Asymmetric Efficiency of Cryptocurrencies During COVID19”, Physica A: Statistical Mechanics and Its Applications, 565, 1-12.
  • Naimy, V.Y. & Hayek, M.R. (2018). “Modelling and predicting the Bitcoin volatility using GARCH models”, 8(3), 197-215.
  • Nelson, D. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, Vol. 59, No. 2, 347-370.
  • Palamalai, S. & Maity, B. (2019). “Return and Volatility Spillover Effects in Leading Cryptocurrencies”, Global Economy Journal, 19(3), 1-20.
  • Soylemez, Y. (2020). “Analysis of Bitcoin Volatility with Generalized Autoregressive Conditional Heteroskedastic Models”, Journal of Business Research-Turk, 12(2), 1322-1333.
  • Wang, J. & Ngene, G.M. (2020). “Does Bitcoin still own the dominant power? An intraday analysis”, Elsevier, International Review of Financial Analysis, 71, 1-12.
  • https://tr.investing.com/crypto/currencies
There are 29 citations in total.

Details

Primary Language English
Subjects Operation
Journal Section Articles
Authors

İlayda İsabetli Fidan 0000-0003-2739-9018

Tuğba Güz 0000-0003-1644-7803

Publication Date December 31, 2022
Submission Date May 23, 2021
Published in Issue Year 2022 Volume: 10 Issue: 2

Cite

APA İsabetli Fidan, İ., & Güz, T. (2022). The Characteristics of Cryptocurrency Market Volatility: Empirical Study For Five Cryptocurrency. Alphanumeric Journal, 10(2), 69-84. https://doi.org/10.17093/alphanumeric.941529

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