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Predicting volatility of the Shanghai silver futures market: What is the role of the U.S. options market?

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  • Luo, Xingguo
  • Ye, Zinan

Abstract

This paper investigates whether the CBOE Silver ETF Volatility Index (VXSLV), which is the implied volatility calculated from the U.S. options market, contains information for predicting the volatility of the Shanghai silver futures market. In particular, we compare its performance with variables observed in the Chinese market. We find both in-sample and out-of-sample evidence that the VXSLV significantly improves daily and weekly volatility forecasts based on realized volatilities. Moreover, when market variables including trading volume, open interest and momentum are included, the VXSLV remains significant and enhances the forecasting performance of the market variables.

Suggested Citation

  • Luo, Xingguo & Ye, Zinan, 2015. "Predicting volatility of the Shanghai silver futures market: What is the role of the U.S. options market?," Finance Research Letters, Elsevier, vol. 15(C), pages 68-77.
    • Handle: RePEc:eee:finlet:v:15:y:2015:i:c:p:68-77
    DOI: 10.1016/j.frl.2015.08.005
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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    3. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    4. Martin Martens & Jason Zein, 2004. "Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1005-1028, November.
    5. Haugom, Erik & Langeland, Henrik & Molnár, Peter & Westgaard, Sjur, 2014. "Forecasting volatility of the U.S. oil market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 1-14.
    6. Paul Berhanu Girma & Mbodja Mougoué, 2002. "An empirical examination of the relation between futures spreads volatility, volume, and open interest," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(11), pages 1083-1102, November.
    7. Bessembinder, Hendrik & Seguin, Paul J., 1993. "Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(1), pages 21-39, March.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Busch, Thomas & Christensen, Bent Jesper & Nielsen, Morten Ørregaard, 2011. "The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets," Journal of Econometrics, Elsevier, vol. 160(1), pages 48-57, January.
    10. Chan, Kam C. & Fung, Hung-Gay & Leung, Wai K., 2004. "Daily volatility behavior in Chinese futures markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(5), pages 491-505, December.
    11. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    12. Nguyen, Duc Khuong & Sousa, Ricardo M. & Uddin, Gazi Salah, 2015. "Testing for asymmetric causality between U.S. equity returns and commodity futures returns," Finance Research Letters, Elsevier, vol. 12(C), pages 38-47.
    13. Onan, Mustafa & Salih, Aslihan & Yasar, Burze, 2014. "Impact of macroeconomic announcements on implied volatility slope of SPX options and VIX," Finance Research Letters, Elsevier, vol. 11(4), pages 454-462.
    14. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    15. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    16. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    17. repec:bla:jfinan:v:53:y:1998:i:1:p:219-265 is not listed on IDEAS
    18. James Doran & Ehud Ronn, 2005. "The bias in Black-Scholes/Black implied volatility: An analysis of equity and energy markets," Review of Derivatives Research, Springer, vol. 8(3), pages 177-198, December.
    19. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," The Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
    20. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    21. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    6. Li, Wenlan & Cheng, Yuxiang & Fang, Qiang, 2020. "Forecast on silver futures linked with structural breaks and day-of-the-week effect," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    7. Luo, Tao & Sun, Huaping & Zhang, Lixia & Bai, Jiancheng, 2024. "Do the dynamics of macroeconomic attention drive the yen/dollar exchange market volatility?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 597-611.
    8. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    9. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.
    10. Bai, Yujuan & Pan, Zhiyuan & Liu, Li, 2019. "Improving futures hedging performance using option information: Evidence from the S&P 500 index," Finance Research Letters, Elsevier, vol. 28(C), pages 112-117.
    11. Song, Wonho & Ryu, Doojin & Webb, Robert I., 2016. "Overseas market shocks and VKOSPI dynamics: A Markov-switching approach," Finance Research Letters, Elsevier, vol. 16(C), pages 275-282.
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    13. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.

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    More about this item

    Keywords

    Silver futures; Volatility forecasting; Volatility index;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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