Asymmetry in tail dependence in equity portfolios

Jondeau, Éric

doi:10.1016/j.csda.2015.02.014

Cited by 31 publications

(6 citation statements)

References 34 publications

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“…The Frank-Copula function has no tail correlation and cannot capture the tail correlation between variables according to Xue (2018). Gumbel Copula performs better in the upper correlation while Clayton Copula performs better in the lower correlation (Jondeau, 2016). Therefore, the Gumbel Copula function would be chosen to construct the joint distribution of monthly rainfall-runoff pairs.…”

Section: Comparison Of Joint Distributionsmentioning

confidence: 99%

A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis

Wen

Yang

Kong

et al. 2022

Front. Environ. Sci.

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A Bayesian-model-averaging Copula (i.e., BMAC) approach was proposed for correlation analysis of monthly rainfall and runoff in Xiangxi River watershed, China. The BMAC approach was formulated by incorporating existing Bayesian model averaging (i.e., BMA) method and Archimedean Copula techniques (e.g., Gumbel-Hougaard, Clayton and Frank Copulas) within a general bivariate hydrologic correlation analysis framework. In this paper, the BMA method was applied to determine the marginal distribution functions of variables, and the Copula method was used to analyze the correlation. Results showed that: 1) the BMA method could improve the representation of the marginal distribution of hydrological variables with smaller corresponding errors; 2) the predictive joint distributions of monthly rainfall and runoff was much better calibrated by the Gumbel Copula according to criteria of the root mean square error (i.e., RMSE), Akaike Information Criterion (i.e., AIC) values, Anderson-Darling test (i.e., AD test), and Cramer-von Mises test (i.e., CM test); and 3) the bivariate joint probability and return periods of rainfall and runoff based on the optimal Copula function was characterized and the monthly rainfall and runoff presented a strong positive correlation based on Kendall and Spearman’s rank correlation coefficients. Therefore, the BMAC approach performed reasonably well and can be further used to simulate runoff values according to the historical and predicted rainfall data. Highlights: 1) A Bayesian-model-averaging Copula method is proposed for correlation analysis; 2) the monthly rainfall and runoff in Xiangxi River watershed has a positive correlation. 3) Gumbel Copula is the best in modelling the joint distributions in the Xiangxi River watershed.

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Section: Comparison Of Joint Distributionsmentioning

confidence: 99%

A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis

Wen

Yang

Kong

et al. 2022

Front. Environ. Sci.

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“…We conclude that a portfolio constructed based on the distribution model that allowed for asymmetric dependence can lead to significantly better asset allocation decisions in time horizons analyzed. Based on the traditional mean-variance analysis by Markowitz ( 1952 ), studies such as Patton ( 2004 ), Hatherley and Alcock ( 2007 ), Jondeau ( 2016 ) and Wang and Xie ( 2016 ), and others have indicated that these benefits are results of more flexibility specifying the dependence structure on the portfolio. The measurement of asymmetric dependence allows diversifying the allocation of resources in portfolios, providing a balance between risks and returns.…”

Section: Final Remarksmentioning

confidence: 99%

“…Since Modern Portfolio Theory (Markowitz 1952 ) a general discussion of this topic, including other aspects as risk and return, expected return, measures of risk and volatility, and diversification, has been in the current literature. Several studies such as Ergen ( 2014 ), Jondeau ( 2016 ) and Caldeira et al. ( 2017 ) have shown that the measuring of dependence existing among the returns of a portfolio is essential to investment strategy development, mainly in the diversification context, which consists of the efficient allocation of distinct assets to minimize risks.…”

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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“…They revealed that the interdependency is different across precious metals, showing different average and tail dependence features. Jondeau [25] explored the tail dependence between the US equity portfolios and discovered asymmetry in the lower and upper tail dependence.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach

Choi

Yoon

2020

Sustainability

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Changes in crude oil price affect the shipping freight market via three different channels. This study explores the dependence structure between oil prices and maritime freight rates to identify the effective channel. Therefore, it investigates the relationship between oil prices and three major maritime freight rates; the Baltic Dry Index (BDI), the Baltic Dirty Tanker Index (BDTI), and the Baltic Clean Tanker Index (BCTI). We employ the decomposition method, not studied in the existing literature, and the copula approach which can identify the time-varying effects and asymmetry in the tail dependence structure between oil prices and freight rates. The main results of this analysis are as follows: the decomposed components display different conditional dependence patterns, and asymmetry is revealed in the upper and lower tail dependence. In the long-run, we find more dependence in extreme periods like the financial crises. In short-run fluctuations, we find the dependence increases in an economic boom. The implications of the results suggest that dependence can vary over time and may change depending on extreme events, implying that the complementary strategies should be different the long-run and short-run.

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Asymmetry in tail dependence in equity portfolios

Cited by 31 publications

References 34 publications

A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis

A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis

Asymmetric dependence of intraday frequency components in the Brazilian stock market

Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach

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