Document Type : Research Paper

Authors

1 Associate Professor, Department of Economics, Faculty of Management and Economics, Sharif University of Technology,Tehran, Iran

2 Ph.D. Student in Economics, Semnan University,Semnan, Iran,

Abstract

In this paper, Value at Risk for Gold prices Is estimated by the Extreme Value theory and parametric method with Normal and t-student distribution for disturbance term in the mean equation together with a range of the conditional variances estimation techniques including, GARCH (1.1), TGARCH, EGARCH, PGARCH, FIGARCH and FIEGARCH Models. The two-stage Back-Testing method is used to evaluate the adequacy and accuracy of the calculation methods. Furthermore, we rank the accuracy of the estimation methods by a loss function. Our findings show that the most accurate method, In terms of the value of the loss function and among the applied econometrics methods, is VaR by t-student distribution for gold return and PGARCH for the long position and acceptable performance for the short position.

Keywords

فلاح‌پور، سعید، فاطمه رضوانی و محمدرضا رحیمی (1394)، «برآورد ارزش در معرض ریسک شرطی با استفاده از مدل‌های ناهمسانی واریانس شرطی متقارن و نامتقارن در بازار طلا و نفت»، فصلنامه علمی-پژوهشی دانش مالی تحلیل اوراق بهادار، سال هشتم، شماره 28، صص 18-1.
فلاح‌پور، سعید و احسان احمدی (1393)، «تخمین ارزش در معرض ریسک پرتفوی نفت و طلا با بهره‎مندی از روش کاپیولا‌ـ گارچ»، تحقیقات مالی، دوره 16، شماره 2، صص 326-309. 
کشاورز، غلامرضا و کبری مفتخر دریایی (1397)، «تاثیر سرایت بازده و تلاطم در برآورد ارزش در معرض ریسک سبد دارایی، متشکل از طلا، ارز و سهام»، تحقیقات اقتصادی، دوره 53، شماره 1، صص 152-117.
Abad, P., & Benito, S. (2013). A detailed comparison of value at risk estimates. Mathematics and Computers in Simulation, 94, 258-276.
Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996), “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 74, 3-30.
Baker, S. A., & Van-Tassel, R. C. (1985), “Forecasting the Price of Gold: A Fundamental Approach”, Journal of Atlantic Economics, 13, 43-52.
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31, 307-327.
Bollerslev, T., & Mikkelsen, H. O. (1996), “Modeling and Pricing Long Memory in Stock Market Volatility”, Journal of Econometrics, 73, 151-184.
Christie-David, R. M. (2000), “Do Macroeconomic News Releases Affect Gold and Silver prices”, Journal of Economic Business, 405-421.
Christoffersen, P. (1998), “Evaluating Interval Forecasting”, International Economic Review, 39, 841-862.
Egan, P. a. (2001), “The Performance of Defensive Investments”, Journal of Alternative Investments, 4, 49-56.
Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50 (4), 987-1007.
Fisher, R. A. (1928), “Limiting Forms of the Frequency Distribution of the Largest or Smallest Member of a Sample”, Proceedings of the Cambridge Philosophical Society 24, 180-190.
Genton, & Marchenko. (2010), “A Suite of Commands for Fitting the Skew-Normal and Skew-T Models”, The Stata Journal, 10, 507–539.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993), “On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks”, Journal of Finance, 48 (5), 1779-1801.
Kaufmann, T., & Winters, R. (1989), “The Price of Gold: A Simple Model”, Resource Policy, 19, 309-318.
Kupiec, P. (1995), “Techniques for Verifying the Accuracy of Risk Measurement Models”, Journal of Derivatives, 2, 73-84.
Lawrence, C. (2003), Why is Gold Different from Other Assets? An Empirical Investigation. World Gold Council.
McNeil, A. J. (1999), “Extreme Value Theory for Risk Managers,” Internal Modeling and CAD II, 93-113.
McNeil, A. J. (2005), Quantitative risk management: Concepts, techniques and tools. Princeton: Princeton University Press.
Nelson, D. B. (1991), “Conditional Heteroskedasticity in Asset Returns: A Nee Approach”, Econometrica, 59 (2), 347-370.
Rozga, A., & Arnerić, J. (2009), “Dependence Between Volatility Persistence, Kurtosis and Degrees of Freedom”, Revista Investigacion Operaconal, vol 30, 32-39.
Şener, E., Baronyan, S., & Mengütürk, L. A. (2012), “Ranking the Predictive Performances of Value-at-risk Estimation Methods”, International Journal of Forecasting, 28, 849-873.
Sherman, E. J. (1983), “A Gold Pricing Model”, Journal of Portfolio Management, 9(3), 60-70.
Smith, R. L. (1987), “Estimating Tails of Probability Distributions”, Annals of Statistics, 15, 1174-1207.
Trück, S., & Liang, K. (2012), “Modelling and Forecasting Volatility in the Gold Market”, International Journal of Banking and Finance, 9, 48-80.
Tully, E., & Lucey, B. M. (2007), “A Power GARCH Examination of the Gold Market”, Research in International Business and Finance, 21(2), 316-325.
Zivot, E., & Wang, J. (2010), Modeling Financial Time Series with S-plus, New York, NY: Springer.