Contagion determination via copula and volatility threshold models

Arakelian, Veni; Δελλαπόρτας, Πέτρος

doi:10.1080/14697680903410023

Cited by 23 publications

(21 citation statements)

References 22 publications

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“…In this context, Rodriguez (2007), Chen and Poon (2007), Chollete et al (2009) and Arakelian and Dellaportas (2012) addressed the time-varying dependency between stock markets using the copula methodology. In contrast to the study by Forbes and Rigobon (2002), they found evidence of contagion.…”

Section: Introductionmentioning

confidence: 99%

Assessing stock market dependence and contagion

Abbara¹,

Zevallos

2014

Quantitative Finance

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To accomplish this task, this paper combines copula modelling with time-varying parameters and pair-copula composition of multiple dependence. The bivariate analyses show an asymmetric dependence between the stock markets as well as contagion. In addition, this work proposes a method to assess the linkages and contagion between two stock markets which takes into account the effects of a third stock market. In applying this method, conditioned on the USA market, most of the evidence of contagion between the Latin American or European markets disappears, but important dependence levels still remain.

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Section: Introductionmentioning

confidence: 99%

Assessing stock market dependence and contagion

Abbara¹,

Zevallos

2014

Quantitative Finance

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“…This effect is known as financial contagion, see e.g. Rodriguez (2003) and Arakelian and Dellaportas (2006). Thus, in multivariate GARCH models, it is important the estimation of dependence measures and volatilities as a function of t. Given the MCMC output, we can obtain samples from the posterior distribution of the individual volatilities, h it , by evaluating their values h…”

Section: Bayesian Inference and Predictionmentioning

confidence: 99%

“…The log return bivariate series, whose sample size is T = 1543, is plotted in Figure 4. This sample has been analyzed previously in Arakelian and Dellaportas (2006) using a copula threshold model which changes discretely over time. Their model predicts four structural breaks in the dependence structure of the series.…”

Section: Real Datamentioning

confidence: 99%

“…This allows us to identify contagion effects or changing dependence structures during periods of financial instability, see e.g. Rodriguez (2003) and Arakelian and Dellaportas (2006). Moreover, we describe how to obtain Bayesian estimations of portfolio VaR and predictive intervals which are much more informative than simple point forecasts.…”

Section: Introductionmentioning

confidence: 99%

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Time-varying joint distribution through copulas

Ausín

Lopes

2010

Computational Statistics & Data Analysis

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This paper deals with the analysis of temporal dependence in multivariate time series. The dependence structure between the marginal series is modelled through the use of copulas which, unlike the correlation matrix, give a complete description of the joint distribution. The parameters of the copula function vary through time following certain evolution equations depending on their previous values and the historical data. The marginal time series follow standard univariate GARCH models. We develop full Bayesian inference where the whole set of model parameters is estimated simultaneously. This represents an essential difference with previous approaches in the literature where the marginal and the copula parameters are estimated separately in two consecutive steps. Moreover, we propose a Bayesian procedure for the estimation of the Value-at-Risk (VaR) of a portfolio of assets, providing point estimates and predictive intervals. The proposed copula model allows us to capture the dependence structure between the individual assets which strongly influences the portfolio VaR. Finally, we also address the problem of optimal portfolio selection based on the estimation of mean-VaR efficient frontiers.The proposed approach is illustrated with simulated and real financial time series.

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“…Several studies in the past invoked copula constructions to investigate either market dependencies, contagion or spillover effects as well, cf. [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Regime Switching Vine Copula Models for Global Equity and Volatility Indices

et al. 2017

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For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature that correlations and higher moments between different indices tend to vary in time. However, to the best of our knowledge, no one has yet considered a global setup including both equity and implied volatility indices of various continents, and allowing for a changing dependence structure. We aim to close this gap by applying Markov-switching R-vine models to investigate the existence of different, global dependence regimes. In particular, we identify times of "normal" and "abnormal" states within a data set consisting of North-American, European and Asian indices. Our results confirm the existence of joint points in a time at which global regime switching between two different R-vine structures takes place.

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Contagion determination via copula and volatility threshold models

Cited by 23 publications

References 22 publications

Assessing stock market dependence and contagion

Assessing stock market dependence and contagion

Time-varying joint distribution through copulas

Regime Switching Vine Copula Models for Global Equity and Volatility Indices

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