A Copula‐Based Quantile Risk Measure Approach to Estimate the Optimal Hedge Ratio

Barbi, Massimiliano; Romagnoli, Silvia

doi:10.1002/fut.21617

Cited by 22 publications

(8 citation statements)

References 41 publications

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“…Owing to the asymmetry of crypto returns as well as the occurrence of extreme events, we consider different dependence structures via a variety of copula models and we optimise the hedge ratio using different risk measures. A similar study was conducted by (Barbi and Romagnoli, 2014) for equity and FX portfolios. Barbi and Romagnoli (2014)'s work is based on Cherubini et al (2011) to derive the distribution of linear combination of margins with copula as their dependence structure.…”

Section: Introductionmentioning

confidence: 92%

“…A similar study was conducted by (Barbi and Romagnoli, 2014) for equity and FX portfolios. Barbi and Romagnoli (2014)'s work is based on Cherubini et al (2011) to derive the distribution of linear combination of margins with copula as their dependence structure. We slightly amend their lemma and come up with a formula for the linear combination of random variables for our purpose.…”

Section: Introductionmentioning

confidence: 92%

“…and finish the proof. Leveraging these two features of copulae, Barbi and Romagnoli (2014) introduce the distribution of linear combinations of random variables using copulae. We slightly edit the Corollary 2.1 of their work and yield the following expression of the distribution.…”

Section: Distribution Of Hedge Portfoliomentioning

confidence: 99%

See 2 more Smart Citations

Hedging Cryptos with Bitcoin Futures

Liu

Packham

et al. 2021

SSRN Journal

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 92%

See 1 more Smart Citation

Hedging Cryptos with Bitcoin Futures

Liu

Packham

et al. 2021

SSRN Journal

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“…They find that performance of Copula-GARCH models has effectiveness as a cross hedging mechanism. Barbi and Romagnoli (2014) present copula based approach for estimating minimum downside-risk hedge ratios employing data for the main UK and US indices and EUR/USD and EUR/GBP exchange rates from January 1, 1990, to January 31, 2011. They indicate that their copula based approach outperforms nonparametric approach in downside risk reductions.…”

Section: Relevant Literaturementioning

confidence: 99%

Dynamic Cross Hedging Effectiveness between Gold and Stock Market Based on Downside Risk Measures: Evidence from Iran Emerging Capital Market

Tehrani

Veisizadeh

2021

J. Mon. Ec.

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This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and copula-GARCH with different copula functions to estimate volatilities and conditional correlations between Iran gold futures contract return and Tehran stock exchange main index return. The empirical results reveal that the dynamic conditional correlations switch between positive and near-zero values over the period under study. These correlations are high and positive during the major national currency devaluation and are low near to zero during other times. Out-of-sample one-step-ahead forecasts based on rolling window analysis show that DCC and ADCC multivariate GARCH models outperform other models for variance reduction, while a more interesting finding is that the copula-GARCH model outperforms other models for downside risks reduction.

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“…Cheng et al (2015) model exceedances over a price threshold using a GPD to investigate the efficacy of margin, capital requirement and price limits for managing default risk in futures markets under VaR, ES and spectral risk measures. On the other hand, a number of studies examine hedging effectiveness based on minimum VaR or ES (Harris and Shen, 2006;Cao et al, 2010;Barbi and Romagnoli, 2014). Pownall and Koedijk (1999) and Bali (2003) present evidence that extreme-value VaR outperforms models which rely on the assumption of normal distribution of returns [2].…”

Section: Introductionmentioning

confidence: 99%

Back-testing extreme value and Lévy value-at-risk models

Mozumder

Dempsey

Kabir

2017

JRF

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Purpose The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Lévy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices. Design/methodology/approach The authors apply tail-based and Lévy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Lévy models. As the Lévy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs. Findings Both EV and Lévy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance. Originality/value To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Lévy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.

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A Copula‐Based Quantile Risk Measure Approach to Estimate the Optimal Hedge Ratio

Cited by 22 publications

References 41 publications

Hedging Cryptos with Bitcoin Futures

Hedging Cryptos with Bitcoin Futures

Dynamic Cross Hedging Effectiveness between Gold and Stock Market Based on Downside Risk Measures: Evidence from Iran Emerging Capital Market

Back-testing extreme value and Lévy value-at-risk models

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