Skip to main content
Log in

Futures Hedging in CSI 300 Markets: A Comparison Between Minimum-Variance and Maximum-Utility Frameworks

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

In this paper, we explore the hedging performance of CSI 300 stock index futures under the minimum-variance and maximum-utility framework. We employ ten commonly used econometric models including constant and dynamic ones. Our empirical results indicate that for all futures contracts none of the single model can outperform all other models out-of-sample. In general, the dynamic hedge ratio models are significantly superior to the constant hedge ratio models under the both frameworks in CSI 300 market. Further we obtain the most robust hedging effectiveness by combining OHRs from dynamic hedge models.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alquist, R., Kilian, L., Vigfusson, R. J. (2013). Forecasting the price of oil. In Handbook of economic forecasting (Vol. 2, pp. 427–507). Elsevier.

  • Baillie, R. T., & Myers, R. J. (1991). Bivariate GARCH estimation of the optimal commodity futures hedge. Journal of Applied Econometrics, 6, 109–124.

    Article  Google Scholar 

  • Bollerslev, T. (1986). A generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.

    Article  Google Scholar 

  • Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The Review of Economics and Statistics, 72, 498–505.

    Article  Google Scholar 

  • Chew, S. H., Epstein, L. G., & Segal, U. (1991). Mixture symmetry and quadratic utility. Econometrica, 59, 139–163.

    Article  Google Scholar 

  • Claeskens, G., Magnus, J. R., Vasnev, A. L., & Wang, W. (2016). The forecast combination puzzle: a simple theoretical explanation. International Journal of Forecasting, 32, 754–762.

    Article  Google Scholar 

  • Collins, R. A. (2000). The risk management effectiveness of multivariate hedging models in the soy complex. Journal of Futures Markets, 20, 189–204.

    Article  Google Scholar 

  • Conlon, T., & Cotter, J. (2013). Downside risk and the energy hedger’s horizon. Energy Economics, 36, 371–379.

    Article  Google Scholar 

  • Cotter, J., & Hanly, J. (2006). Re-examining hedging performance. Journal of Futures Markets, 26, 657–676.

    Article  Google Scholar 

  • Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34, 157–170.

    Article  Google Scholar 

  • Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of variance of U.K. inflation. Econometrica, 50, 987–1008.

    Article  Google Scholar 

  • Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20, 339–350.

    Article  Google Scholar 

  • Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11, 122–150.

    Article  Google Scholar 

  • Epstein, L. G., & Segal, U. (1992). Quadratic social welfare functions. Journal of Political Economy, 100, 691–712.

    Article  Google Scholar 

  • Ghosh, A. (1993). Hedging with stock index futures: Estimation and forecasting with error correction model. Journal of Futures Markets, 13, 743–752.

    Article  Google Scholar 

  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48, 1779–1801.

    Article  Google Scholar 

  • Gong, Y., Chen, Q., & Liang, J. (2018). A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets. Economic Modelling, 68, 586–598.

    Article  Google Scholar 

  • Goyal, A., & Welch, I. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21, 1455–1508.

    Article  Google Scholar 

  • Haigh, M. S., & Holt, M. T. (2002). Crack spread hedging: accounting for time-varying volatility spillovers in the energy futures markets. Journal of Applied Econometrics, 17, 269–289.

    Article  Google Scholar 

  • Hou, Y., & Li, S. (2013). Hedging performance of Chinese stock index futures: An empirical analysis using wavelet analysis and flexible bivariate GARCH approaches. Pacific-Basin Finance Journal, 24, 109–131.

    Article  Google Scholar 

  • Hou, Y., & Li, S. (2014). The impact of the CSI 300 stock index futures: Positive feedback trading and autocorrelation of stock returns. International Review of Economics & Finance, 33, 319–337.

    Article  Google Scholar 

  • Hsu, C. C., Tseng, C. P., & Wang, Y. H. (2008). Dynamic hedging with futures: A copula-based GARCH model. Journal of Futures Markets, 28, 1095–1116.

    Article  Google Scholar 

  • Kroner, K. F., & Sultan, J. (1993). Time varying distribution and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28, 535–551.

    Article  Google Scholar 

  • Lee, H. T. (2009). A copula-based regime-switching GARCH model for optimal futures hedging. Journal of Futures Markets, 29, 946–972.

    Article  Google Scholar 

  • Myers, R. J., & Thompson, S. R. (1989). Generalized optimal hedge ratio estimation. American Journal of Agricultural Economics, 71, 858–868.

    Article  Google Scholar 

  • Polak, E. (1997). Optimization: Algorithms and Consistent approximations. New York: Springer.

    Book  Google Scholar 

  • Qu, H., Wang, T., Zhang, Y., & Sun, P. (2018). Dynamic hedging performance of the CSI 300 index futures: The realized minimum-variance hedge ratio approach. Rochester: Social Science Electronic Publishing.

    Google Scholar 

  • Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. The Review of Financial Studies, 23, 821–862.

    Article  Google Scholar 

  • Sen, A. K. (1982). Choice, welfare, and measurement. Cambridge, MA: MIT Press.

    Google Scholar 

  • Sen, A. K. (1986). Social choice theory. In K. Arrow & M. Intriligator (Eds.), Handbook of mathematical economics (Vol. 3). Amsterdam: North-Holland.

    Google Scholar 

  • Suo, Y., Wang, D., & Li, S. (2015). Risk estimation of CSI 300 index spot and futures in China from a new perspective. Economic Modelling, 49, 344–353.

    Article  Google Scholar 

  • Tangian, A.S. (1992). On constructing quadratic objective functions. FernUniversität Hagen, Discussion Paper 192

  • Wang, Y., Wu, C., & Yang, L. (2015). Hedging with futures: Does anything beat the naïve hedging strategy? Manag Sci, 61, 2870–2889.

    Article  Google Scholar 

  • Wei, Y., Wang, Y., & Huang, D. (2011). A copula-multifractal volatility hedging model for CSI 300 index futures. Physica A, 390, 4260–4272.

    Article  Google Scholar 

  • Weymark, J. (1991). A reconsideration of the Harsanyi-Sen debate on utilitarianism. In J. Elster & J. Roemer (Eds.), Interpersonal comparisons of well-being. Cambridge: Cambridge University Press.

    Google Scholar 

  • Yan, Z., & Li, S. (2018). Hedge ratio on Markov regime-switching diagonal Bekk-Garch model. Finance Research Letters, 24, 49–55.

    Article  Google Scholar 

  • Yun, W. C., & Kim, H. J. (2010). Hedging strategy for crude oil trading and the factors influencing hedging effectiveness. Energy Policy, 38, 2404–2408.

    Article  Google Scholar 

Download references

Funding

Funding was provided by National Natural Science Foundation of China (Grant No. 71722015). Yudong Wang particularly acknowledges the financial support from the Fok Ying-Tong Education Foundation of China.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yudong Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geng, Q., Wang, Y. Futures Hedging in CSI 300 Markets: A Comparison Between Minimum-Variance and Maximum-Utility Frameworks. Comput Econ 57, 719–742 (2021). https://doi.org/10.1007/s10614-020-09979-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-020-09979-z

Keywords

Navigation