Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation

Billio, Monica; Caporin, Massimiliano; Gobbo, Michele

doi:10.1080/17446540500428843

Cited by 136 publications

(88 citation statements)

References 13 publications

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“…The application we illustrate is the classification of the twenty sectorial indexes of the Italian Mibtel general index. They can be divided in three main sectors: These series were studied by Billio et al (2006), who deduce similar correlation dynamics within the main sectors. Anyway similar correlation dynamics do not imply similar degrees of risk.…”

Section: An Example:classifying the Italian Sectorial Indexesmentioning

confidence: 99%

Clustering heteroskedastic time series by model-based procedures

Otranto¹

2008

Computational Statistics & Data Analysis

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Section: An Example:classifying the Italian Sectorial Indexesmentioning

confidence: 99%

Clustering heteroskedastic time series by model-based procedures

Otranto¹

2008

Computational Statistics & Data Analysis

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“…The DCC model of (2002) investigates the conditional correlations in stock, bond and foreign exchange markets in USA and France. The model has been applied in several asset interactions, such as the conditional correlations across stock markets by Billio et al (2006); across stock and foreign exchange markets by Kuper and Lestano (2007) and across bond and stock markets by Dean and Faff (2001). These authors find evidence of time-varying conditional correlations.…”

Section: Brief Review Of the Literaturementioning

confidence: 93%

Investigation of Socially Responsible Investment Markets (SRI) Using Dynamic Conditional Correlation (DCC) Method: Implications for Diversification

T.¹

2010

Journal of Mathematics and Statistics

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Problem statement: Over the last ten years there has been a phenomenal growth in the amount of funds placed in SRI globally estimated to be around US$6.5 trillion while around US$55 billion in the Australian market. Accurate knowledge of correlation of the Australian SRI market with other SRI markets overseas is crucially important for Australian (SRI) investors for international portfolio diversification since portfolio diversification theory posits that the lower (higher) the correlation between markets, the higher (lower) the gains to be made. The study examines the relationship of the Australian SRI market with fourteen other markets-Canada, Denmark, France, Germany, Hong Kong, Ireland, Japan, Netherlands, Norway, South Africa, Sweden, Switzerland, United Kingdom and the United States. Approach: The relationships of the Australian Socially Responsible Investment (SRI) market with other SRI markets worldwide during the period 1994-2009 are examined based on the dynamic conditional correlation multivariate GARCH model (DCCMVGarch). In the DCC method, the multivariate conditional variance estimation is simplified by estimating univariate GARCH models for each market. Using the transformed residuals resulting from the first stage, the authors can estimate a conditional correlation estimator. The standard errors for the first stage parameters remain while the standard errors for the correlation parameters are modified. Results: Our results showed that the Australian market experienced a surge in correlation with all other markets during the global financial crisis. During the period of study, the correlation of Australia with Canada, Denmark, Norway and the United Kingdom increased over time while its correlation with other countries remained stationary. This implies that the Australian SRI market is becoming more integrated with those of Canada, Denmark, Norway and the United Kingdom. Therefore, these overseas markets provide less portfolio diversification benefits to Australian SRI investors while the other markets still offer some opportunities. Conclusion/Recommendations: This study examined the relationship of the Australian Socially Responsible Investment (SRI) market with other SRI markets worldwide during the period 1994-2009 based on dynamic conditional correlation multivariate GARCH model that provides accurate correlations over time that can be incorporated into portfolio models. Australian SRI market was analyzed with fourteen other markets around the world such as Canada, Denmark, France, Germany, Hong Kong, Ireland, Japan, Netherlands, Norway, South Africa, Sweden, Switzerland, United Kingdom and the United States. The results showed that the Australian market experienced a spike in correlation with the other markets during periods of market distress; for example, during the recent global financial crisis. In spite of the fluctuations in the correlations of Australian with the other markets the correlations generally remain below 1. Australia's correlation with Denmark, Norway and Japan in...

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“…We can therefore extend the model to allow for di erent rank correlation dynamics across several blocks of assets as in [6], [48] and [5]. These asset blocks can correspond to industries or ratings.…”

Section: DCC Gaussian Copulamentioning

confidence: 99%

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

Jin

Lehnert

2018

Dependence Modeling

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Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) for passive portfolios and dynamic optimal portfolios using Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1) Modeling the marginal distribution is important for dynamic high-dimensional multivariate models. (2) Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3) The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4) Grouped t-copulas and t-copulas with dynamic degrees of freedom further match the fat tail. (5) Correctly modeling the dependence structure makes an improvement in portfolio optimization with respect to tail risk. (6) Models driven by multivariate t innovations with exogenously given degrees of freedom provide a exible and applicable alternative for optimal portfolio risk management.

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Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation

Cited by 136 publications

References 13 publications

Clustering heteroskedastic time series by model-based procedures

Clustering heteroskedastic time series by model-based procedures

Investigation of Socially Responsible Investment Markets (SRI) Using Dynamic Conditional Correlation (DCC) Method: Implications for Diversification

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

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