Table of Contents
Economics Research International
Volume 2016 (2016), Article ID 2361954, 7 pages
http://dx.doi.org/10.1155/2016/2361954
Research Article

Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium

Department of Finance and Department of Accounting, College of Business, Chung Yuan Christian University, 200 Chung Pei Road, Chungli City 32023, Taiwan

Received 23 July 2015; Revised 29 October 2015; Accepted 29 October 2015

Academic Editor: Jean Paul Chavas

Copyright © 2016 John Francis T. Diaz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. E. H. Arouri, S. Hammoudeh, A. Lahiani, and D. K. Nguyen, “Long memory and structural breaks in modeling the return and volatility dynamics of precious metals,” The Quarterly Review of Economics and Finance, vol. 52, no. 2, pp. 207–218, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Mabrouk and C. Aloui, “One-day-ahead value-at-risk estimations with dual long-memory models: evidence from the Tunisian stock market,” International Journal of Financial Services Management, vol. 4, no. 2, pp. 77–94, 2010. View at Publisher · View at Google Scholar
  3. S. H. Tan and M. T. Khan, “Long memory features in return and volatility of the Malaysian stock market,” Economics Bulletin, vol. 30, no. 4, pp. 3267–3281, 2010. View at Google Scholar
  4. L. Nouira, I. Ahamada, J. Jouini, and A. Nurbel, “Long-memory and shifts in the unconditional variance in the exchange rate euro/US dollar returns,” Applied Economics Letters, vol. 11, no. 9, pp. 591–594, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Beine, S. Laurent, and C. Lecourt, “Accounting for conditional leptokurtosis and closing days effects in FIGARCH models of daily exchange rates,” Applied Financial Economics, vol. 12, no. 8, pp. 589–600, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. K. Choi and S. Hammoudeh, “Long memory in oil and refined products markets,” The Energy Journal, vol. 30, no. 2, pp. 97–116, 2009. View at Google Scholar · View at Scopus
  7. C. Kyrtsou, W. C. Labys, and M. Terraza, “Noisy chaotic dynamics in commodity markets,” Empirical Economics, vol. 29, no. 3, pp. 489–502, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. G. G. Rompotis, “Predictable patterns in ETFs' return and tracking error,” Studies in Economics and Finance, vol. 28, no. 1, pp. 14–35, 2011. View at Publisher · View at Google Scholar
  9. J. H. Chen and J. F. Diaz, “Spillover and leverage effects of faith-based exchange-traded funds,” Journal of Business and Policy Research, vol. 7, no. 2, pp. 1–12, 2013. View at Google Scholar
  10. J. F. T. Diaz and A. Masa, “Positive dependence and volatility asymmetry properties of the largest exchange-traded notes (ETNs),” Euro-Asian Journal of Economics and Finance, vol. 2, no. 2, pp. 100–107, 2014. View at Google Scholar
  11. J. A. Batten, C. Ciner, and B. M. Lucey, “The macroeconomic determinants of volatility in precious metals markets,” Resources Policy, vol. 35, no. 2, pp. 65–71, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Fama, “Efficient capital markets: a review of theory and empirical work,” The Journal of Finance, vol. 25, no. 2, pp. 383–417, 1970. View at Publisher · View at Google Scholar
  13. C. W. Granger and R. Joyeux, “An introduction to long-memory time series models and fractional differencing,” Journal of Time Series Analysis, vol. 1, no. 1, pp. 15–29, 1980. View at Publisher · View at Google Scholar · View at MathSciNet
  14. J. R. Hosking, “Fractional differencing,” Biometrika, vol. 68, no. 1, pp. 165–176, 1981. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation,” Econometrica, vol. 50, no. 4, pp. 987–1007, 1982. View at Publisher · View at Google Scholar · View at MathSciNet
  17. Z. Ding, C. W. J. Granger, and R. F. Engle, “A long memory property of stock market returns and a new model,” Journal of Empirical Finance, vol. 1, no. 1, pp. 83–106, 1993. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Baillie, T. Bollerslev, and H. Mikkelsen, “Fractionally integrated generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 74, no. 1, pp. 3–30, 1996. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. Y. K. Tse, “The conditional heteroscedasticity of the yen-dollar exchange rate,” Journal of Applied Econometrics, vol. 13, no. 1, pp. 49–55, 1998. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Markowitz, “Portfolio selection,” The Journal of Finance, vol. 7, no. 1, pp. 77–91, 1952. View at Publisher · View at Google Scholar
  21. N. Niarchos, Y. Tse, C. Wu, and A. Young, “International transmission of information: a study of the relationship between the U.S. and Greek stock markets,” Multinational Finance Journal, vol. 3, no. 1, pp. 19–40, 1999. View at Publisher · View at Google Scholar
  22. B.-N. Huang and C. W. Yang, “Volatility of changes in G-5 exchange rates and its market transmission mechanism,” International Journal of Finance and Economics, vol. 7, no. 1, pp. 37–50, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. X. E. Xu and H.-G. Fung, “Cross-market linkages between U.S. and Japanese precious metals futures trading,” Journal of International Financial Markets, Institutions and Money, vol. 15, no. 2, pp. 107–124, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. J.-H. Chen and C.-Y. Huang, “An analysis of the spillover effects of exchange-traded funds,” Applied Economics, vol. 42, no. 9, pp. 1155–1168, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Ruzgar and I. Kale, “The use of ARCH and GARCH models for estimating and forecasting volatility,” Kocaeli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 14, no. 2, pp. 78–109, 2007. View at Google Scholar
  26. R. Tansuchat, C. L. Chang, and M. McAleer, Modelling Long Memory Volatility in Agricultural Commodity Futures Returns, 2009.
  27. H. Goudarzi, “Modeling long memory in the indian stock market using fractionally integrated EGARCH model,” International Journal of Trade, Economics and Finance, vol. 1, no. 3, pp. 231–237, 2010. View at Publisher · View at Google Scholar