The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures

Thumbnail Image
Official URL
Full text at PDC
Publication Date
Advisors (or tutors)
Journal Title
Journal ISSN
Volume Title
Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
Google Scholar
Research Projects
Organizational Units
Journal Issue
The paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects. We modify the jump-robust covariance estimator of Koike (2016), such that the estimated matrix is positive definite. Using this approach, we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results for daily crude oil and gold futures show that the co-jumps of the two futures have significant impacts on future co-volatility, but that the impact is negligible in forecasting weekly and monthly horizons.
Unesco subjects
Aït-Sahalia, Y., and J. Jacod (2012), “Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data”, Journal of Economic Literature, 50, 1007–1050. Aït-Sahalia, Y., J. Jacod, and J. Li (2012), “Testing for Jumps in Noisy High Frequency Data”, Journal of Econometrics, 168, 207–322. Andersen, T., and T. Bollerslev (1998), “Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts”, International Economic Review, 39, 657–668. Andersen, T.G., T. Bollerslev, and F.X. Diebold (2007), “Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility”, Review of Economics and Statistics, 89, 701–720. Asai, M. and M. McAleer (2017), “The Impact of Jumps and Leverage in Forecasting Co-Volatility”, Econometric Reviews, 36, 638–650. Bahloul, W., M. Balcilar, J. Cunado, and R. Gupta (2018), “The Role of Economic and Financial Uncertainties in Predicting Commodity Futures Returns and Volatility: Evidence from a Nonparametric Causality-in-Quantiles Test”, Journal of Multinational Financial Management, 45, 52–71. Balcilar, M., Z.A. Ozdemir, and M. Shahbaz (2018), “On the Time-Varying Links between Oil and Gold: New Insights from the Rolling and Recursive Rolling Approaches”, to appear in International Journal of Finance and Economics. Bampinas G., and T. Panagiotidis (2015), “On the Relationship between Oil and Gold before and after Financial Crisis: Linear, Nonlinear and Time-Varying Causality Testing”, Studies in Nonlinear Dynamics & Econometrics, 19, 657–668. Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde, and N. Shephard (2011), “Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of The Covariation of Equity Prices with Noise and Non-Synchronous Trading”, Journal of Econometrics, 162, 149–169. Baur, D.G. and B.M. Lucey (2010), “Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold”, The Financial Review, 45, 217–229. Baur, D.G. and T.K. McDermott (2010), “Is Gold a Safe Haven? International Evidence”, Journal of Banking & Finance, 34, 1886–1898. Bickel, P. J., and E. Levina (2008a), “Regularized Estimation of Large Covariance Matrices”, Annals of Statistics, 36, 199–277. Bickel, P. J., and E. Levina (2008b), “Covariance Regularization by Thresholding”, Annals of Statistics, 36, 2577–2604. Bollerslev, T., U. Kretschmer, C. Pigorsch, and G. Tauchen (2009), “A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects”, Journal of Econometrics, 150, 151–166. Büyük¸sahin, B., and M.A. Robe (2014), “Speculators, Commodities and Crossmarket Linkages”, Journal of International Money and Finance, 42, 38–70. Chang, C.-L., Y.-Y. Li and M. McAleer (2018a), “Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice”, Energies, 11(6:1595), 1-19. Chang, C.-L. , M. McAleer, and Y. Wang (2018b), “Testing Co-volatility Spillovers for Natural Gas Spot, Futures and ETF Spot using Dynamic Conditional Covariances”, Energy, 151, 984–997. Christensen, K., S. Kinnebrock, and M. Podolskij (2012), “Pre-Averaging Estimators of the Ex-Post Covariance Matrix in Noisy Diffusion Models with Non-Synchronous Data”, Journal of Econometrics, 159, 116–133. Corsi, F., D. Pirino, and R. Ren`o (2010), “Threshold Bipower Variation and The Impact of Jumps on Volatility Forecasting”, Journal of Econometrics, 159, 276–288. Corsi, F. and R. Ren`o (2012), “Discrete-time Volatility Forecasting with Persistent Leverage Effect and the Link with Continuous-time Volatility Modeling”, Journal of Business & Economic Statistics, 30, 368–380. Degiannakis, S., and G. Filis (2017), “Forecasting Oil Price Realized Volatility using Information Channels from Other Asset Classes”, Journal of International Money and Finance, 76, 28–49. Demirer, R., K. Gkillas, R. Gupta, and C. Pierdzioch (Forthcoming), “Time-varying Risk Aversion and Realized Gold Volatility”, North American Journal of Economics and Finance. Diebold, F. and R. Mariano (1995), “Comparing Predictive Accuracy”, Journal of Business & Economic Statistics, 13, 253–263. Ewing, B.T., and F. Malik (2013), “Volatility Transmission between Gold and Oil Futures under Structural Breaks”, International Review of Economics and Finance, 25, 113–121. Fattouh, B., L. Kilian, and L. Mahadeva (2013), “The Role of Speculation in Oil Markets: What Have We Learned So Far?”, Energy Journal, 34, 7–33. Hayashi, T. and N. Yoshida (2005), “On Covariance Estimation of Nonsynchronously Observed Diffusion Processes,” Bernoulli, 11, 359–379. Koike, Y. (2016), “Estimation of Integrated Covariances in the Simultaneous Presence of Non-synchronicity, Microstructure Noise and Jumps”, Econometric Theory, 32, 533-611. Kroner, K.F., and V.K. Ng. (1998), “Modeling Asymmetric Co-movements of Assets Returns”, Review of Financial Studies, 11, 817–844. Mensi, W., M. Beljid, A. Boubaker, and S. Managi (2013), “Correlations and Volatility Spillovers across Commodity and Stock Markets: Linking Energies, Food, and Gold”, Economic Modelling, 32, 15–22. Muteba Mwamba, J.W., S. Hammoudeh, and R. Gupta (2017), “Financial Tail Risks in Conventional and Islamic Stock Markets: A Comparative Analysis”, Pacific-Basin Finance Journal, 42(C), 60–82. Patton, A.J., and K. Sheppard (2013), “Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility”, Unpublished paper, University of Oxford. Prokopczuk, M., L. Symeonidis, and C. Wese Simen (2015), “Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets”, Journal of Futures Markets, 36, 758–792. Reboredo, J.C. (2013a), “Is Gold a Safe Haven or a Hedge for the US Dollar? Implications for Risk Management”, Journal of Banking & Finance, 37, 2665–2676. Reboredo, J.C. (2013b), “Is Gold a Hedge or Safe Haven against Oil Price Movements?”, Resources Policy, 38, 130–137. Sévi, B. (2014), “Forecasting the Volatility of Crude Oil Futures using Intraday Data”, European Journal of Operational Research, 235, 643–659. Silvennoinen, A., and S. Thorp (2013), “Financialization, Crisis and Commodity Correlation Dynamics”, Journal of International Financial Markets, Institutions and Money, 24, 42–65. Smales, L.A. (2014), “News Sentiment in the Gold Futures Market”, Journal of Banking & Finance, 49, 275–286. Tang, K., and W. Xiong (2012), “Index Investment and the Financialization of Commodities”, Financial Analysts Journal, 68, 54–74. Tiwari, A.K., J. Cunado, R. Gupta, and M.E. Wohar (2018), “Volatility Spillovers across Global Asset Classes: Evidence from Time and Frequency Domains”, Quarterly Review of Economics and Finance, 70, 194–202. Tao, M., Y. Wang, Q. Yao and J. Zou (2011), “Large Volatility Matrix Inference via Combining LowFrequency and High-Frequency Approaches”, Journal of the American Statistical Association, 106, 1025–1040. Wen, F., X. Gong, and S. Cai (2016), “Forecasting the Volatility of Crude Oil Futures using HAR-Type Models with Structural Breaks”, Energy Economics, 59, 400–413. Yaya, O.S., M.M. Tumala, and C.G. Udomboso (2016), “Volatility Persistence and Returns Spillovers between Oil and Gold Prices: Analysis Before and After the Global Financial Crisis”, Resources Policy, 49, 273–281.