Adaptive hedging horizon and hedging performance estimation

TL;DR

This paper introduces an adaptive hedging approach using empirical mode decomposition to determine optimal hedging horizons and employs a cross-validation method for robust performance evaluation, highlighting the importance of contract matching at specific time scales.

Contribution

It proposes an adaptive hedging method based on EMD and a new cross-validation technique for more accurate hedging performance estimation.

Findings

EMD-based hedging outperforms minimum variance hedging on VaR criterion.

In-sample and out-sample hedging performances are inconsistent.

Matching spot and futures at specific time scales improves hedging effectiveness.

Abstract

In this study, we constitute an adaptive hedging method based on empirical mode decomposition (EMD) method to extract the adaptive hedging horizon and build a time series cross-validation method for robust hedging performance estimation. Basing on the variance reduction criterion and the value-at-risk (VaR) criterion, we find that the estimation of in-sample hedging performance is inconsistent with that of the out-sample hedging performance. The EMD hedging method family exhibits superior performance on the VaR criterion compared with the minimum variance hedging method. The matching degree of the spot and futures contracts at the specific time scale is the key determinant of the hedging performance in the corresponding hedging horizon.