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Optimal futures hedging strategies based on an improved kernel density estimation method.

Xing Yu1, Xinxin Wang1, Weiguo Zhang2

  • 1School of Economics and Business Administration, Central China Normal University, Wuhan, 430079 China.

Soft Computing
|September 6, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances crude oil futures hedging by using a novel bivariate kernel density estimation for lower partial moments (LPMs). The improved method offers superior hedging efficiency compared to traditional approaches.

Keywords:
ARCH modelCrude oil priceFutures hedgingGenetic algorithmImproved kernel density estimationLower partial moment

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Area of Science:

  • Finance
  • Econometrics
  • Risk Management

Background:

  • Hedging crude oil price volatility is crucial for market participants.
  • Existing methods often rely on univariate approaches and global distribution assumptions.

Purpose of the Study:

  • To develop and evaluate an improved method for estimating optimal hedge ratios for crude oil futures.
  • To enhance the hedging effectiveness using lower partial moments (LPMs) and bivariate kernel density estimation.

Main Methods:

  • Utilizing a bivariate kernel density estimation with non-identical bandwidths to calculate LPMs.
  • Employing ARCH models to generate independent noise for bivariate analysis.
  • Applying a genetic algorithm to optimize parameters for quasi-likelihood maximization.

Main Results:

  • The proposed bivariate kernel density method significantly improves hedging efficiency.
  • The new strategy outperforms traditional parametric hedging methods.
  • Static hedging demonstrates superior risk control compared to time-varying hedging.

Conclusions:

  • The bivariate kernel density estimation offers a more effective approach to crude oil futures hedging.
  • The study highlights the advantages of localized distribution assumptions and advanced estimation techniques.
  • Static hedging strategies are recommended for better risk management in this context.