Journal of Applied Mathematics

A New Method for Setting Futures Portfolios’ Maintenance Margins: Evidence from Chinese Commodity Futures Markets

Chi Xie, Jiao-Jiao Yang, and Gang-Jin Wang

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Abstract

The Chinese commodity futures markets neglect the existence of the risk hedge and diversification between futures contracts, thus leading to overcharge futures portfolio holders’ maintenance margins. To this end, this paper proposes a new method, namely, the multivariate t-Copula-POT-PSRM method, which combines three models, that is, the multivariate t-Copula, the peaks over threshold (POT), and the power spectral risk measures (PSRM), to set futures portfolios’ maintenance margins. In the empirical analysis, we first construct four kinds of futures portfolios and set their maintenance margins by using the new method. Then, we introduce two evaluation indicators, namely, the prudence index (PI) and the opportunity cost index (OCI), to assess the effectiveness of the proposed method. We also compare the outcomes of the two evaluation indicators of the new method with those of the widely used linear additive model. The empirical results show that the new method can, respectively, lower the OCI value of all four kinds of futures portfolios for the In-sample period and the Out-of-sample period without significantly reducing the PI value as against the traditional model, which implies that the proposed method can be used to balance security and investment efficiency in the futures market.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 325975, 11 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305619

Digital Object Identifier
doi:10.1155/2014/325975

Citation

Xie, Chi; Yang, Jiao-Jiao; Wang, Gang-Jin. A New Method for Setting Futures Portfolios’ Maintenance Margins: Evidence from Chinese Commodity Futures Markets. J. Appl. Math. 2014 (2014), Article ID 325975, 11 pages. doi:10.1155/2014/325975. https://projecteuclid.org/euclid.jam/1425305619


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References

  • G. Frahm, M. Junker, and R. Schmidt, “Estimating the tail-dependence coefficient: properties and pitfalls,” Insurance, vol. 37, no. 1, pp. 80–100, 2005.
  • E. I. George and S. T. Jensen, “A latent variable perspective of copula modeling,” Marketing Science, vol. 30, no. 1, pp. 22–24, 2011.
  • S. Kusuoka and T. Nakashima, “A remark on credit risk models and copula,” Advances in Mathematical Economics, vol. 16, pp. 53–84, 2012.
  • A. Panagiotelis, C. Czado, and H. Joe, “Pair copula constructions for multivariate discrete data,” Journal of the American Statistical Association, vol. 107, no. 499, pp. 1063–1072, 2012.
  • N. Li and X. J. Cheng, “A new method for setting dynamic futures portfolio margin level,” Journal of University of Science and Technology of China, vol. 42, no. 3, pp. 198–202, 2012 (Chinese).
  • Z.-R. Wang, X.-H. Chen, Y.-B. Jin, and Y.-J. Zhou, “Estimating risk of foreign exchange portfolio: using VaR and CVaR based on GARCHEVT-Copula model,” Physica A, vol. 389, no. 21, pp. 4918–4928, 2010.
  • F. J. Acero, J. A. García, and M. C. Gallego, “Peaks-over-threshold study of trends in extreme rainfall over the Iberian Peninsula,” Journal of Climate, vol. 24, no. 4, pp. 1089–1105, 2011.
  • J. Beirlant, E. Joossens, and J. Segers, “Second-order refined peaks-over-threshold modelling for heavy-tailed distributions,” Journal of Statistical Planning and Inference, vol. 139, no. 8, pp. 2800–2815, 2009.
  • V. Chavez-Demoulin and A. C. Davison, “Modelling time series extremes,” Revstat Statistical Journal, vol. 10, no. 1, pp. 109–133, 2012.
  • E. F. Eastoe and J. A. Tawn, “Modelling the distribution of the cluster maxima of exceedances of subasymptotic thresholds,” Biometrika, vol. 99, no. 1, pp. 43–55, 2012.
  • J. A. Chan-Lau, S. Mitra, and L. L. Ong, “Identifying contagion risk in the international banking system: an extreme value theory approach,” International Journal of Finance and Economics, vol. 17, no. 4, pp. 390–406, 2012.
  • I. Papastathopoulos and J. A. Tawn, “Extended generalised Pareto models for tail estimation,” Journal of Statistical Planning and Inference, vol. 143, no. 1, pp. 131–143, 2013.
  • M. Gilli and E. Këllezi, “An application of extreme value theory for measuring financial risk,” Computational Economics, vol. 27, no. 2-3, pp. 207–228, 2006.
  • Q. F. Liu and Z. Wang, “Setting dynamic margin levels in Chinese futures markets,” Journal of Systems Engineering, vol. 26, no. 6, pp. 777–784, 2011 (Chinese).
  • C. Acerbi, “Spectral measures of risk: a coherent representation of subjective risk aversion,” Journal of Banking and Finance, vol. 26, no. 7, pp. 1505–1518, 2002.
  • J. Cotter and K. Dowd, “Extreme spectral risk measures: an application to futures clearinghouse margin requirements,” Journal of Banking and Finance, vol. 30, no. 12, pp. 3469–3485, 2006.
  • K. Dowd, J. Cotter, and G. Sorwar, “Spectral risk measures: properties and limitations,” Journal of Financial Services Research, vol. 34, no. 1, pp. 61–75, 2008.
  • D. Z. Han, X. F. Wang, F. Xing, M. M. Yang, and Y. J. Lou, “Setting up of dynamic margin of futures based on spectral risk measurement of extreme value,” Journal of Management Sciences, vol. 22, no. 1, pp. 86–94, 2009 (Chinese).
  • T. Bollerslev, “A conditionally heteroskedastic time series model for security prices and rates of return data,” The Review of Economics and Statistics, vol. 69, no. 3, pp. 542–547, 1987.
  • A. K. Nikoloulopoulos, H. Joe, and H. Li, “Extreme value properties of multivariate t copulas,” Extremes, vol. 12, no. 2, pp. 129–148, 2009.
  • R. L. Smith, “Threshold methods for sample extremes,” Statistical Extremes and Applications, vol. 131, pp. 621–638, 1985.
  • A. A. Balkema and L. de Haan, “Residual life time at great age,” The Annals of Probability, vol. 2, no. 5, pp. 792–804, 1974.
  • J. Pickands III, “Statistical inference using extreme order statistics,” Annals of Statistics, vol. 3, no. 1, pp. 119–131, 1975.
  • J. Beirlant, P. Vynckier, and J. L. Teugels, “Excess functions and the estimation of the extreme-value index,” Bernoulli, vol. 2, no. 4, pp. 293–318, 1996.
  • P. H. Kupiec, “Techniques for verifying the accuracy of risk measurement models,” The Journal of Derivatives, vol. 3, no. 2, pp. 73–84, 1995.
  • C. Xie, J. J. Yang, and Y. J. Zhao, “Setting of futures maintenance margin levels based on SV-M-POT-PSRM model: an empirical analysis on high-frequency data of CSI300 stock index futures,” Journal of Systems and Management, vol. 22, no. 6, pp. 768–776, 2013 (Chinese). \endinput