Canonical vine copulas in the context of modern portfolio management: Are they worth it?

Low, Rand Kwong Yew; Alcock, Jamie; Faff, Robert W.; Brailsford, Tim

doi:10.1016/j.jbankfin.2013.02.036

Cited by 135 publications

(64 citation statements)

References 34 publications

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“…We make use of the multivariate Gaussian and the multivariate t-distribution as baseline models for financial market data. These reference cases are compared against vine copulas, a novelty in high-dimensional dependence modeling and state-of-the-art in the copula literature due to their superior flexibility (Low et al (2013); Weiß and Supper (2013)). Third, we perform a large-scale empirical study on the S&P 500 from January 1990 until October 2015.…”

Section: Introductionmentioning

confidence: 99%

Statistical arbitrage with vine copulas

Stübinger

Mangold

Krauß

2018

Quantitative Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe develop a multivariate statistical arbitrage strategy based on vine copulas -a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25 percent p.a. and a Sharpe ratio of 1.12 after transaction costs for the period from 1992 until 2015. Tail risk is limited, with maximum drawdown at 6.57 percent. The high returns can only partially be explained by common sources of systematic risk. We benchmark the vine copula strategy against other variants relying on the multivariate Gaussian and t-distribution and we find its results to be superior in terms of risk and return characteristics. The multivariate dependence structure of the vine copulas is time-varying, and we see that the share of copulas capable of modeling upper and lower tail dependence increases well over 90 percent at times of high market turmoil.

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Section: Introductionmentioning

confidence: 99%

Statistical arbitrage with vine copulas

Stübinger

Mangold

Krauß

2018

Quantitative Finance

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“…More recently, Al Janabi (2013Janabi ( , 2014 tackles the issue of adverse market price impacts on liquidity risk and coherent portfolio optimization using a parametric liquidity-adjusted VaR methodology. 4 On the subject of dependence estimation using copulas, our paper is related to the recent studies by Low et al (2013) and Weiß and Supper (2013). The former forecasts portfolio returns with both symmetric and asymmetric copula models, subject to no short-sales constraints and the minimization of CVaR.…”

Section: Introductionmentioning

confidence: 99%

Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios

Janabi

Hernández

Berger

et al. 2017

European Journal of Operational Research

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We propose a model for optimizing structured portfolios with liquidity-adjusted Value-at-Risk (LVaR) constraints, whereby linear correlations between assets are replaced by the multivariate nonlinear dependence structure based on Dynamic Conditional Correlation t-copula modeling. Our portfolio optimization algorithm minimizes the LVaR function under adverse market circumstances and multiple operational and financial constraints. When we consider a diversified portfolio of international stock and commodity market indices under multiple realistic portfolio optimization scenarios, the obtained results consistently show the superiority of our approach relative to other competing portfolio strategies including the minimum-variance, risk-parity and equally weighted portfolio allocations.

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“…See, e.g., Dißmann et al (2013) and references therein, Min and Czado (2014), Beare and Seo (2015), Brechmann and Joe (2015), Schepsmeier (2015), Weiß and Scheffer (2015), etc. Vine copula has been widely applied in finance, see, e.g., Low et al (2013), Weiß and Supper (2013), Abbara (2014), Arreola Hernandez (2014), Brechmann et al (2014), Markwat (2014), Allen et al (2014), Zhang (2014), Siburg et al (2015), etc. For example, it can be applied in portfolio optimization in mainly two directions.…”

Section: Introductionmentioning

confidence: 99%