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Journals / Alanya Akademik Bakış / 2021 / Cilt: 5 - Sayı: 2

Engle-Granger Cointegration Analysis Between GARCHType Volatilities of Gold and Silver Returns

Yakup ARI
Pages
589–618
Publication Date
2022-10-12
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is concluded that the volatilities of silver and gold returns are cointegrated and the deviation from long-run equilibrium is rebalanced within 20 days.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to reveal the cointegration relationship between the volatility of silver and gold returns.
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Key Findings & Data:The volatility data of the returns are obtained from 10 different GARCH-type models and using 8 different probability distributions in these models.
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Conclusion & Impact:It is concluded that the volatilities of silver and gold returns are cointegrated and the deviation from long-run equilibrium is rebalanced within 20 days.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The aim of this study is to reveal the cointegration relationship between the volatility of silver and gold returns. For this purpose, the volatility of silver and gold returns is modeled with GARCH-type models. The volatility data of the returns are obtained from 10 different GARCH-type models and using 8 different probability distributions in these models. The most adequate fit models for the volatility of silver and gold returns are found as ALLGARCH (1,1) and AVGARCH (1,1), respectively, where innovations are normal inverse Gaussian distributed. Volatility data are obtained from these models and it is determined that they are not stationary at the level. Therefore, the long-run and short-run relationships between the two volatilities are tested by the two-step Engle-Granger Cointegration method. Furthermore, the volatility spillover from silver returns to gold returns is shown by using the squared standardized residuals of the volatility model of silver returns as an exogenous variable in the nig-AVGARCH (1,1) volatility model of gold returns. It is concluded that the volatilities of silver and gold returns are cointegrated and the deviation from long-run equilibrium is rebalanced within 20 days.

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Cite This Article

ARI, Y. (2022). Engle-Granger Cointegration Analysis Between GARCHType Volatilities of Gold and Silver Returns. Alanya Akademik Bakış, Cilt: 5 - Sayı: 2, 589–618. https://ajindex.com/articles/434748

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References (15)

  1. AKGIRAY, V. (1989). “Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts.” Journal of Business, 62:55–80.
  2. ARI, Y. (2020a). Volatility Transmission Model Using Dcc-Garch Representation. Book Chapter in: Studies at the Crossroads of Management & Economics, pp.237-250.ISBN: 978-1-912503-92-6
  3. IJOPEC Publication. ARI, Y. (2020b). From Discrete to Continuous GARCH Volatility Modelling With R. Available at https://math-stat.net/garch-and-cogarch-modelling.htm
  4. BATTEN, J. A., LUCEY, B. M. & PEAT, M. (2016). “Gold and silver manipulation: What can be empirically verified?” Economic Modelling, 56:168–176
  5. BOLLERSLEV, T. P. (1986). “Generalized autoregressive conditional heteroscedasticity”, Journal of Econometrics, 31, pp. 307-327.
  6. CHARLES, A., DARN_E, O. & KIM, J. H. (2015). “Will precious metals shine? A market efficiency perspective.” International Review of Financial Analysis, 41:284-291.
  7. CHATRATH, A., ADRANGI, B. & SHANK, T. (2001). “Nonlinear Dependence in Gold and Silver Futures: Is It Chaos?” The American Economist, 45(2):25–32.
  8. CHRISTIE-DAVID, R. A., CHAUDHRY, M., AND KOCH, T. W. (2000). “Do macroeconomics news releases affect gold and silver prices?” Journal of Economics and Business, 52(5):405–421.
  9. CICIOĞLU, Ş., ERASLAN, B., & TORUN, P. (2018). “Türkiye’de Altın Fiyatlarını Belirleyen Faktörler.” Journal of Human Sciences, 15(3), 1551-1560. doi:10.14687/jhs.v15i3.47181553 CINER, C. (2001). “On the long run relationship between gold and silver prices: A note.” Global Finance Journal, 12(2):299-303.
  10. COCHRAN, S. J., MANSUR, I., & ODUSAMI, B. (2012). “Volatility persistence in metal returns: A FIGARCH approach.” Journal of Economics and Business, 64(4):287-305.
  11. DEMİRALAY, S. & BAYRACI, S. (2015). “Central and Eastern European Stock Exchanges under Stress: A Range-Based Volatility Spillover Framework.” Czech Journal of Economics and Finance (Finance a uver), vol. 65(5), pages 411-430,
  12. October. DING Z., ENGLE R.F. & GRANGER C.W.J. (1993). “A long memory property of stock market return and a new model,” Journal of Empirical Finance 1(1), 83-106.
  13. DIEBOLD, F.X. & YILMAZ, K. (2009). “Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets.” Economic Journal, 119, 158-171.
  14. DUTTA, A. (2018). “A note on the implied volatility spillovers between gold and silver markets.” Resources Policy, vol. 55(C), pages 192-195.
  15. DIEBOLD, F.X. & YILMAZ, K. (2012), “Better to Give than to Receive: Forecast-