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Bitcoin İle Değerli Metaller Arasında Dinamik Stokastik Volatilite Yayılımı

Yıl 2025, Cilt: 9 Sayı: 1, 53 - 72
https://doi.org/10.29216/ueip.1596577

Öz

Bitcoin’in 2008'de yaratılmasından beri güvenli liman, riskten korunma ve risk varlıklarının çeşitlendirilmesi gibi kripto para birimleri ve değerli metallerin paylaştığı çeşitli ortak özellikler geniş çapta tartışma konusu olmuştur. Bitcoin getirileri ile değerli metaller olan altın, bakır, gümüş ve platin getirileri arasındaki dinamik koşullu korelasyonları ve volatilite yayılımlarını analiz eden bu çalışmada DCC- GARCH modeli kullanılmıştır. Bitcoin getirileri ile değerli metaller olan altın, bakır, gümüş ve platin getirilerinin tüm modellerde volatilitelerinin kalıcı olduğu, incelenen tüm getiri serilerinde volatilite kümelenmesinin oluştuğu gözlemlenmiştir. Altın piyasasından Bitcoin piyasasına doğru tek yönlü volatilite aktarımına karşın, Bitcoin piyasasından bakır, gümüş ve platin piyasalarına doğru tek yönlü volatilite aktarımı bulunmuştur. Dinamik koşullu korelasyonlarda Bitcoin ile altın piyasalarında altın piyasası için, Bitcoin ve aakır piyasalarında Bitcoin ve aakır için anlamlı sonuçlar çıkmıştır. Bitcoin ve gümüş ile Bitcoin ve platin piyasaları arasında dinamik koşullu korelasyonlara rastlanmamıştır.

Kaynakça

  • Akçalı, B. Y., Mollaahmetoğlu, E. and Altay, E. (2019). Borsa İstanbul ve Küresel Piyasa Göstergeleri Arasındaki Volatilite Etkileşiminin DCC-GARCH Yöntemi ile Analizi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 14(3), 597-614.
  • Akkus, H. T., Gursoy, S. and Dogan, M. (2022). The Volatility Spillover Between NFT Investment Index and Global Technology Index: DCC-GARCH Application. Research Journal of Business and Management, 9(2), 85-91.
  • Apostolakis, G. N. (2024). Bitcoin Price Volatility Transmission Between Spot and Futures Markets. International Review of Financial Analysis, 103251.
  • Baek, C. and Elbeck, M. (2015). Bitcoins as an Investment or Speculative Vehicle? A First Look, Applied Economics Letters, 22(1), 30-34.
  • Bala, D. A. and Takimoto, T. (2017). Stock Markets Volatility Spillovers During Financial Crises: A DCC-MGARCH with Skewed-t Density Approach. Borsa Istanbul Review, 17(1), 25-48.
  • Baumöhl, E. and Lyócsa, Š. (2014). Volatility and Dynamic Conditional Correlations of Worldwide Emerging and Frontier Markets. Economic Modelling, 38, 175-183.
  • Baur, D. G. and Smales, L. A. (2020). Hedging Geopolitical Risk with Precious Metals. Journal of Banking & Finance, 117, 105823.
  • Baur, D. G., Hong, K. and Lee, A. D. (2018). Bitcoin: Medium of Exchange or Speculative Assets?, Journal of International Financial Markets, Institutions and Money, 54, 177-189.
  • Baur, D.G. and Lucey, B.M. (2010). Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold. Financial Rev. 45 (2), 217–229.
  • Bianchi, D., Guidolin, M. and Pedio, M. (2023). The Dynamics of Returns Predictability in Cryptocurrency Markets. The European Journal of Finance, 29(6), 583-611.
  • Bilgin, M.H., Gozgor, G., Lau, C.K.M. and Sheng, X. (2018). The Effects of Uncertainty Measures on the Price of Gold. Int. Rev. Financ. Anal. 58, 1–7.
  • Bollerslev T., (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 498-505.
  • Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992). ARCH Modeling in Finance: A Review of The Theory and Empirical Evidence. Journal of econometrics, 52(1-2), 5-59.
  • Bollerslev, T., Engle, R.F. and Wooldridge, J.M. (1988). A Capital Asset Pricing Model with Time Varying Covariances. Journal of Political Economy, 96, 116-131 .
  • Celik, I., Ozdemir, A., Gursoy, S. and Unlu, H. U. (2018). Return and Volatility Spillover Between Emerging Stock Markets and Precious Metals. Ege Academic Review, 18(2), 217-230.
  • Chen, S., Chen, C. Y. H. and Härdle, W. K. (2020). A First Econometric Analysis of the CRIX Family. arXiv preprint arXiv:2009.12129.
  • Chiang, T. C. (2022). Can Gold or Silver be Used as a Hedge Against Policy Uncertainty and COVID-19 in the Chinese Market? China Finance Review International, 12(4), 571-600.
  • Conrad, C., Custovic, A. and Ghysels, E. (2018). Long-and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis. Journal of Risk and Financial Management, 11(2), 1-12.
  • Corbet, S., Hou, Y. G., Hu, Y., Larkin, C. and Oxley, L. (2020). Any Port in a Storm: Cryptocurrency Safe-Havens During the COVID-19 Pandemic. Economics letters, 194, 109377.
  • D’Amato, V., Levantesi, S. and Piscopo, G. (2022). Deep Learning in Predicting Cryptocurrency Volatility. Physica A: Statistical Mechanics and its Applications, 596, 127158.
  • Dickey, D. A. and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of American Statistical Association, 74, 427-431.
  • Dickey, D. A. and Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49, 1057-1072.
  • Doumenis, Y., Izadi, J., Dhamdhere, P., Katsikas, E. and Koufopoulos, D. (2021). A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets. Risks, 9(11), 207.
  • Dyhrberg, A. H. (2016). Bitcoin, Gold and The Dollar–a GARCH Volatility Analysis, Finance Research Letters, 16, 85-92.
  • Elsayed, A. H., Gozgor, G. and Yarovaya, L. (2022). Volatility and Return Connectedness of Cryptocurrency, Gold, and Uncertainty: Evidence from the Cryptocurrency Uncertainty Indices. Finance Research Letters, 47, 102732.
  • 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.
  • Engle, R. (2004). Risk and Volatility: Econometric Models and Financial Practice. American Economic Review, 94(3), 405-420.
  • Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U.K. Inflation, Econometrica, 50, 987-1008
  • Engle, R.F. (1979). A General Approach to the Construction of Model Diagnostics Based Upon the Lagrange Multiplier Principle. The Warwick Economics Research Paper Series, 156, University of Warwick, Department of Economics.
  • Eom, C., Kaizoji, T., Kang, S. H. and Pichl, L. (2019). Bitcoin and Investor Sentiment: Statistical Characteristics and Predictability. Physica A: Statistical Mechanics and its Applications, 514, 511–521.
  • Fama, Eugene F. (1965). Random Walks in Stock Market Prices, Financial Analysts Journal, 51(1), 75-80.
  • Fama, Eugene F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25(2), 383-417.
  • Gabauer, D. (2020). Volatility Impulse Response Analysis for DCC‐GARCH Models: The Role of Volatility Transmission Mechanisms. Journal of Forecasting, 39(5), 788-796.
  • Ghorbel, A. and Jeribi, A. (2021). Investigating the Relationship Between Volatilities of Cryptocurrencies and Other Financial Assets, Decisions in Economics and Finance, 44(2), 817-843.
  • Gozgor, G., Lau, C. K. M., Sheng, X. and Yarovaya, L. (2019). The Role of Uncertainty Measures on the Returns of Gold. Economics Letters, 185, 108680.
  • Gujarati, D. N. (2003). Basic Econometrics. Newyork: McGraw Hill.
  • Hassan, M. K., Hasan, M. B. and Rashid, M. M. (2021). Using Precious Metals to Hedge Cryptocurrency Policy and Price Uncertainty. Economics Letters, 206, 109977.
  • Ilbasmış, M. A (2024). Comparison of Forecasting Accuracy Between Two Dynamic Conditional Correlation (DCC) Models. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 9(23), 1-11.
  • Ji, Q., Zhang, D. and Zhao, Y. (2020). Searching for Safe-Haven Assets During the COVID-19 Pandemic. International Review of Financial Analysis, 71, 101526.
  • Kang, S. H., McIver, R. P. and Hernandez, J. A. (2019). Co-Movements Between Bitcoin and Gold: a Wavelet Coherence Analysis. Physica A: Statistical Mechanics and its Applications, 536, 120888.
  • Karpuz, E. (2023). Uluslararası Piyasalarda Getiri ve Volatilite Etkileşimi: Asimetrik Yapı ve Bulaşıcılık. Uluslararası Ekonomi ve Siyaset Bilimleri Akademik Araştırmalar Dergisi, 7(18), 1-16.
  • Katsiampa, P. (2019). An Empirical Investigation of Volatility Dynamics in the Cryptocurrency Market. Research in International Business and Finance, 50, 322-335.
  • Kılıç, E. (2021). DCC-GARCH ile Altında Spot Fiyat, Vadeli Fiyat ve Risk İlişkisi. Bingöl Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 5(Uluslararası İktisadi ve İdari Bilimler Kongresi: Krizler, Belirsizlikler ve Arayışlar Özel Sayısı), 55-68.
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  • Klein, T. (2017). Dynamic Correlation of Precious Metals and Flight-to-Quality in Developed Markets. Finance Research Letters, 23, 283-290.
  • Klein, T., Thu, H. P. and 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.
  • L´opez-Cabarcos, M.´A., P´erez-Pico, A. M., Pi˜neiro-Chousa, J. and ˇSevi´c, A. (2021). Bitcoin Volatility, Stock Market and Investor Sentiment. Are They Connected? Finance Research Letters, 38, 101399.
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Dynamic Stochastic Volatility Spillover Between Bitcoin and Precious Metals

Yıl 2025, Cilt: 9 Sayı: 1, 53 - 72
https://doi.org/10.29216/ueip.1596577

Öz

Since its creation in 2008, Bitcoin has often been compared to precious metals due to their shared characteristics as safe havens, hedges, and risk diversification tools. This study uses the DCC-GARCH model to analyze dynamic conditional correlations and volatility spillovers between Bitcoin and the returns of gold, copper, silver, and platinum. The findings reveal persistent volatility and clustering in the returns of both Bitcoin and these metals. There is a one-way volatility spillover from gold to Bitcoin, and from Bitcoin to copper, silver, and platinum. Significant dynamic conditional correlations are observed between Bitcoin and both gold and copper, while no significant correlations are found with silver and platinum. These results provide valuable insights for portfolio diversification strategies and inform policymaker decisions in financial markets.

Kaynakça

  • Akçalı, B. Y., Mollaahmetoğlu, E. and Altay, E. (2019). Borsa İstanbul ve Küresel Piyasa Göstergeleri Arasındaki Volatilite Etkileşiminin DCC-GARCH Yöntemi ile Analizi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 14(3), 597-614.
  • Akkus, H. T., Gursoy, S. and Dogan, M. (2022). The Volatility Spillover Between NFT Investment Index and Global Technology Index: DCC-GARCH Application. Research Journal of Business and Management, 9(2), 85-91.
  • Apostolakis, G. N. (2024). Bitcoin Price Volatility Transmission Between Spot and Futures Markets. International Review of Financial Analysis, 103251.
  • Baek, C. and Elbeck, M. (2015). Bitcoins as an Investment or Speculative Vehicle? A First Look, Applied Economics Letters, 22(1), 30-34.
  • Bala, D. A. and Takimoto, T. (2017). Stock Markets Volatility Spillovers During Financial Crises: A DCC-MGARCH with Skewed-t Density Approach. Borsa Istanbul Review, 17(1), 25-48.
  • Baumöhl, E. and Lyócsa, Š. (2014). Volatility and Dynamic Conditional Correlations of Worldwide Emerging and Frontier Markets. Economic Modelling, 38, 175-183.
  • Baur, D. G. and Smales, L. A. (2020). Hedging Geopolitical Risk with Precious Metals. Journal of Banking & Finance, 117, 105823.
  • Baur, D. G., Hong, K. and Lee, A. D. (2018). Bitcoin: Medium of Exchange or Speculative Assets?, Journal of International Financial Markets, Institutions and Money, 54, 177-189.
  • Baur, D.G. and Lucey, B.M. (2010). Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold. Financial Rev. 45 (2), 217–229.
  • Bianchi, D., Guidolin, M. and Pedio, M. (2023). The Dynamics of Returns Predictability in Cryptocurrency Markets. The European Journal of Finance, 29(6), 583-611.
  • Bilgin, M.H., Gozgor, G., Lau, C.K.M. and Sheng, X. (2018). The Effects of Uncertainty Measures on the Price of Gold. Int. Rev. Financ. Anal. 58, 1–7.
  • Bollerslev T., (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 498-505.
  • Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992). ARCH Modeling in Finance: A Review of The Theory and Empirical Evidence. Journal of econometrics, 52(1-2), 5-59.
  • Bollerslev, T., Engle, R.F. and Wooldridge, J.M. (1988). A Capital Asset Pricing Model with Time Varying Covariances. Journal of Political Economy, 96, 116-131 .
  • Celik, I., Ozdemir, A., Gursoy, S. and Unlu, H. U. (2018). Return and Volatility Spillover Between Emerging Stock Markets and Precious Metals. Ege Academic Review, 18(2), 217-230.
  • Chen, S., Chen, C. Y. H. and Härdle, W. K. (2020). A First Econometric Analysis of the CRIX Family. arXiv preprint arXiv:2009.12129.
  • Chiang, T. C. (2022). Can Gold or Silver be Used as a Hedge Against Policy Uncertainty and COVID-19 in the Chinese Market? China Finance Review International, 12(4), 571-600.
  • Conrad, C., Custovic, A. and Ghysels, E. (2018). Long-and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis. Journal of Risk and Financial Management, 11(2), 1-12.
  • Corbet, S., Hou, Y. G., Hu, Y., Larkin, C. and Oxley, L. (2020). Any Port in a Storm: Cryptocurrency Safe-Havens During the COVID-19 Pandemic. Economics letters, 194, 109377.
  • D’Amato, V., Levantesi, S. and Piscopo, G. (2022). Deep Learning in Predicting Cryptocurrency Volatility. Physica A: Statistical Mechanics and its Applications, 596, 127158.
  • Dickey, D. A. and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of American Statistical Association, 74, 427-431.
  • Dickey, D. A. and Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49, 1057-1072.
  • Doumenis, Y., Izadi, J., Dhamdhere, P., Katsikas, E. and Koufopoulos, D. (2021). A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets. Risks, 9(11), 207.
  • Dyhrberg, A. H. (2016). Bitcoin, Gold and The Dollar–a GARCH Volatility Analysis, Finance Research Letters, 16, 85-92.
  • Elsayed, A. H., Gozgor, G. and Yarovaya, L. (2022). Volatility and Return Connectedness of Cryptocurrency, Gold, and Uncertainty: Evidence from the Cryptocurrency Uncertainty Indices. Finance Research Letters, 47, 102732.
  • 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.
  • Engle, R. (2004). Risk and Volatility: Econometric Models and Financial Practice. American Economic Review, 94(3), 405-420.
  • Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U.K. Inflation, Econometrica, 50, 987-1008
  • Engle, R.F. (1979). A General Approach to the Construction of Model Diagnostics Based Upon the Lagrange Multiplier Principle. The Warwick Economics Research Paper Series, 156, University of Warwick, Department of Economics.
  • Eom, C., Kaizoji, T., Kang, S. H. and Pichl, L. (2019). Bitcoin and Investor Sentiment: Statistical Characteristics and Predictability. Physica A: Statistical Mechanics and its Applications, 514, 511–521.
  • Fama, Eugene F. (1965). Random Walks in Stock Market Prices, Financial Analysts Journal, 51(1), 75-80.
  • Fama, Eugene F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25(2), 383-417.
  • Gabauer, D. (2020). Volatility Impulse Response Analysis for DCC‐GARCH Models: The Role of Volatility Transmission Mechanisms. Journal of Forecasting, 39(5), 788-796.
  • Ghorbel, A. and Jeribi, A. (2021). Investigating the Relationship Between Volatilities of Cryptocurrencies and Other Financial Assets, Decisions in Economics and Finance, 44(2), 817-843.
  • Gozgor, G., Lau, C. K. M., Sheng, X. and Yarovaya, L. (2019). The Role of Uncertainty Measures on the Returns of Gold. Economics Letters, 185, 108680.
  • Gujarati, D. N. (2003). Basic Econometrics. Newyork: McGraw Hill.
  • Hassan, M. K., Hasan, M. B. and Rashid, M. M. (2021). Using Precious Metals to Hedge Cryptocurrency Policy and Price Uncertainty. Economics Letters, 206, 109977.
  • Ilbasmış, M. A (2024). Comparison of Forecasting Accuracy Between Two Dynamic Conditional Correlation (DCC) Models. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 9(23), 1-11.
  • Ji, Q., Zhang, D. and Zhao, Y. (2020). Searching for Safe-Haven Assets During the COVID-19 Pandemic. International Review of Financial Analysis, 71, 101526.
  • Kang, S. H., McIver, R. P. and Hernandez, J. A. (2019). Co-Movements Between Bitcoin and Gold: a Wavelet Coherence Analysis. Physica A: Statistical Mechanics and its Applications, 536, 120888.
  • Karpuz, E. (2023). Uluslararası Piyasalarda Getiri ve Volatilite Etkileşimi: Asimetrik Yapı ve Bulaşıcılık. Uluslararası Ekonomi ve Siyaset Bilimleri Akademik Araştırmalar Dergisi, 7(18), 1-16.
  • Katsiampa, P. (2019). An Empirical Investigation of Volatility Dynamics in the Cryptocurrency Market. Research in International Business and Finance, 50, 322-335.
  • Kılıç, E. (2021). DCC-GARCH ile Altında Spot Fiyat, Vadeli Fiyat ve Risk İlişkisi. Bingöl Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 5(Uluslararası İktisadi ve İdari Bilimler Kongresi: Krizler, Belirsizlikler ve Arayışlar Özel Sayısı), 55-68.
  • Kılıç, E. (2022). Bitcoin ile Vadeli İşlemler Piyasası Arasındaki İlişkinin Analizi. Gaziantep University Journal of Social Sciences, 21(3), 1457-1470.
  • Klein, T. (2017). Dynamic Correlation of Precious Metals and Flight-to-Quality in Developed Markets. Finance Research Letters, 23, 283-290.
  • Klein, T., Thu, H. P. and 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.
  • L´opez-Cabarcos, M.´A., P´erez-Pico, A. M., Pi˜neiro-Chousa, J. and ˇSevi´c, A. (2021). Bitcoin Volatility, Stock Market and Investor Sentiment. Are They Connected? Finance Research Letters, 38, 101399.
  • Letho, L., Chelwa, G. and Alhassan, A. L. (2022). Cryptocurrencies and Portfolio Diversification in an Emerging Market. China Finance Review International, 12(1), 20-50.
  • Li, S., andLucey, B. M. (2017). Reassessing the Role of Precious Metals as Safe Havens–What Colour is Your Haven and Why? Journal of Commodity Markets, 7, 1-14.
  • Liu, J. and Serletis, A. (2019). Volatility in the Cryptocurrency Market. Open Economies Review, 30(4), 779-811.
  • Mabrouk, H. B. and Khalifa, I. B. (2024). Asymmetric Volatility Spillovers Between Bitcoin, Oil and Precious Metals. International Journal of Economics and Business Research, 28(1), 44-64.
  • Malladi, R. K. and Dheeriya, P. L. (2021). Time Series Analysis of Cryptocurrency Returns and Volatilities. Journal of Economics and Finance, 45(1), 75-94.
  • Marobhe, M. I. (2022). Cryptocurrency as a Safe Haven for Investment Portfolios Amid COVID-19 Panic Cases of Bitcoin, Ethereum and Litecoin. China Finance Review International, 12(1), 51-68.
  • Mensi, W., Sensoy, A., Aslan, A. and Kang, S. H. (2019). High-Frequency Asymmetric Volatility Connectedness Between Bitcoin and Major Precious Metals Markets. The North American Journal of Economics and Finance, 50, 101031.
  • Mishra, A. (2019). Crude Oil, Stock Market, and Foreign Exchange Return Volatility and Spillover: A GARCH DCC Analysis Of Indian and Japanese Financial Market. International Journal of Business Innovation and Research, 20(1), 25-46.
  • Murty, S., Victor, V. and Fekete-Farkas, M. (2022). Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis. Journal of Risk and Financial Management, 15(7), 317.
  • Nakamoto, Satoshi. 2008. Bitcoin: Bitcoin: A Peer-to-Peer Electronic Cash System. Access address: https://www.lopp.net/pdf/bitcoin.pdf
  • Ozturk, S. S. (2020). Dynamic Connectedness Between Bitcoin, Gold, and Crude Oil Volatilities and Returns. Journal of Risk and Financial Management, 13(11), 275.
  • Perron, P. (1989). The Great Crash, The Oil Price Shock, and The Unit Root Hypothesis. Econometrica, 57,1361-1401. Rastogi, S. and Agarwal, A. (2020). Volatility Spillover Effect in Spot, Futures and Option Markets. Test Enginnering and Management, 83(May-June), 10114-10127.
  • Rehman, M. U. (2020). Do Bitcoin and Precious Metals Do any Good Together? An Extreme Dependence and Risk Spillover Analysis. Resources Policy, 68, 101737.
  • Sapkota, N. (2022). News-Based Sentiment and Bitcoin Volatility. International Review of Financial Analysis, 82, 102183.
  • Seker, K. (2023a). Döviz Getirisinde Haftanın Günü Etkisinin Araştırılması: Türkiye Örneği. Uluslararası Akademik Birikim Dergisi, 6(1), 1-18.
  • Seker, K. (2023b). Döviz Kuru ile BISTde İşlem Gören Sigorta Şirketlerinin Hisse Senedi Getirileri Arasındaki Oynaklık Yayılımları: CCC-GARCH Modeli (2002-2022), Editör: K. Seker (Volatilite Analizleri), 1.Baskı, Gazi Kitabevi, Ankara.
  • Shahzad, S. J. H., Bouri, E., Roubaud, D. and Kristoufek, L. (2020). Safe Haven, Hedge and Diversification for G7 Stock Markets: Gold Versus Bitcoin. Economic Modelling, 87, 212-224.
  • Singh, V. V., Singh, H. and Ansari, A. (2024). Bitcoin as a Distinct Asset Class for Hedging and Portfolio Diversification: A DCC-GARCH Model Analysis. NMIMS Management Review, 32(1), 7-13.
  • Szetela, B., Mentel, G. and Gedek, S. (2016). Dependency Analysis Between Bitcoin and Selected Global Currencies, Dynamic Econometric Models, 16(1), 133-144.
  • Tse, Y.K. and Tsui, A.K.C. (2002). A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Timevarying Correlations. Journal of Business and Economic Statistics, 20(3), 351-362.
  • Tuncay, M. (2021). VIX Korku Endeksinin Bist Sektör Endeksleri ile Volatilite Etkileşiminin CCC-GARCH ile Araştırılması: 2013-2202 Dönemi. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(21), 126-146.
  • Ustaoğlu, E. (2022). Return and Volatility Spillover Between Cryptocurrency And Stock Markets: Evidence From Turkey. Muhasebe ve Finansman Dergisi, (93), 117-126.
  • Wei, Y., Wang, Y., Lucey, B. M. and Vigne, S. A. (2023). Cryptocurrency Uncertainty and Volatility Forecasting of Precious Metal Futures Markets. Journal of Commodity Markets, 29, 100305.
  • Woebbeking, F. (2021). Cryptocurrency Volatility Markets. Digital Finance, 3(3), 273-298.
  • Wu, M., Wang, L. and Yang, H. (2024). Heterogeneity in The Volatility Spillover of Cryptocurrencies and Exchanges. Financial Innovation, 10(1), 85.
  • Yaya, O. S., Lukman, A. F. and Vo, X. V. (2022). Persistence and Volatility Spillovers of Bitcoin Price to Gold and Silver Prices. Resources Policy, 79, 103011.
  • Yen, K. C. and Cheng, H. P. (2021). Economic Policy Uncertainty and Cryptocurrency Volatility. Finance Research Letters, 38, 101428.
  • Yermack, D. (2013). Is Bitcoin a Real Currency? An Economic Appraisal, National Bureau of Economic Research, Working Paper 19747.
  • Zhang, S. and Mani, G. (2021). Popular Cryptoassets (Bitcoin, Ethereum, and Dogecoin), Gold, and Their Relationships: Volatility and Correlation Modeling. Data Science and Management, 4, 30-39.
  • Zivot, E. and Andrews, D. W. K. (1992). Further Vidence on The Great Crash, the Oil-Price Shock, and The Unit-Root Hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomik Modeller ve Öngörü, Uygulamalı Mikro Ekonometri, Zaman Serileri Analizi
Bölüm ARAŞTIRMA MAKALELERİ
Yazarlar

Kudbeddin Şeker 0000-0001-6705-2890

Ahmet Gökçe Akpolat 0000-0001-7149-6339

Yayımlanma Tarihi
Gönderilme Tarihi 5 Aralık 2024
Kabul Tarihi 22 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 1

Kaynak Göster

APA Şeker, K., & Akpolat, A. G. (t.y.). Dynamic Stochastic Volatility Spillover Between Bitcoin and Precious Metals. Uluslararası Ekonomi İşletme Ve Politika Dergisi, 9(1), 53-72. https://doi.org/10.29216/ueip.1596577

Uluslararası Ekonomi, İşletme ve Politika Dergisi

Recep Tayyip Erdoğan Üniversitesi
İktisadi ve İdari Bilimler Fakültesi
İktisat Bölümü
RİZE / TÜRKİYE