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Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas

Quanrui Song, +2 more
- Vol. 7, Iss: 3, pp 274
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TLDR
It is shown that BRICS has the highest risk and G20 has the lowest risk of the three groups and real financial data demonstrated that Factor copulas have stronger stability and perform better than the other two copulas in high-dimensional data.
Abstract
Multivariate copulas have been widely used to handle risk in the financial market. This paper aimed to adopt two novel multivariate copulas, Vine copulas and Factor copulas, to measure and compare the financial risks of the emerging economy, developed economy, and global economy. In this paper, we used data from three groups (BRICS, which stands for emerging markets, specifically, those of Brazil, Russia, India, China, and South Africa; G7, which refers to developed countries; and G20, which represents the global market), separated into three periods (pre-crisis, crisis, and post-crisis) and weighed Value at Risk (VaR) and Expected Shortfall (ES) (based on their market capitalization) to compare among three copulas, C-Vine, D-Vine, and Factor copulas. Also, real financial data demonstrated that Factor copulas have stronger stability and perform better than the other two copulas in high-dimensional data. Moreover, we showed that BRICS has the highest risk and G20 has the lowest risk of the three groups.

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Citations
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Journal ArticleDOI

Analyzing the Causality and Dependence between Gold Shocks and Asian Emerging Stock Markets: A Smooth Transition Copula Approach

TL;DR: In this article, the authors investigated the causality and dependence structure of gold shocks and Asian emerging stock markets and proposed a Smooth Transition Dynamic Copula that allows for the structural change in time-varying dependence between gold and Asian stock markets' volatilities.
Journal ArticleDOI

Examining the Interdependence between the Exchange Rates of China and ASEAN Countries: A Canonical Vine Copula Approach

TL;DR: Based on the canonical vine copula approach, this paper examined the interdependence between the exchange rates of the Chinese Yuan and the currencies of major Association of Southeast Asian Nations (ASEAN) countries.
Journal ArticleDOI

Measuring risk spillovers from multiple developed stock markets to China: A vine-copula-GARCH-MIDAS model

TL;DR: Wang et al. as mentioned in this paper developed a vine-copula-GARCH-MIDAS model to estimate the multivariate joint distribution, and then derive CoVaR-type risk measures.
Journal ArticleDOI

Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends

TL;DR: In this article, the authors employed the ARMAX process to investigate the influence of Google Trends on contagion prediction in seven out of 10 stock market pairs: SP-FTSE, SP-TSX and SP-DAX.
Journal ArticleDOI

Spatial tale of G-7 and Brics stock markets during COVID-19: An event study

TL;DR: Ledwani et al. as mentioned in this paper investigated the impact of COVID-19 on the performance of major stock markets of G-7 nations vis-a-vis BRICS nations.
References
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Journal ArticleDOI

Conditional value-at-risk for general loss distributions

TL;DR: Fundamental properties of conditional value-at-risk are derived for loss distributions in finance that can involve discreetness and provides optimization shortcuts which, through linear programming techniques, make practical many large-scale calculations that could otherwise be out of reach.
Journal ArticleDOI

Evaluating Interval Forecasts.

TL;DR: In this paper, a consistent framework for conditional interval forecast evaluation with higher-order moment dynamics is presented. But this framework is not suitable for the case of exchange rate forecasting, where higher order moment dynamics are present.
Posted Content

Techniques for Verifying the Accuracy of Risk Measurement Models

TL;DR: In this paper, the authors consider the formal statistical procedures that could be used to assess the accuracy of value at risk (VaR) estimates and show that verification of the accuracy becomes substantially more difficult as the cumulative probability estimate being verified becomes smaller.
Journal ArticleDOI

Pair-copula constructions of multiple dependence

TL;DR: This work uses the pair-copula decomposition of a general multivariate distribution and proposes a method for performing inference, which represents the first step towards the development of an unsupervised algorithm that explores the space of possible pair-Copula models, that also can be applied to huge data sets automatically.
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