Abstract

One of the more efficient methods to hedge portfolios of securities whose put options are not traded is to use stock index options. We use the mean-extended Gini (MEG) model to derive the optimal hedge ratios for stock index options. We calculate the MEG ratios for some main stocks traded on the Tel Aviv Stock Exchange and compare them to the minimum-variance hedge ratios. Computed for specific values of risk aversion, MEG hedge ratios combine systematic risk with basis risk. Our results show that increasing the risk aversion used in the computation reduces the size of the hedge ratio, implying that less put options are needed to hedge away each and every security.

Highlights

  • In this paper we use the mean-extended Gini (MEG) model to derive optimal hedge ratios for portfolios with stock index options

  • In the third section, we present a primer on mean-Gini theory whose purpose is to show why the MEG model has been used in futures hedging

  • The systematic risk βi used for the hedge ratio is slightly different from the usual definition of beta because it is obtained by regressing the stock prices on the index underlying the put option, which is done as follows: Si 0 i I i

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Summary

Introduction

In this paper we use the mean-extended Gini (MEG) model to derive optimal hedge ratios for portfolios with stock index options. Since their introduction in the earlier 1980’s, stock index futures and options have allowed investors to manage equity portfolios by hedging against systematic risk. The standard approach for reducing risk in futures hedging is to use minimum variance to maximize expected utility so as to determine the optimal hedge ratios. We use the mean-Gini methodology to derive the MEG hedge ratios with stock index put options. We apply this methodology to securities traded on the Tel-Aviv Stock Exchange and estimate the hedging ratios

The Portfolio Insurance Model with Index Put Options
P i i i 1
A Primer on Mean-Gini
The Mean-Extended Gini Hedging Methodology
Data and Estimation Results
Conclusion
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