Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas

Weiß, Gregor N. F.; Supper, Hendrik

doi:10.1016/j.jbankfin.2013.05.013

Cited by 83 publications

(59 citation statements)

References 53 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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“…2 The assessment and forecasting of liquidity risk typically depend on many interlinked factors, such as the dependence between asset prices and their time-variations, sector-specific market frictions, financial and market information availability from and across market sectors, stock market confidence, financial trading regulations in stress markets, sudden market shocks resulting in market downturns and contractions in capital inflow and outflow, and cap-ital reserve levels of financial and trading institutions. In spite of several works on liquidity risk (Berkowitz, 2000;Bangia et al, 2002;Angelidis and Benos, 2006;Al Janabi, 2013Weiß and Supper, 2013), accurate estimations of market liquidity risk and its application to the problem of portfolio optimization remain as challenging tasks for financial entities. This paper investigates the above-mentioned issue by developing and implementing robust modeling techniques to assess liquidity risk under illiquid market scenarios, while taking into account multivariate asset dependence.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…Second, our proposed approach, which consists of replacing the variance risk measure by the LVaR algorithms and the linear correlation between assets by their multivariate nonlinear dependence structure based on the DCC t-copula, is a thorough enhancement of the traditional Markowitz (1952) mean-variance portfolio optimization given the relevance of these factors in asset pricing and allocation (Liu, 2006;Heinen and Valdesogo, 2008;Cornett et al, 2011). Third, our study is among few studies (Angelidis and Benos, 2006;Al Janabi, 2013Weiß and Supper, 2013) that examine liquidity risk, based on the use of daily data of stock market indices from developed and emerging markets, along with two major commodities: gold and oil. This research design is advantageous in that country indices better capture the effects of liquidity on asset prices and market drivers, due to aggregation.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Weiß et al used the Vine Copula model to predict the portfolio risk. The results show that the Vine Copula model can accurately measure the risk of portfolio [15]. Fan Guobin et al used the C-Vine Copula model to characterize the nonlinear dependencies between multiple financial assets.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Dynamic Portfolio VaR Based on Mixed Vine Copula Model

Ru-bo¹

2017

JFA

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Abstract:The measurement of portfolio VaR has been a hot issue in the field of the academic and the industry. This paper applies three kinds of Vine Copula model to describe high-dimensional dependency structure between multiple assets, introduces mixed binary copula function to improve the accuracy of tail dependence structure. We use six important stock markets as stock portfolio to test this model. The empirical results show that introducing mixed Copula function can improve the measurement reliability of Vine Copula model, and the reliability of mixed R-Vine model is highest in three kinds of mixed Vine Copula models.

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Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas

Cited by 83 publications

References 53 publications

Statistical arbitrage with vine copulas

Statistical arbitrage with vine copulas

Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios

Measurement of Dynamic Portfolio VaR Based on Mixed Vine Copula Model

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