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Improvement of Hedging Effect Based on the Average Hedging Ratio

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Recent Developments in Data Science and Business Analytics

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Abstract

This paper is aimed at exploring the improvement of hedging effect based on the theory of portfolio hedging, with multiple groups of CSI300 stock index futures and spot sample data as the analysis object. The minimum variance method is employed to estimate the optimal hedging ratio under the OLS and GARCH hedging models and calculate the average of the hedge ratios. By comparing the hedging effects of the constructed portfolio outside of samples based on different hedging ratios, the empirical analysis displays that the hedging effect of the average hedge ratio was superior to the hedging effect of the estimated hedge ratio of most individual historical samples. Therefore, the methodology supposed is deeply improved by considering the average value of the hedging ratio in order to optimize the optimal hedging ratio.

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Acknowledgments

This work was supported by Social Science Research Foundation of Ministry of Education of China (15YJA790051), National Social Science Fund Project of China (17BGL058) and Shandong Province Natural Science Foundation (ZR2016GM20).

Author information

Authors and Affiliations

  1. College of Mathematics and Systems Science of Shandong University of Science and Technology, Qingdao, China

    Yang Liu

  2. Institute of Financial Engineering of Shandong Women’s University, Jinan, China

    Chuan-he Shen

Authors
  1. Yang Liu
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  2. Chuan-he Shen
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Corresponding author

Correspondence to Chuan-he Shen .

Editor information

Editors and Affiliations

  1. Department of Business Systems and Analytics, La Salle University, Philadelphia, Pennsylvania, USA

    Madjid Tavana

  2. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

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Liu, Y., Shen, Ch. (2018). Improvement of Hedging Effect Based on the Average Hedging Ratio. In: Tavana, M., Patnaik, S. (eds) Recent Developments in Data Science and Business Analytics. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-72745-5_30

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