Regime Dynamics of International Precious Metal Markets

Yıl 2017, Sayı: 107, 26 - 40, 18.04.2017

Ayben Koy , Güldenur Çetin , İhsan Ersan

https://doi.org/10.33203/mfy.307172

Cited By: 1

Öz

The aim of this study is to analyze
whether the precious metals have a nonlinear pattern by using Multivariate
Markov Switching Vector Autoregressive Models (MMS-VAR). The observation period
is between 02 January 2002 and 28 March 2016 and includes daily closed prices
of gold, silver, palladium and platinum. Research results have evidence that
the international precious metal market have a structure with three regimes as
depression, moderate growth and expansion.

Anahtar Kelimeler

Precious Metals Markets, Nonlinearity, Markov Switching Vector Autoregressive Model

Uluslararası Kıymetli Metal Piyasalarının Rejim Dinamikleri

Öz

Bu çalışmanın
amacı, kıymetli metal piyasalarının doğrusal olmayan yapılarını Çok Değişkenli
Markov Rejim Değişim Modelleriyle (MMS-VAR) analiz etmektir. Çalışmanın gözlem
aralığı 02 Ocak 2002 – 28 Mart 2016 olup, spot altın, gümüş, paladyum ve
platine ait günlük kapanış fiyatlarını içermektedir. Araştırma sonuçları,
uluslararası kıymetli metal piyasasının daralma, ılımlı büyüme ve genişleme
rejimlerinden oluşan bir yapıya sahip olduğuna dair kanıtlar sunmaktadır.

Anahtar Kelimeler

Kıymetli Metal Piyasaları, Doğrusal Olmama, Markov Rejim Değişim Vektör Otoregresif Modeli

Kaynakça

• Ang, A., Timmermann A. G. 2011. Regime Changes and Financial Markets. Netspar Discussion Papers, DP 06/2011-068.
• Arouri, M. vd. 2013. On the short- and long-run efficiency of energy and precious metal markets. Energy Economics. 40, 832–844
• Arouri, M. H., D. N. Nguyen. 2010. Oil prices, stock markets and portfolio investment: evidence from sector analysis in Europe over the last decade. Energy Policy. 38, 4528–4539.
• Balcılar, M. vd. 2015. A regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates. International Review of Economics and Finance. 40, 72-89.
• Bildirici, M. vd. 2010. İktisatta Kullanılan Doğrusal Olmayan Zaman Serisi Yöntemleri. İstanbul: Türkmen Kitabevi.
• Brockwell, P. 2011. Discussion of Threshold models in time series analysis-30 years on. Statistics and Its Interface. 4, 129-130.
• Caporin, M., A. Ranaldo and G. G. Velo. 2015. Precious metals under the microscope: a high-frequency analysis. Quantitative Finance. 15( 5), 743–759.
• Charlot, P. and V. Marimoutou. 2014. On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree, Energy Economics. 44, 456-467.
• Cheng, H., L. Shao, and Y. Guo. 2013. State Transition Behaviors of SHFE Copper Prices Based on Markovswitching Model, Journal of Convergence Information Technology. 8(6).
• Conover, C. M., G. R. Jensen, R. R. Johnson, J. M. Mercer. 2010. Is now the time to add commodities to your portfolio? Journal of Invest. 19, 10–19.
• Daskalaki, C., G. S. Skiadopoulos. 2011. Should investors include commodities in their portfolios after all? New evidence. Journal of Banking and Finance. 35, 2606–2626.
• Franses P.H. and D. van. Dijk. 2000. Nonlinear Time Series Models in Empirical Finance. Cambridge Universtiy Press.
• Guidolin, M. Modelling, Estimating and Forecasting Financial Data under Regime (Markov) Switching. Lecture 7. Department of Finance. Bocconi University. http://didattica.unibocconi.it/mypage/dwload.php?nomefile=Lecture_7_-_Markov_Switching_Models20130520235704.pdf, 30.03.2016.
• Hamilton, J. D. 1989. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica. 57(2), 357-384.
• Hammoudeh, S. P. Araújo-Santos, A. Al-Hassan, 2013. Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks. The North American Journal of Economics and Finance. 25, 318–334.
• Neftçi, S. N. 1984. Are Economic Time Series Asymmetric over the Business Cycle? The Journal of Political Economy. 92(2), 307-328.
• Koy, A., G. Çetin, 2016. Metal Vadeli İşlem Piyasaları ve Doğrusal Olmayan Dinamikleri. İşletme ve İktisat Çalışmaları Dergisi. 4(4), 165-176.
• Krolzig, H. M. 1997. Markov Switching Vector Autoregressions: Modeling, Statistical Inference, and Application to Business Cycle Analysis. Springer Verlag.
• Krolzig, H. M. 1998. Econometric Modeling of Markov-Switching Vector Autoregressions using MSVAR for OX. Institute of Economics and Statistics and Nuffield College. Oxford.
• Krolzig, H. M. 2000. Predicting Markov-Switching Vector Autoregressive Processes. Oxford University. Working Paper 2000W31. Krolzig, H. M. 2001. Markov-Switching Procedures for Dating the Euro-Zone Business Cycle. Vierteljahrshefte zur Wirtschaftsforschung. 70(3), 339-351.
• Seuk Wai, P., M. T. Ismail, S. K. Sek. 2013. A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data. Information Management and Business Review. 5(8), 379-384.
• Tong, H. 1983. Threshold Models in Non-linear Time Series Analysis. Lecture Notes in Statistics. New York: Springer-Verlag.
• A-Mark Precious Metals, http://www.amark.com/