Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Esta revista está autorizada por una licencia de atribución Creative Commons (CC BY-NC-SA 4.0) Attribution-Non Commercial 4.0 International. Para las licencias CC, el principio es el de la libertad creativa. Este sistema complementa el derecho de autor sin oponerse a este, conscientes de su importancia en nuestra cultura. El contenido de los artículos es responsabilidad de cada autor y no compromete, de ninguna manera, a la revista o a la institución. Se permite la divulgación y reproducción de títulos, resúmenes y contenido total, con fines académicos, científicos, culturales, siempre y cuando, se cite la respectiva fuente. Esta obra no puede ser utilizada con fines comerciales.
La revista no cobra a los autores por la presentación o la publicación de sus artículos
Resumen
Este estudio investiga la dinámica de volatilidad y las correlaciones variables en el tiempo entre Bitcoin (BTC) y los principales mercados financieros y de materias primas, incluidos el oro, el petróleo, el NASDAQ, el NIKKEI, el FTSE, el DAX y el índice del dólar estadounidense (USDINX). Utilizando datos diarios y modelos de la familia GARCH, se cuantificó la persistencia, la asimetría y la memoria a medio plazo en la volatilidad del BTC. La selección de modelos mediante los criterios de log-verosimilitud, SIC y AIC identifica al modelo EGARCH como el más adecuado para capturar el comportamiento de la varianza condicional. Posteriormente, se empleó un marco DCC-MGARCH para estimar las correlaciones del mercado transversal en evolución. Los resultados indican que la volatilidad del BTC es altamente persistente, presenta reacciones más fuertes ante choques negativos y muestra una reversión moderada a la media. El oro presenta la menor persistencia, lo que confirma su papel como activo diversificador estable. Las estimaciones del DCC-MGARCH revelan correlaciones positivas débiles entre BTC y oro, correlaciones negativas entre BTC y USDINX y ausencia de vínculos significativos entre BTC y
petróleo o BTC y DAX, lo que implica un considerable potencial de diversificación. Cabe destacar que las correlaciones BTC-NIKKEI se fortalecieron durante el período de la COVID-19, mientras que las correlaciones BTC-oro aumentaron de forma moderada. Estos hallazgos subrayan la importancia de estrategias dinámicas de cartera: los pesos óptimos cambian con la evolución de las covarianzas condicionales, lo que hace que las asignaciones estáticas sean subóptimas.
Para los responsables de políticas, la persistencia de la volatilidad y los umbrales de correlación
pueden orientar los límites de apalancamiento y exposición, especialmente cuando los vínculos
de BTC con los activos tradicionales se intensifican.
Citas
Akkaya, M. (2021). Disruptive Financial Innovation and Big Data Implications in Digital Finance. In M. E. A. Abdelli, W. Ajili-Ben Youssef, U. Özgöker, & I. Ben Slimene (Eds.), Big Data for Entrepreneurship and Sustainable Development (pp. 69–90). Taylor & Francis. https://doi.org/10.1201/9781003090045-4
Akkaya, M (2022). Kripto Para İşlemlerini Yönlendiren Ekonomik ve Finansal Faktörler: Bitcoin Fiyat Oluşumu. Tesam Akademi, 9(1), 209–226. https://doi.org/10.30626/tesamakademi.979573
Al-Yahyaee, K. H., Mensi, W., Al-Jarrah, I. M. W., Hamdi, A., & Kang, S. H. (2019). Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analysis with yellow metal. The North American Journal of Economics and Finance, 49, 104–120. https://doi.org/10.1016/j.najef.2019.04.001
Ammous, S., & D’Andrea, F. A. M. C. (2022). Hard Money and Time Preference: a Bitcoin perspective. MISES: Interdisciplinary Journal of Philosophy, Law and Economics, 10. https://doi.org/10.30800/mises.2022.v10.1495
Azimova, T. (2022). Modelling volatility transmission in regional Asian stock markets. The Journal of Economic Asymmetries, 26, e00274. https://doi.org/10.1016/j.jeca.2022.e00274
Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30. https://doi.org/10.1016/S0304-4076(95)01749-6
Baur, D. G., & Hoang, L. (2021). The Bitcoin gold correlation puzzle. Journal of Behavioral and Experimental Finance, 32, 100561. https://doi.org/10.1016/j.jbef.2021.100561
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. https://doi.org/10.1016/j.intfin.2017.12.004
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Brooks, C. (2008). RATS Handbook to accompany introductory econometrics for finance. Cambridge
Books. https://doi.org/10.1017/CBO9780511814082
Chang, H. L., Nie, W. Y., Chang, L. H., Cheng, H. W., & Yen, K. C. (2023). Cryptocurrency Momentum and VIX premium. Finance Research Letters, 57, 104196. https://doi.org/10.1016/j.frl.2023.104196
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057–1072. https://doi.org/10.2307/1912517
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A Long Memory Property of Stock Market Returns and a New Model. Journal of Empirical Finance, 1, 83–106. https://doi.org/10.1016/0927-5398(93)90006-D
Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85–92. https://doi.org/10.1016/j.frl.2015.10.008
Dyhrberg, A. H. (2016b). Hedging capabilities of Bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139–144. https://doi.org/10.1016/j.frl.2015.10.025
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987–1007. https://doi.org/10.2307/1912773
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. https://doi.org/10.1198/073500102288618487
Erdas, M. L., & Caglar, A. E. (2018). Analysis of the relationships between Bitcoin and exchange rate, commodities and global indexes by asymmetric causality test. Eastern Journal of European Studies, 9(2).
Fousekis, P. (2020). Sign and size asymmetry in the stock returns-implied volatility relationship. The Journal of Economic Asymmetries, 21, e00162. https://doi.org/10.1016/j.jeca.2020.e00162
Ghorbel, A., Frikha, W., & Manzli, Y. S. (2022). Testing for asymmetric non-linear short-and longrun relationships between crypto-currencies and stock markets. Eurasian Economic Review, 12(3), 387–425. https://doi.org/10.1007/s40822-022-00206-8
Glaser, F., Zimmermann, K., Haferkon, M., Weber, M.C., & Siering, M. (2014). Bitcoin - Asset or Currency? Revealing Users’ Hidden Intentions. ECIS 2014 (Tel Aviv). https://ssrn.com/abstract=2425247
Glosten, L., Jagannathan, R., & Runkle, D. (1993). Relationship between The Expected Value and The Volatility of The Nominal Excess Return on Stocks. Journal of Finance, 48, 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Gökgöz, H., Syed, A. A., Alnafisah, H., & Jeribi, A. (2025). Safe Haven for Bitcoin: Digital and Physical Gold or Currencies? Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 171. https://doi.org/10.3390/jtaer20030171
Greenberg, A. (2011, April 20). Crypto Currency. Forbes.com. https://www.forbes.com/forbes/2011/0509/technology-psilocybin-bitcoins-gavin-andresen-crypto-currency.html
Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis, 63, 431–437. https://doi.org/10.1016/j.irfa.2018.03.004
Guizani, S., & Nafti, I. K. (2019). The determinants of bitcoin price volatility: An investigation with ARDL model. Procedia computer science, 164, 233–238. https://doi.org/10.1016/j.procs.2019.12.177
Gujarati, D. N. (2002). Basic Econometrics (4th ed.). McGraw-Hill.
Gupta, S. K., Fegade, A., Padhye, P., Mohture, A., Kalshetti, P., & Murthy, Y. S. (2024). Evaluating the Effect of COVID-19-19 on Major World Indices: In relation to Gold, Silver, Crude Oil, and Bitcoin. Academy of Marketing Studies Journal, 28(2).
Gürgün, G., & Ünalmış, İ. (2014). Is gold a safe haven against equity market investment in emerging and developing countries? Finance Research Letters, 11(4), 341–348. https://doi.org/10.1016/j.frl.2014.07.003
Hou, Y., Li, Y., Hu, Y., & Oxley, L. (2022). Time-varying spillovers of higher moments between Bitcoin and crude oil markets and the impact of the US–China trade war: a regime-switching perspective. The European Journal of Finance, 1–31. https://doi.org/10.1080/1351847X.2022.2067002
Jareño, F., de la O. González, M., Tolentino, M., & Sierra, K. (2020). Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67, 101666. https://doi.org/10.1016/j.resourpol.2020.101666
Jin, M., Chen, X., & Lin, S. J. (2019). Reducing the bandwidth of block propagation in bitcoin network with erasure coding. IEEE Access, 7, 175606–175613. https://doi.org/10.1109/ACCESS.2019.2957496
Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280–284. https://doi.org/10.1016/j.frl.2017.12.009
Jiang, S., Li, Y., Lu, Q., Wang, S., & Wei, Y. (2022). Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets. Research in International Business and Finance, 59, 101543. https://doi.org/10.1016/j.ribaf.2021.101543
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
Kurihara, Y., & Fukushima, A. (2018). How Does Price of Bitcoin Volatility Change? International Research in Economics and Finance, 2(1), 8–14. https://doi.org/10.20849/iref.v2i1.317
Kwon, J. H. (2020). Tail behavior of Bitcoin, the dollar, gold and the stock market index. Journal of International Financial Markets, Institutions and Money, 67, 101202. https://doi.org/10.1016/j.intfin.2020.101202
Liu, J., & Serletis, A. (2019). Volatility in the Cryptocurrency Market. Open Economies Review, 30, 779–811. https://doi.org/10.1007/s11079-019-09547-5
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, 1–7. https://doi.org/10.1016/j.frl.2019.101399
Loukil, S., Syed, A. A., Hamza, F., & Jeribi, A. (2025). Decoding the dynamic connectedness between traditional and digital assets under dynamic economic conditions. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 97. https://doi.org/10.3390/jtaer20020097
Mensi, W., Al-Yahyaee, K. H., & Kang, S. H. (2019). Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum. Finance Research Letters, 29, 222–230. https://doi.org/10.1016/j.frl.2018.07.011
Mohammadi, M., Farzin, M., Hosseini, S., & Mohammadi, T. (2023). Investigating the Impact of the Dollar Index and Gold Return Rate on Bitcoin Price: Non-linear and Asymmetric Analysis. International Journal of Business and Development Studies, 15(1).
Munyas, T., & Atasoy, F. (2021). An empirical investigation of the relationship between Bitcoin and developed and developing country stock markets. Journal of Accounting, Finance and Auditing Studies, 7(3), 104–120. https://doi.org/10.32602/jafas.2021.021
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, 21260.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347–370. https://doi.org/10.2307/2938260
Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific–Basin, Journal of International Money and Finance, 19, 207–233. https://doi.org/10.1016/S0261-5606(00)00006-1
Oad Rajput, S. K., Soomro, I. A., & Soomro, N. A. (2022). Bitcoin sentiment index, bitcoin performance and US dollar exchange rate. Journal of Behavioral Finance, 23(2), 150–165. https://doi.org/10.1080/15427560.2020.1864735
Okorie, D. I., & Lin, B. (2020). Crude oil price and cryptocurrencies: evidence of volatility connectedness and hedging strategy. Energy economics, 87, 104703. https://doi.org/10.1016/j.eneco.2020.104703
Omri, I. (2023). Directional predictability and volatility spillover effect from stock market indexes to Bitcoin: evidence from developed and emerging markets. The Journal of Risk Finance, 24(2), 226–243. https://doi.org/10.1108/JRF-06-2022-0130
Polasik, M., Piotrowska, A.I., Wisniewski, T.P., Kotkowski, R., & Lightfoot, G. (2015). Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20(1), 9–49. https://doi.org/10.1080/10864415.2016.1061413
Salisu, A. A., Ndako, U. B., & Vo, X. V. (2023). Oil price and the Bitcoin market. Resources Policy, 82, 103437. https://doi.org/10.1016/j.resourpol.2023.103437
Scaillet, O., Treccani, A., & Trevisan, C. (2017). High-Frequency Jump Analysis Of The Bitcoin Market. Swiss Finance Institute Research Paper, 17–19. https://doi.org/10.2139/ssrn.2982298
Selmi, R., Mensi, W., Hammoudeh, S., & Bouoiyour, J. (2018). Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Economics, 74, 787–801. https://doi.org/10.1016/j.eneco.2018.07.007
Sin, C. Y., & White, H. (1996). Information criteria for selecting possibly misspecified parametric models. Journal of Econometrics, 71(1–2), 207–225. https://doi.org/10.1016/0304-4076(94)01701-8
Telek, C., & Şit, A. (2020). The Relationship of Crypto Coins with Gold and Foreign Exchange: The Case of Bitcoin. Turkish Studies - Economy, 15(2), 913–924. https://doi.org/10.29228/TurkishStudies.42650
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82. https://doi.org/10.1016/j.econlet.2016.09.019
Uzonwanne, G. (2021). Volatility and return spillovers between stock markets and cryptocurrencies. The Quarterly Review of Economics and Finance, 82, 30–36. https://doi.org/10.1016/j.qref.2021.06.018
Wang, C., Shen, D., & Li, Y. (2022). Aggregate investor attention and Bitcoin return: The long short-term memory networks perspective. Finance Research Letters, 49, 103143. https://doi.org/10.1016/j.frl.2022.103143
Wang, X., Chen, X., & Zhao, P. (2020). The relationship between Bitcoin and stock market. International Journal of Operations Research and Information Systems (IJORIS), 11(2), 22–35. https://doi.org/10.4018/IJORIS.2020040102
Wen, F., Tong, X., & Ren, X. (2022). Gold or Bitcoin, which is the safe haven during the COVİD-19 pandemic? International Review of Financial Analysis, 81, 102121. https://doi.org/10.1016/j.irfa.2022.102121
Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31–43). Academic Press. https://doi.org/10.1016/B978-0-12-802117-0.00002-3
Zwick, H. S., & Syed, S. A. S. (2019). Bitcoin and gold prices: A fledging long-term relationship. Theoretical Economics Letters, 9(7), 2516–2525. https://doi.org/10.4236/tel.2019.97159
##submission.citations.for##
