Skip to main content
SpringerLink
Log in

Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

In this paper, we investigate volatility spillovers and dynamic correlations between crude oil and stock markets using GARCH-class models. We focus on the dynamic relationships of seven major oil-exporting countries and nine oil-importing countries. Our main findings based on in-sample and out-of-sample evidence suggest that the volatility spillovers and dynamic correlations between global crude oil market and a country’s stock market depend on the net position of oil imports and exports of this country in the world market. In addition, crude oil risk can be better hedged by investing in stocks of oil-exporting countries than in those of oil-importing countries.

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

Similar content being viewed by others

Notes

  1. It is known that more complex models always have more parameters to be estimated, and therefore, they always have greater estimation errors in the process of out-of-sample forecasting (see, e.g., Jorion 1992; DeMiguel et al. 2009). Due to the existence of estimation error, it is not necessary that multivariate GARCH models generate more accurate forecasts than simple univariate ones.

  2. The acronym comes from synthesized work on multivariate GARCH models by Baba, Engle, Kraft, and Kroner. Unlike the VEC model of Bollerslev et al. (1988), the BEKK model does not need to impose strong restrictions on the parameters to ensure the positivity of variance and covariance matrix, \(\hbox {H}_{\mathrm{t}}\).

  3. It may be argued that in prior to model the volatility relationships, we should model the return relationships. For example, Aloui and Jammazi (2009) consider the stylized fact that stock returns react asymmetrically to changes in price of crude oil, depending on whether stock markets are in bullish or bearish phases (also see the references therein). Actually, we find that if we do so, we can obtain the similar results with those in Table 1. As our main interest is volatility comovement rather than return comovement and also for the purpose of reducing the burden of estimating and forecasting, we assume that the conditional mean is a constant following Koopman et al. (2005) and Wang and Wu (2012a, b).

  4. We also calculate the conditional correlations using BEKK models. They are similar to the correlations from DCC models. To save space, we do not report these correlations but they are available upon request.

  5. To save space, we only give the probabilities of regime 1. The probabilities of regime 2 is equal to 1 minus those of regime 1.

  6. According to Engle (2002) and Caporin and McAleer (2012), the DCC and CCC parameters can be estimated using a two-step method, the first step being univariate model estimates for each series and the second step being the correlation estimates. Based on the two-step method, the volatility forecasts obtained from the CCC and the DCC model are equivalent to those from the univariate GARCH model. The only difference is the correlation term. Thus, in this paper, the performances of the CCC and the DCC models are not evaluated in the sense of volatility forecasting.

  7. Here, we thank an anonymous referee for this suggestion.

  8. The recent studies on forecasting oil market can be seen in Nomikos and Pouliasis (2011) and Wang and Wu (2012a, b).

References

  • Akram QF (2004) Oil prices and exchange rates: Norwegian evidence. Econom J 7:476–504

    Article  Google Scholar 

  • Aloui C, Jammazi R (2009) The effects of crude oil shocks on stock market shifts behavior: a regime switching approach. Energy Econ 31:789–799

    Article  Google Scholar 

  • Amano RA, van Norden S (1998) Oil prices and the rise and fall of the US real exchange rate. J Int Money Finance 17:299–316

    Article  Google Scholar 

  • Apergis N, Miller SM (2009) Do structural oil-market shocks affect stock prices? Energy Econ 31:569–575

    Article  Google Scholar 

  • Arouri MEH (2011) Does crude oil move stock markets in Europe? a sector investigation. Econ Model 28:1716–1725

    Article  Google Scholar 

  • Arouri MEH, Lahiani A, Nguyen DK (2011) Return and volatility transmission between world oil prices and stock markets of GCC countries. Econ Model 28:1815–1825

    Article  Google Scholar 

  • Arouri MEH, Jouini J, Nguyen DK (2012) On the impacts of oil price fluctuations on European equity markets: volatility spillover and hedging effectiveness. Energy Econ 34:611–617

    Article  Google Scholar 

  • Ashley R, Granger CWJ, Schmalensee R (1980) Advertising and aggregate consumption: an analysis of causality. Econometrica 48:1149–1167

    Article  Google Scholar 

  • Baillie RT, Myers R (1991) Bivariate GARCH estimation of the optimal commodity futures hedge. J Appl Econom 6:109–124

    Article  Google Scholar 

  • Barsky RB, Kilian L (2004) Oil and macroeconomy since the 1970s. J Econ Perspect 18:115–134

    Article  Google Scholar 

  • Billio M, Caporin M (2005) Multivariate Markov switching dynamic conditional correlation GARCH representations for contagion analysis. Stat Methods Appl 14:145–161

  • Bjørnland HC (2009) Oil price shocks and stock market booms in an oil-exporting country. Scott J Polit Econ 56:232–254

    Article  Google Scholar 

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31:307–327

    Article  Google Scholar 

  • Bollerslev T (1990) Modelling the coherence in short-run nominal exchange rate: a multivariate generalized ARCH approach. Rev Econ Stat 72:498–505

    Article  Google Scholar 

  • Bollerslev T, Wooldridge JM (1992) Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econom Rev 11:143–172

  • Bollerslev T, Engle RF, Nelson D (1994) ARCH models. In: Engle RF, McFadden DL (eds) Handbook of econometrics, vol. IV. Elsevier Science B.V, Amsterdam, pp 2961–3038

    Google Scholar 

  • Bollerslev T, Engle RF, Wooldridge JM (1988) A capital asset model with time-varying covariances. J Polit Econ 96:116–131

    Article  Google Scholar 

  • Caporin M, McAleer M (2012) Do we really need both BEKK and DCC? a tale of two covariance models. J Econ Surv 26:736–751

    Article  Google Scholar 

  • Chang C-L, McAleer M, Tansuchat R (2010) Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ 32:1445–1455

    Article  Google Scholar 

  • Chang C-L, McAleer M, Tansuchat R (2011) Crude oil hedging strategies using dynamic multivariate GARCH. Energy Econ 33:912–923

    Article  Google Scholar 

  • Chen S-S (2009) Oil price pass-through into inflation. Energy Econ 31:126–133

    Article  Google Scholar 

  • DeMiguel V, Garlappi L, Nogales F, Uppal R (2009) a generalized approach to portfolio optimization: improving performance by constraining portfolio norms. Manage Sci 55:798–812

    Article  Google Scholar 

  • Diebold FX, Lopez JA (1996) Forecast evaluation and combination. In: Maddala GS, Rao CR (eds) Handbook of statistics: statistical methods in finance, vol. 14. North-Holland, Amsterdam, pp 241–268

    Chapter  Google Scholar 

  • Elder J, Serletis A (2010) Oil price uncertainty. J Money Credit Bank 42:1137–1159

    Article  Google Scholar 

  • Engle RF (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007

    Article  Google Scholar 

  • Engle RF (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20:339–350

    Article  Google Scholar 

  • Engle RF, Kroner KF (1995) Multivariate simultaneous generalized ARCH. Econ Theory 11:122–150

    Article  Google Scholar 

  • Fan Y, Xu J-H (2011) What has driven oil prices since 2000? a structural change perspective. Energy Econ 33:1082–1094

    Article  Google Scholar 

  • Ferderer JP (1996) Oil price volatility and the macroeconomy. J Macroecon 18:1–26

    Article  Google Scholar 

  • Filis G, Degiannakis S, Foros C (2011) Dynamic correlation between stock market and oil prices: the case of oil-importing and oil-exporting countries. Int Rev Financ Anal 20:152–164

    Article  Google Scholar 

  • Hamilton JD (1983) Oil and the macroeconomy since World War II. J Polit Econ 91:228–248

    Article  Google Scholar 

  • Hamilton JD (1996) This is what happened to the oil price–macroeconomy relationship. J Monet Econ 38:215–220

    Article  Google Scholar 

  • Hamilton JD (2003) What is an oil shock? J Econom 113:363–398

    Article  Google Scholar 

  • Hamilton JD (2008) Macroeconomics and ARCH. NBER working paper 14151

  • Hamilton JD (2009) Understanding crude oil prices. Energy J 30:179–206

    Article  Google Scholar 

  • Hansen PR (2005) A test for superior predictive ability. J Bus Econ Stat 23:365–380

    Article  Google Scholar 

  • Hansen PR, Lunde A (2006) Consistent ranking of volatility models. J Econom 131:97–121

    Article  Google Scholar 

  • Inoue A, Kilian L (2004) In-sample or out-of-sample tests of predictability: Which one should we use? Econom Rev 23:371–402

    Article  Google Scholar 

  • Johnson LL (1960) The theory of hedging and speculation in commodity futures. Rev Econ Stud 27:139–151

    Article  Google Scholar 

  • Jorion P (1992) Portfolio optimization in practice. Financ Anal J 48:68–74

    Article  Google Scholar 

  • Jung H, Park C (2011) Stock market reaction to oil price shocks: a comparison between an oil-exporting economy and an oil-importing economy. J Econ Theory Econom 22:1–29

    Google Scholar 

  • Kilian L (2009) Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am Econ Rev 99:1053–1069

    Article  Google Scholar 

  • Kilian L, Park C (2009) The impact of oil price shocks on the U.S. stock market. Int Econ Rev 50:1267–1287

    Article  Google Scholar 

  • Koopman SJ, Jungbacker B, Hol E (2005) Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements. J Empir Finance 12:445–475

    Article  Google Scholar 

  • Ku Y-H, Chen H-C, Chen K-H (2007) On the application of the dynamic conditional correlation model in the estimating optimal time-varying hedge ratios. Appl Econ Lett 14:503–509

    Article  Google Scholar 

  • LeBlanc M, Chinn MD (2004) Do high oil prices presage inflation? Bus Econ 39:38–48

    Google Scholar 

  • Ljung M, Box G (1978) On a measure of lack of fit in time series models. Biometrika 65:297–303

    Article  Google Scholar 

  • Lopez JA (2001) Evaluation of predictive accuracy of volatility models. J Forecast 20:87–109

    Article  Google Scholar 

  • Malik F, Hammoudeh S (2007) Shock and volatility transmission in the oil, US and Gulf equity markets. Int Rev Econ Finance 16:357–368

    Article  Google Scholar 

  • Nomikos NK, Pouliasis PK (2011) Forecasting petroleum futures markets volatility: the role of regimes and market conditions. Energy Econ 33:321–337

    Article  Google Scholar 

  • Park J, Ratti RA (2008) Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Econ 30:2587–2608

    Article  Google Scholar 

  • Patton AJ (2011) Volatility forecast comparison using imperfect volatility proxies. J Econ 160:246–256

    Article  Google Scholar 

  • Politis DN, Romano JP (1994) The stationary bootstrap. J Am Stat Assoc 89:1303–1313

    Article  Google Scholar 

  • Sadorsky P (1999) Oil price shocks and stock market activity. Energy Econ 21:449–469

    Article  Google Scholar 

  • Sadorsky P (2012) Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Econ 34:248–255

    Article  Google Scholar 

  • Sadorsky P (2014) Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Econ 43:72–81

    Article  Google Scholar 

  • Wang Y, Wu C (2012a) Energy prices and exchange rates of the U.S. dollar: further evidence from linear and nonlinear causality analysis. Econ Model 29:2289–2297

    Article  Google Scholar 

  • Wang Y, Wu C (2012b) Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models? Energy Econ 34:2167–2181

    Article  Google Scholar 

  • Wang Y, Wu C, Yang L (2013) Oil price shocks and stock market activities: evidence from oil-importing and oil-exporting countries. J Comp Econ 41:1220–1239

    Article  Google Scholar 

  • Wei C (2003) Energy, the stock market, and the putty-clay investment model. Am Econ Rev 93:311–323

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the editor, Robert Kunst, and two referees for making many constructive and useful comments and suggestions that helped us to improve the paper. The suggested additional analyses and changes proved to be important in making our findings more comprehensive, convincing, and better understood. This paper is supported by the National Science Foundation of China (No. 71401077).

Author information

Authors and Affiliations

  1. School of Economics and Mangement, Nanjing University of Science and Technology, XiaoLinwei 200, Xuanwu District, Nanjing, China

    Yudong Wang

  2. School of Finance, Nanjing Audit University, Nanjing, China

    Li Liu

Authors
  1. Yudong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yudong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Liu, L. Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging. Empir Econ 50, 1481–1509 (2016). https://doi.org/10.1007/s00181-015-0983-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-015-0983-2

Keywords

JEL Classification

Search

Navigation