Portfolio risk decomposition through pair-copula models

Abbara, Omar; Zevallos, Mauricio

doi:10.1080/23737484.2017.1399483

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…This subsection closely follows Abbara and Zevallos (2017), and useful reviews are given by Nelsen (1999), Cherubini et al (2004), and Trivedi and Zimmer (2007). This subsection closely follows Abbara and Zevallos (2017), and useful reviews are given by Nelsen (1999), Cherubini et al (2004), and Trivedi and Zimmer (2007).…”

Section: Copula Modelingmentioning

confidence: 91%

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Modeling and forecasting intraday VaR of an exchange rate portfolio

Abbara

Zevallos

2018

Journal of Forecasting

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The main task of this work was to predict, for the next 15 minutes, the value‐at‐risk (VaR) of an equally weighted portfolio composed of four exchange rates against the American dollar: Japanese yen, euro, Australian dollar and Swiss franc. The dataset consists of transaction prices of each asset recorded every 15 minutes, from January 7, 2013 to December 31, 2013. For each time series, the multiplicative‐component generalized autoregressive conditional heteroskedasticity model of Engle and Sokalska (Journal of Financial Econometrics, 2012, 10, 54–83) is fitted, and the dependence among the series is modeled by a D‐vine pair‐copula. VaR predictions are estimated based on simulated observations of the fitted model following the proposal of Berg and Aas (European Journal of Finance, 2009, 15, 639–659). The proposed method presents good results in terms of out‐of‐sample intraday VaR forecasting.

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Section: Copula Modelingmentioning

confidence: 91%

“…In this subsection we describe the principles of copula modeling. This subsection closely follows Abbara and Zevallos (2017), and useful reviews are given by Nelsen (1999), Cherubini et al (2004), and Trivedi and Zimmer (2007).…”

Section: Copula Modelingmentioning

confidence: 99%

Modeling and forecasting intraday VaR of an exchange rate portfolio

Abbara

Zevallos

2018

Journal of Forecasting

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“…There are different copulas approach applications for the optimization of returns of assets seen on Righi and Ceretta ( 2013 ), Kakouris and Rustem ( 2014 ), Bartels and Ziegelmann ( 2016 ) and Abbara and Zevallos ( 2017 ). One of these methods is the multivariate pair-copula models of Joe ( 1997 ), extended by Bedford and Cooke ( 2001 ) and Bedford and Cooke ( 2002 ) with a hierarchic graphic construction of bivariate copulas called regular vines copulas.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric dependence of intraday frequency components in the Brazilian stock market

et al. 2021

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The multivariate dependence plays an important role in financial instrument management. Due to the inherent characteristics in the financial market, such as heavy tails in the returns unconditional distribution and asymmetry between gain and loss, we obtained the asymmetric dependence structure in different short-term variation scales based on the wavelet technique MODWT. The study sought to capture the relations between financial returns represented by its frequency components. Intraday returns series was used in the 15-min sampling interval from stocks and applied the D-Vine pair-copula to decompose in trade frequencies of 15 min, 1 h, 1 day, and 1 week with margin adjustments of ARIMA-APARCH class and BB7 copula function, responsible for measuring the dependence on tails. The results indicated the prevalence of a high dependence during market upturns, rising over the analyzed frequencies. Being an important tool in financial management and allowing short-term strategies of diversification.

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Modeling dependence structure between green sukuk spread in Malaysia and the uncertainty factors before and during the COVID-19 pandemic

Hariz,

Mezghani,

Abbes

2023

JIABR

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Purpose This paper aims to analyze the dependence structure between the Green Sukuk Spread in Malaysia and uncertainty factors from January 1, 2017, to May 23, 2023, covering two main periods: the pre-COVID-19 and the COVID-19 periods. Design/methodology/approach This study contributes to the current literature by explicitly modeling nonlinear dependencies using the Regular vine copula approach to capture asymmetric characteristics of the tail dependence distribution. This study used the Archimedean copula models: Student’s-t, Gumbel, Gaussian, Clayton, Frank and Joe, which exhibit different tail dependence structures. Findings The empirical results suggest that Green Sukuk and various uncertainty variables have the strongest co-dependency before and during the COVID-19 crisis. Due to external uncertainties (COVID-19), the results reveal that global factors, such as the Infect-EMV-index and the higher financial stress index, significantly affect the spread of Green Sukuk. Interestingly, in times of COVID-19, its dependence on Green Sukuk and the news sentiment seems to be a symmetric tail dependence with a Student’s-t copula. This result is relevant for hedging strategies, as investors can enhance the performance of their portfolio during the COVID-19 crash period. Originality/value This study contributes to a better understanding of the dependency structure between Green Sukuk and uncertainty factors. It is relevant for market participants seeking to improve their risk management for Green Sukuk.

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Portfolio risk decomposition through pair-copula models

Cited by 3 publications

References 25 publications

Modeling and forecasting intraday VaR of an exchange rate portfolio

Modeling and forecasting intraday VaR of an exchange rate portfolio

Asymmetric dependence of intraday frequency components in the Brazilian stock market

Modeling dependence structure between green sukuk spread in Malaysia and the uncertainty factors before and during the COVID-19 pandemic

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