IS BITCOIN MORE THAN A DIVERSIFIER FOR COMMODITIES? RANGE-BASED ANALYSIS VIA cDCC-GARCH
Year 2022,
Volume: 7 Issue: 2, 227 - 240, 30.06.2022
Tuğrul Kandemir
,
Halilibrahim Gökgöz
Abstract
The purpose of this study is to examine the diversifying role of Bitcoin for commodities and its interaction with commodities. Within the scope of the study, the daily data set consisting of Bitcoin, gold, silver, commodity index, crude oil, and energy commodities index variables covering the period 17.09.2014 - 24.11.2021 was transformed into Garman-Klass series, and dynamic conditional correlation models were applied. As a result of the application, it was observed that the most suitable model to test the interaction between Bitcoin and commodities was cDCC-GARCH. The interaction between Bitcoin and commodities (excluding silver) was negative; It has been determined that the interaction between the commodities is positive. The findings show that Bitcoin is a better diversifier for commodities (except silver) than other commodities and acts as a hedge when included in a portfolio of commodities.
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BİTCOİN, EMTİALAR İÇİN ÇEŞİTLENDİRİCİDEN FAZLASI MI? ARALIĞA DAYALI cDCC-GARCH İLE ANALİZİ
Year 2022,
Volume: 7 Issue: 2, 227 - 240, 30.06.2022
Tuğrul Kandemir
,
Halilibrahim Gökgöz
Abstract
Bu çalışmanın amacı Bitcoin’in emtialar için çeşitlendirici rolünün ve emtialarla etkileşiminin incelenmesidir. İnceleme kapsamında Bitcoin, altın, gümüş, emtia endeksi, ham petrol ve enerji emtiaları endeksi değişkenlerinden oluşan 17.09.2014 - 24.11.2021 dönemini kapsayan günlük veri seti Garman-Klass serilerine dönüştürülmüş ve dinamik koşullu korelasyon modelleri uygulanmıştır. Uygulama sonucunda Bitcoin ile emtialar arasındaki etkileşimi test etmek için en uygun modelin cDCC-GARCH olduğu gözlenmiş ve Bitcoin ile emtialar (gümüş hariç) arasındaki etkileşimin negatif yönlü; emtiaların kendi aralarındaki etkileşimin pozitif yönlü olduğu tespit edilmiştir. Bulgular, Bitcoin’in emtialar için (gümüş hariç) diğer emtialara göre daha iyi bir çeşitlendirici olduğunu ve Bitcoin’in emtia bulunduran portföye dahil edildiğinde hedge etme görevi üstlendiğini göstermektedir.
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- Chou, R. Y. Chou, H., Liu, N. (2010). Range volatility models and their applications in finance. Handbook of Quantitative Finance and Risk Management, 1273-1281, Springer. New York. DOI:10.1007/978-0-387-77117-5_83.
- Chou, R. Y.,Chou, H., N. Liu. (2015). “Range Volatility: A Review of Models and Empirical Studies.” In Hand-book of Financial Econometrics and Statistics, 2029–2050. New York, NY: Springer.
- Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The Economics of Bitcoin Price Formation. Applied Economics, 48, 1799-1815. https://doi.org/10.1080/00036846.2015.1109038.
- 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. https://doi.org/10.1016/j.econlet.2018.01.004.
- 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. https://doi.org/10.1016/j.frl.2019.101335
- Do, A., Powell, R., Yong, J., Singh, A. (2019). Time-varying asymmetric volatility spillover between global markets and China’s A, B and H-shares using EGARCH and DCC-EGARCH models, The North American Journal of Economics and Finance, Vol. 54. https://doi.org/10.1016/j.najef.2019.101096
- Dyhrberg, A. H. (2016). Bitcoin, Gold and the Dollar – A GARCH Volatility. Finance Research Letters, 16, 85-92. http://dx.doi.org/10.1016/j.frl.2015.10.008.
- Eisl, A., Gasser, S., Weinmayer, K. (2015). Caveat Emptor: Does Bitcoin Improve Portfolio Diversification? http://dx.doi.org/10.2139/ssrn.2408997.
- Garman, M. B., & Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53 (1), 67–7.
- 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.
- Halaburda, H., Gandal, N. (2014). Can we predict the winner in a market with network effects? Competition in cryptocurrency market," in Games 7 (3), 16, NET Institute Working Paper No. 14-17. http://dx.doi.org/10.2139/ssrn.2506463.
- Jareno, F., de la Gonzalez, M., Tolentino, M., Sierra, K. (2020). Bitcoin and Gold price returns: a quantile regression and NARDL analysis. Resour. Pol. 67, 1–16. https://doi.org/10.1016/j.resourpol.2020.101666.
- Jiang, S., Li, Y., Luc, 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. Int. Rev. Financ. Anal. 59, 105–116. https://doi.org/10.1016/j.irfa.2018.07.010.
- Kurka, J. (2019). Do Cryptocurrencies and Traditional Asset Classes Influence Each Other? Finance Research Letters, 31, 38–4. https://doi.org/10.1016/j.frl.2019.04.018.
- Li, X., Wang, C. A. (2017). Quantile spillovers and dependence between Bitcoin, equities and strategic commodities. Economic Modelling, 93, 230-258. https://doi.org/10.1016/j.econmod.2020.07.012.
- Lin, M. Y., An, C. L. (2021). The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach. Resources Policy, 74, 102383. https://doi.org/10.1016/j.resourpol.2021.102383.
- Lucey, B., Larkin, C., O’Connor, F. (2014). Gold Markets around the World – Who Spills over What, to Whom, When? Applied Economics Letters, 21 (13), 887–892. https://doi.org/10.1080/13504851.2014.896974.
- Lyócsa, S. (2014). Growth-Returns Nexus: Evidence from Three Central and Eastern European Countries. Economic Modelling, 42, 343–355. https://doi.org/10.1016/j.econmod.2014.07.023.
- Mensi, W., Şensoy, A., Aslan, A., & Kang, S. H. (2019). High-Frequency Asymmetric Volatility Connectedness between Bitcoin and Major Precious Metals Markets. North American Journal of Economics & Finance, 50, 1-38. https://doi.org/10.1016/j.najef.2019.101031.
- Molnar, P. (2016). High-low range in GARCH models of stock return volatility. Applied Economıcs, 48 (51), 4977–4991. http://dx.doi.org/10.1080/00036846.2016.1170929.
- Molnár, P. 2012. Properties of Range-Based Volatility Estimators. International Review of Financial Analysis 23, 20–29. https://doi.org/10.1016/j.irfa.2011.06.012.
- Moussa, W., Mgadmi, N., B´ejaoui, A., Regaieg, R. (2021). Resources Policy, 74, 102416. https://doi.org/10.1016/j.resourpol.2021.102416.
- Okorie, D. I., & Lin, B. (2020). Crude Oil Price and Cryptocurrencies: Evidence of Volatility Connectedness and Hedging Strategy. Energy Economics, 87, art. 104703. https://doi.org/10.1016/j.eneco.2020.10470.
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