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Article
Publication date: 29 November 2022

Menggen Chen and Yuanren Zhou

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

Abstract

Purpose

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

Design/methodology/approach

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

Findings

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

Originality/value

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

Details

International Journal of Emerging Markets, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-8809

Keywords

Book part
Publication date: 31 December 2010

Rania Hentati and Jean-Luc Prigent

Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.Methodology/approach – Goodness-of-fit tests, based on the…

Abstract

Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.

Methodology/approach – Goodness-of-fit tests, based on the Kendall's functions, are applied as selection criteria of the “best” copula. After estimating the parametric copula that best fits the used data, we apply previous results to construct the cumulative distribution functions of the equally weighted portfolios.

Findings – The empirical validation shows that copula clearly allows better estimation of portfolio returns including hedge funds. The three studied portfolios reject the assumption of multivariate normality of returns. The chosen structure is often of Student type when only indices are considered. In the case of portfolios composed by only hedge funds, the dependence structure is of Franck type.

Originality/value of the chapter – Introducing goodness-of-fit bootstrap method to validate the choice of the best structure of dependence is relevant for hedge fund portfolios. Copulas would be introduced to provide better estimations of performance measures.

Details

Nonlinear Modeling of Economic and Financial Time-Series
Type: Book
ISBN: 978-0-85724-489-5

Keywords

Book part
Publication date: 1 December 2008

Wenbo Hu and Alec N. Kercheval

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their pricing an estimation of the dependence structure of defaults, which is…

Abstract

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their pricing an estimation of the dependence structure of defaults, which is known to exhibit tail dependence as reflected in observed default contagion. A popular model with this property is the (Student's) t-copula; unfortunately there is no fast method to calibrate the degree of freedom parameter.

In this paper, within the framework of Schönbucher's copula-based trigger-variable model for basket CDS pricing, we propose instead to calibrate the full multivariate t distribution. We describe a version of the expectation-maximization algorithm that provides very fast calibration speeds compared to the current copula-based alternatives.

The algorithm generalizes easily to the more flexible skewed t distributions. To our knowledge, we are the first to use the skewed t distribution in this context.

Details

Econometrics and Risk Management
Type: Book
ISBN: 978-1-84855-196-1

Abstract

Details

Structural Models of Wage and Employment Dynamics
Type: Book
ISBN: 978-0-44452-089-0

Book part
Publication date: 18 January 2022

Dante Amengual, Enrique Sentana and Zhanyuan Tian

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to…

Abstract

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

Details

Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology
Type: Book
ISBN: 978-1-80262-065-8

Keywords

Book part
Publication date: 16 December 2009

Chinman Chui and Ximing Wu

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely…

Abstract

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely summarizes the dependence structure among multiple variables. We propose a multivariate exponential series estimator (ESE) to estimate copula densities nonparametrically. The ESE has an appealing information-theoretic interpretation and attains the optimal rate of convergence for nonparametric density estimations in Stone (1982). More importantly, it overcomes the boundary bias of conventional nonparametric copula estimators. Our extensive Monte Carlo studies show the proposed estimator outperforms the kernel and the log-spline estimators in copula estimation. It also demonstrates that two-step density estimation through an ESE copula often outperforms direct estimation of joint densities. Finally, the ESE copula provides superior estimates of tail dependence compared to the empirical tail index coefficient. An empirical examination of the Asian financial markets using the proposed method is provided.

Details

Nonparametric Econometric Methods
Type: Book
ISBN: 978-1-84950-624-3

Article
Publication date: 19 May 2022

Fahim Afzal, Tonmoy Toufic Choudhury and Muhammad Kamran

Because of the growing financial market integration, China’s stock market’s volatility spillover effect has gradually increased. Traditional strategies do not capture…

Abstract

Purpose

Because of the growing financial market integration, China’s stock market’s volatility spillover effect has gradually increased. Traditional strategies do not capture stock volatility in dependence and dynamic conditions. Therefore, this study aims to find an effective stochastic model to predict the volatility spillover effect in the dynamic stock markets.

Design/methodology/approach

To assess the time-varying dynamics and volatility spillover, this study has used an integrated approach of dynamic conditional correlation model, copula and extreme-value theory. A daily log-returns of three leading indices of Pakistan Stock Exchange (PSX) and Shanghai Stock Exchange (SSE) from the period of 2009 to 2019 is used in the modeling of value-at-risk (VaR) for volatility estimation. The Student’s t copula has been selected based on maximum likelihood estimation and Akaike’s information criteria values of all the copulas using the goodness-of-fit test.

Findings

The model results show stronger dependency between all major portfolios of PSX and SSE, with the parametric value of 0.98. Subsequently, the results of dependence structure positively estimate the spillover effect of SSE over PSX. Furthermore, the back-testing results show that the VaR model performs well at 99% and 95% levels of confidence and gives more accurate estimates upon the maximum level of confidence.

Practical implications

This study is helpful for the investment managers to manage the risk associated to portfolios under dependence conditions. Moreover, this study is also helpful for the researchers in the field of financial risk management who are trying to improve the returns by addressing the issues of volatility estimations.

Originality/value

This study contributes to the body of knowledge by providing a practical model to manage the volatility spillover effect in dependence conditions between as well as across the financial markets.

Details

International Journal of Islamic and Middle Eastern Finance and Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8394

Keywords

Article
Publication date: 26 May 2021

Wuyi Ye and Ruyu Zhao

The stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market…

Abstract

Purpose

The stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).

Design/methodology/approach

The authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.

Findings

The empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.

Practical implications

The risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.

Originality/value

The authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

Details

The Journal of Risk Finance, vol. 22 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 4 November 2013

Ryan Larsen, James W. Mjelde, Danny Klinefelter and Jared Wolfley

What copulas are, their estimation, and use is illustrated using a geographical diversification example. To accomplish this, dependencies between county-level yields are…

Abstract

Purpose

What copulas are, their estimation, and use is illustrated using a geographical diversification example. To accomplish this, dependencies between county-level yields are calculated for non-irrigated wheat, upland cotton, and sorghum using Pearson linear correlation and Kendall's tau. The use of Kendall's tau allows the implementation of copulas to estimate the dependency between county-level yields. The paper aims to discuss these issues.

Design/methodology/approach

Four parametric copulas, Gaussian, Frank, Clayton, and Gumbel, are used to estimate Kendall's tau. These four estimates of Kendall's tau are compared to Pearson's linear correlation, a more typical measure of dependence. Using this information, functions are estimated to determine the relationships between dependencies and changes in geographic and climate data.

Findings

The effect on county-level crop yields based on changes of geographical and climate variables differed among the different dependency measures among the three different crops. Implementing alternative dependency measures changed the statistical significance and the signs of the coefficients in the sorghum and cotton dependence functions. Copula-based elasticities are consistently less than the linear correlation elasticities for wheat and cotton. For sorghum, however, the copula-based elasticities are generally larger. The results indicate that one should not take the issue of measuring dependence as a trivial matter.

Originality/value

This research not only extends the current literature on geographical diversification by taking a more detailed examination of factors impacting yield dependence, but also extends the copula literature by comparing estimation results using linear correlation and copula-based rank correlation.

Details

Agricultural Finance Review, vol. 73 no. 3
Type: Research Article
ISSN: 0002-1466

Keywords

Article
Publication date: 15 August 2016

Mingyuan Guo and Xu Wang

– The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.

Abstract

Purpose

The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.

Design/methodology/approach

Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China.

Findings

This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view.

Originality/value

Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

Details

China Finance Review International, vol. 6 no. 3
Type: Research Article
ISSN: 2044-1398

Keywords

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