Skip to main content
SpringerLink
Log in

GARCH modeling of five popular commodities

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

Flexible models for the innovation process of GARCH models have been limited. Here, we show the flexibility of two recently proposed distributions due to Zhu and Zinde-Walsh (J Econom 148:86–99, 2009) and Zhu and Galbraith (J Econom 157:297–305, 2010) by means of GARCH modeling of five popular commodities. The five commodities considered are Cocoa bean, Brent crude oil, West Texas intermediate crude oil, Gold and Silver. For each commodity, one of the two models due to Zhu and Zinde-Walsh (2009) and Zhu and Galbraith (2010) is shown to perform better than those commonly known.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Acatrinei M, Gorun A, Marcu N (2013) A DCC-GARCH model to estimate the risk to the capital market in Romania. Rom J Econ Forecast 16:136–148

    Google Scholar 

  • Agnolucci P (2009) Volatility in crude oil futures: a comparison of the predictive ability of GARCH and implied volatility models. Energy Econ 31:316–321

    Article  Google Scholar 

  • Aloui C, Mabrouk S (2010) Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy 38:2326–2339

    Article  Google Scholar 

  • Anderson TW, Darling DA (1954) A test of goodness-of-fit. J Am Stat Assoc 49:765–769

    Article  Google Scholar 

  • Assis K, Amran A, Remali Y (2010) Forecasting cocoa bean prices using univariate time series models. J Arts Sci Commerce 1:71–80

  • Azzalini A (1985) A class of distributions which includes the normal ones. Scand J Stat 12:171–178

    Google Scholar 

  • Azzalini A (1986) Further results on a class of distributions which includes the normal ones. Statistica 46:199–208

    Google Scholar 

  • Barndorff-Nielsen O (1977) Exponentially decreasing distributions for the logarithm of particle size. Proc R Soc Lond Ser A Math Phys Sci 353:401–409

    Article  Google Scholar 

  • Belval B (2007) Silver. The Rosen Publishing Group, New York

    Google Scholar 

  • Bildirici M, Ersin OO (2009) Improving forecasts of GARCH family models with the artificial neural networks: an application to the daily returns in Istanbul Stock Exchange. Expert Syst Appl 36: 7355–7362

  • Bolgorian M, Gharli Z (2011) A multifractal detrended fluctuation analysis of Gold price fluctuations. Acta Phys Pol B 42:159–169

    Article  Google Scholar 

  • Campbell JY, Hentschel L (1992) No news is good news: an asymmetric model of changing volatility in stock returns. J Financ Econ 31:281–318

    Article  Google Scholar 

  • Chang H-L, Su C-W (2010) The relationship between the Vietnam stock market and its major trading partners—TECM with bivariate asymmetric GARCH model. Appl Econ Lett 17:1279–1283

    Article  Google Scholar 

  • Chang S-C, Shen C-H (2011) The effect of exchange-rate uncertainty on unemployment in three developing Asian countries: evidence from bivariate GARCH approach. Appl Econ Lett 18:783–788

    Article  Google Scholar 

  • Cheng W-H, Hung J-C (2011) Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns. J Empir Finance 18:160–173

    Article  Google Scholar 

  • Cheong CW (2011) Parametric and non-parametric approaches in evaluating martingale hypothesis of energy spot markets. Math Comput Model 54:1499–1509

    Article  Google Scholar 

  • Corti C, Holliday R (2010) Gold: science and applications. CRC Press, Boca Raton

    Google Scholar 

  • Dajcman S, Festic M (2012) Interdependence between the Slovenian and European stock markets—a DCC-GARCH analysis. Econ Res 25:379–395

    Google Scholar 

  • de Moivre A (1738) The doctrine of chances. ISBN 0821821032

  • De Vany A, Walls WD (2002) Does Hollywood make too many R-rated movies? Risk, stochastic dominance, and the illusion of expectation. J Bus 75:425–451

    Article  Google Scholar 

  • Deepika MG, Gautam N, Rajkumar M (2012) Forecasting price and analysing factors influencing the price of gold using ARIMA model and multiple regression analysis. Working Paper Number 135, Amrita School of Business, India

  • dos Santos Coelhoa L, Santos AAP (2011) A RBF neural network model with GARCH errors: application to electricity price forecasting. Electr Power Syst Res 81:74–83

    Article  Google Scholar 

  • Drakos AA, Kouretas GP, Zarangas LP (2010) Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model. Int J Finance Econ 15:331–350

    Google Scholar 

  • Erb CB, Harvey CR (2012) An impressionistic view of the “real” price of gold around the world. Working Paper. National Bureau of Economic Research, Cambridge

  • Essegbey GO, Ofori-Gyamfi E (2012) Ghana Cocoa industry—an analysis from the innovation system perspective. Technol Invest 3:276–286

    Article  Google Scholar 

  • Fama EF (1965) The behavior of stock-market prices. J Bus 38:34–105

    Article  Google Scholar 

  • Fernandez C, Steel MFJ (1998) On Bayesian modeling of fat tails and skewness. J Am Stat Assoc 93: 359–371

  • Gauss CF (1809) Theoria motvs corporvm coelestivm in sectionibvs conicis Solem ambientivm

  • Geary RC (1947) Testing for normality. Biometrika 34:209–242

    Article  Google Scholar 

  • Gosset WS (1908) The probable error of a mean. Biometrika 6:1–25

    Article  Google Scholar 

  • Gregory-Allen R, Lu H, Stork P (2012) Asymmetric extreme tails and prospective utility of momentum returns. Econ Lett 117:295–297

    Article  Google Scholar 

  • Heshelow K (2010) Investing in oil and gas—the ABC’s of direct participation program: the state of oil and gas, and why you need to learn about this now. iUniverse, Indianapolis

  • Idris N, Hameed AAA, Niti MA, Arshad FM (2011) Export performance and trade competitiveness of the Malaysian cocoa products. Afr J Bus Manag 5:12291–12308

    Google Scholar 

  • James S (2010) Silver price set to rise with gold. Gold Min J 1

  • Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett 6:255–259

    Article  Google Scholar 

  • Lecumberri E, Mateos R, Izquierdo-Pulido M, Rupérez P, Goya L, Bravo L (2007) Dietary fibre composition, antioxidant capacity and physico-chemical properties of a fibre-rich product from cocoa. Food Chem 104:948–954

    Article  Google Scholar 

  • Lee C-C, Zeng J-H (2011) Revisiting the relationship between spot and futures oil prices: evidence from quantile cointegrating regression. Energy Econ 33:924–935

    Article  Google Scholar 

  • Lee Y-H, Chiu C-L (2011) Arbitrage behaviour in the exchange rates of Taiwan and Japan: applying the smooth transition vector error correction model with GJR-GARCH and spillover volatility. Appl Econ 43:1935–1943

    Article  Google Scholar 

  • Lee Y-H, Huang Y-L, Yang H-J (2012) The asymmetric long-run relationship between crude oil and gold futures. Glob J Bus Res 6:9–15

    Google Scholar 

  • Lu J-R (2011) Dealer spread and portfolio selection under price risks: evidence from the gold service industry. Serv Ind J 31:975–996

    Article  Google Scholar 

  • Mandelbrot B (1963) The variation of certain speculative prices. J Bus 36:394–419

    Article  Google Scholar 

  • Maurice N, Davis J (2011) Unravelling the underlying causes of price volatility in world coffee and cocoa commodity markets. Working Paper, UNCTAD Special Unit on Commodities

  • Nakamura T, Small M (2007) Tests of the random walk hypothesis for financial data. Physica A 377:599–615

    Article  Google Scholar 

  • Novotny F (2012) The link between the Brent crude oil price and the US dollar exchange rate. Prague Econ Pap 2:220–232

    Google Scholar 

  • Owen D (1980) A table of normal integrals. Commun Stat 9:389–419

    Article  Google Scholar 

  • R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

  • Reboredo JC (2013) Is gold a hedge or safe haven against oil price movements? Resour Policy 38:130–137

    Article  Google Scholar 

  • Salisu AA, Fasanya IO (2013) Modelling oil price volatility with structural breaks. Energy Policy 52: 554–562

  • Shafiee S, Topal E (2010) An overview of global gold market and gold price forecasting. Resour Policy 35:178–189

    Article  Google Scholar 

  • Speight JG (2011) An introduction to petroleum technology, economics, and politics. Wiley, New York

    Book  Google Scholar 

  • Subbotin MT (1923) On the law of frequency of errors. Matematicheskii Sbornik 31:296–301

    Google Scholar 

  • Teresiene D (2009) Lithuanian stock market analysis using a set of GARCH models. J Bus Econ Manag 10:349–360

    Article  Google Scholar 

  • Theodossiou P (1998) Financial data and the skewed generalized \(t\) distribution. Manag Sci 44:1650–1661

    Article  Google Scholar 

  • Ubilava D, Helmers CG (2011) The ENSO impact on predicting world cocoa prices. Paper Number 103528, Annual Meeting, Pittsburgh

  • World Gold Council (2012) Gold demand trends. Second Quarter 2012. World Gold Council, London

  • Wuertz D, Chalabi Y (2013) fGarch: Rmetrics—autoregressive conditional heteroskedastic modelling. http://cran.r-project.org/web/packages/fGarch/

  • Yazdani-Chamzini A, Yakhchali SH, Volungeviciene D, Zavadskas EK (2012) Forecasting gold price changes by using adaptive network fuzzy inference system. J Bus Econ Manag 13:994–1010

    Article  Google Scholar 

  • Ye F, Garcia TP, Pourahmadi M, Lord D (2012) Extension of negative binomial GARCH model analyzing effects of gasoline price and miles traveled on fatal crashes involving intoxicated drivers in Texas. Transp Res Rec 2279:31–39

    Article  Google Scholar 

  • Yildirim I, Ozsahin S, Akyuz KC (2011) Prediction of the financial return of the paper sector with artificial neural networks. Bioresources 6:4076–4091

    Google Scholar 

  • Zhang Y-J, Wei Y-M (2010) The crude oil market and the gold market: evidence for cointegration, causality and price discovery. Resour Policy 35:168–177

    Article  Google Scholar 

  • Zhu D, Galbraith JW (2010) A generalized asymmetric Student-\(t\) distribution with application to financial econometrics. J Econom 157:297–305

    Article  Google Scholar 

  • Zhu D, Galbraith JW (2011) Modelling and forecasting expected shortfall with the generalized asymmetric Student-\(t\) and asymmetric exponential power distributions. J Empir Finance 18:765–778

    Article  Google Scholar 

  • Zhu D, Zinde-Walsh V (2009) Properties and estimation of asymmetric exponential power distribution. J Econom 148:86–99

  • Ziaei SM (2012) Asia Pacific business innovation and technology management society effects of gold price on equity, bond and domestic credit: Evidence from ASEAN +3. Procedia 40:341–346

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Editor and the two referees for careful reading and for their comments which greatly improved the paper.

Author information

Authors and Affiliations

  1. School of Mathematics, University of Manchester, Manchester , M13 9PL, UK

    Saralees Nadarajah, Emmanuel Afuecheta & Stephen Chan

Authors
  1. Saralees Nadarajah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Afuecheta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stephen Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saralees Nadarajah.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nadarajah, S., Afuecheta, E. & Chan, S. GARCH modeling of five popular commodities. Empir Econ 48, 1691–1712 (2015). https://doi.org/10.1007/s00181-014-0845-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-014-0845-3

Keywords

Search

Navigation