Home  Journals  Economic Analysis: Theory and Practice  20(419) - 2015 May

Price behavior forecasting and investment in gold risk assessment

Vol. 14, Iss. 20, MAY 2015

Article PDF Version

Available online: 7 June 2015

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: 

Pages: 50-56

Subject The article studies the price behavior in the gold market as gold is a reserve metal for the world economy.
     Objectives The purpose of the study is to obtain prediction estimates of gold price, to assess the risk of investing in gold, and to perform an econometric analysis to identify the factors having effect on the gold price.
     Methods The methodology base includes the analysis of trends, seasonality, and techniques to reveal autoregressive time series patterns of an isolated time series. To study the of multivariate time series, I applied econometric techniques of cointegration and vector autoregressive modeling. The information base of the research includes the monthly data on the gold prices per gram and on a number of macroeconomic indicators, like the Dow Jones index, monetary gold reserves of Russia, the dollar exchange rate, the refinance rate of the Federal Reserve, inflation in the United States.
     Results I have developed a trend-seasonal autocorrelation model to predict the dynamics of the gold price in view of the 2008 crisis, a and vector autoregressive model with the error correction mechanism for a multivariate time series.
     Conclusions and Relevance Under the model, the short-term forecast of gold price has an average relative error of 3.8%. The econometric modeling shows that the gold price is significantly determined by the Dow Jones Index, monetary gold reserves of Russia, the US inflation rate and the refinance rate of the Federal Reserve System, as well as by long-term market mechanisms of price adjustment. For the studied time period, the estimated risk of investing in gold is 11.98% at the level of reliability of 0.99.

Keywords: gold, forecasting, vector autoregression, investment risk

References:

1. Aleksandrovskaya Yu.P. Ispol'zovanie fraktal'nykh metodov dlya analiza finansovykh ryadov [The use of fractal methods to analyze financial series]. Vestnik Kazanskogo tekhnologicheskogo universiteta = Bulletin of KSTU, 2014, vol. 17, no. 18, pp. 257–261.
2. Verbik M. Putevoditel' po sovremennoi ekonometrike [A guide to modern econometrics]. Moscow, Nauchnaya kniga Publ., 2008, 616 p.
3. Volkov R.G. Zoloto v zolotovalyutnykh rezervakh Rossii: skol'ko ego nuzhno? [The gold in the Russian gold and foreign currency reserves: how much do we need it?]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2013, no. 28, pp. 15–20.
4. Gaukhman O.L. Tsentral'nye banki na mirovom rynke zolota [Central banks in the global gold market]. Bankovskoe delo = Banking, 2009, no. 3, pp. 60–64.
5. Golikova L.A., Kozina V.V. Vozmozhnosti ispol'zovaniya statisticheskikh metodov dlya analiza i prognozirovaniya mirovoi tseny zolota [Usability of statistical methods to analyze and forecast world price for gold]. Severo-Vostochnyi nauchnyi zhurnal = North East Scientific Journal, 2012, no. 2, pp. 22–24.
6. Gusev K.N. Zoloto vozvrashchaet utrachennye pozitsii [Gold recovers ground]. Bankovskoe delo = Banking, 2007, no. 6, pp. 44–46.
7. Zuev K.N. Otsenka vliyaniya sprosa i predlozheniya na tsenu zolota [Assessing the impact of supply and demand on gold price]. Available at: Link. (In Russ.)
8. Linkova M.A. Prognozirovanie dinamiki indeksa tseny dragmetallov na primere zolota. V kn.: XXI vek: itogi proshlogo i problemy nastoyashchego plyus [Predicting the dynamics of precious metals price index on the case for gold. In: 21st Century: Results of Past and Problems of Present Plus], 2012, no. 4, pp. 197–204.
9. Mironov D.I. Faktory, vliyayushchie na tsenu zolota [Factors influencing the gold price]. Biznes v zakone = Business in Law, 2011, no. 2, pp. 248–251.
10. Petrosov A.A., Potemkina A.A. Dolgosrochnoe prognozirovanie tseny zolota [Long-term forecasting of gold prices]. Gornyi zhurnal = Mining Journal, 2013, no. 12, pp. 25–28.
11. Rudnitskii A.G., Rudnitskaya A.A. Determinirovannyi khaos i nelineinyi prognoz dinamiki tsen zolota na rynkakh Pol'shi, Rossii i Ukrainy [Deterministic chaos and nonlinear prediction of gold prices in the markets of Poland, Russia and Ukraine]. Ekonomika i predprinimatel'stvo = Economy and Entrepreneurship, 2012, no. 1, pp. 17–25.
12. Fantazzini D. Ekonometricheskii analiz finansovykh dannykh v zadachakh upravleniya riskom [Econometric analysis of financial data in risk management]. Prikladnaya ekonometrika = Applied Econometrics, 2008, no. 2, pp. 91–137.
13. Fedorova E.A., Cherepennikova Yu.G. Analiz zavisimosti tseny na zoloto i indeksa RTS dlya rossiiskogo rynka s vyyavleniem krizisnykh periodov [Analysis of dependence of gold price and the RTS index for the Russian market and crisis periods identification]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2012, no. 44, pp. 63–68.
14. Shchegoleva N.G., Khabarov V.I. Transformatsiya rynka zolota i ego perspektivy [The transformation of the gold market and its perspectives]. Finansy i kredit = Finance and Credit, 2012, no. 25, pp. 31–39.
15. Engle R.F., Granger C.W.J. Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 1987, vol. 55, no. 2, pp. 251–276.
16. Green W.H. Econometric Analysis. Prentice Hall International, Inc., 2003, 193 p.
17. Hamilton J. Time Series Analysis. Princeton Univ. Pr., 1994, 816 p.
18. Johansen S. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 1991, no. 59, pp. 1551–1580.
19. Jorion P. Financial Risk Manager Handbook. Wiley, 2007, 752 p.
20. Lütkepohl H. New Introduction to Multiple Time Series Analysis. Berlin, Springer-Verlag, 2005, 764 p.

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