Currency-Hedging Optimization for Multi-Asset Portfolios

Guo, Helen; Ryan, Laura

doi:10.3905/jpm.2018.44.2.100

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The authors study the hedging effectiveness in terms of value-at-risk and expected shortfall. On a similar note, Guo and Ryan (2018) use the expected shortfall optimization framework to more accurately account for tail risk with non-Gaussian returns. A similar analysis is also performed in Álvarez-Díez et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Dynamic currency hedging with non-Gaussianity and ambiguity

Polak,

Ulrych

2024

Quantitative Finance

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This paper introduces a non-Gaussian dynamic currency hedging strategy for globally diversified investors with ambiguity. It provides theoretical and empirical evidence that, under the stylized fact of non-Gaussianity of financial returns and for a given optimal portfolio, the investor-specific ambiguity can be estimated from historical asset returns without the need of additional exogenous information.Acknowledging non-Gaussianity, we compute an optimal ambiguity-adjusted mean-variance (dynamic) currency allocation. Next, we propose an extended filtered historical simulation that combines Monte Carlo simulation based on volatility clustering patterns with the semi-parametric non-normal return distribution from historical data. This simulation allows us to incorporate investor's ambiguity into a dynamic currency hedging strategy algorithm that can numerically optimize an arbitrary risk measure, such as the expected shortfall. The out-of-sample backtest demonstrates that, for globally diversified investors, the derived non-Gaussian dynamic currency hedging strategy is stable, robust, and highly risk reductive. It outperforms the benchmarks of constant hedging as well as static/dynamic hedging approaches with Gaussianity in terms of lower maximum drawdown and higher Sharpe and Sortino ratios in gross terms and net of transaction costs.

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Section: Introductionmentioning

confidence: 99%