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Currency hedging strategies using dynamic multivariate GARCH

Authors: Chia-Lin Chang; Lydia González-Serrano; Juan-Angel Jimenez-Martin;

Currency hedging strategies using dynamic multivariate GARCH

Abstract

This paper examines the effectiveness of using futures contracts as hedging instruments of: (1) alternative models of volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of futures contracts. For this purpose, daily data of futures and spot exchange rates of three major international currencies, Euro, British pound and Japanese yen, against the American dollar, are used to analyze hedge ratios and hedging effectiveness resulting from using two different maturity currency contracts, near-month and next-to-near-month contract. Following Chang et al. [17], we estimate four multivariate volatility models (namely CCC, VARMA-AGARCH, DCC and BEKK), and calculate optimal portfolio weights and optimal hedge ratios to identify appropriate currency hedging strategies. The hedging effectiveness index suggests that the best results in terms of reducing the variance of the portfolio are for the USD/GBP exchange rate. The empirical results show that futures hedging strategies are slightly more effective when the near-month future contract is used for the USD/GBP and USD/JPY currencies. Moreover, the CCC and AGARCH models provide similar hedging effectiveness, which suggests that dynamic asymmetry may not be crucial empirically, although some differences appear when the DCC and BEKK models are used.

Country
Spain
Keywords

Hedging strategies., Optimal portfolio weights, Optimal hedge ratio, exchange rates, 5302 Econometría, Econometría (Economía), G32, G11, multivariate GARCH, hedging, C53, Statistical methods; risk measures, G17, Conditional correlations, Exchange rates, Multivariate GARCH, conditional correlations, exchange rates, optimal hedge ratio, optimal portfolio weights, hedging strategies., Multivariate GARCH, Econometría, C22, jel: jel:C22, jel: jel:C53, jel: jel:G32, jel: jel:G11, jel: jel:G17

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Top 10%
Average
Green
bronze