Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model

Andréas Heinen and Alfonso Valdesogo Robles

No 2009069, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine copula, which can be decomposed into a cascade of bivariate conditional copulas. Due to this decomposition, the model does not suffer from the curse of dimensionality. The canonical vine autoregressive (CAVA) captures asymmetries in the dependence structure. The model is applied to 95 S&P500 stocks. For the marginal distributions, we use non-Gaussian GARCH models, that are designed to capture skewness and kurtosis. By conditioning on the market index and on sector indexes, the dependence structure is much simplified and the model can be considered as a non-linear version of the CAPM or of a market model with sector effects. The model is shown to deliver good forecasts of Value-at-Risk.

Keywords: asymmetric dependence; high dimension; multivariate copula; multivariate GARCH; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C32 C53 G10 (search for similar items in EconPapers)
Date: 2009-11-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-rmg
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Citations: View citations in EconPapers (45)

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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2009069

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