Modeling international financial returns with a multivariate regime switching copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.asymmetric dependence, canonical vine copula, international returns, regime-switching, risk management, Value-at-Risk.

Modeling International Financial Returns with a Multivariate Regime Switching Copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.Asymmetric dependence; Canonical vine copula; International returns; Regime-Switching; Risk Management; Value-at-Risk

Modelling international financial returns with a multivariate regime switching copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copula. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are construted from bivariate conditional copulas and provide a very flexible way of characterizig dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.asymmetric dependence, canonical vine copula, international returns, regime-switching, risk management, Value-at-Risk

Modeling International Financial Returns with a Multivariate Regime Switching Copula

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.Asymmetric dependence; Canonical vine copula; International returns; Regime-Switching; Risk Management; Value-at-Risk

Multivariate volatility models using copulas

In this thesis we use the notion of copulas in order to create flexible multivariate volatility models that can capture some stylized facts presented in the financial data, such as leptokurtosis, skewness and asymmetric dependence. In the first chapter we investigate multivariate regime-switching models of copulas. We provide further evidence on asymmetric dependence in international financial returns. We find that canonical vine models with asymmetric copulas perform better than models that impose symmetric dependence. These findings have important for financial implications in risk management and portfolio selection. In the second chapter we propose a new method for the construction of flexible large-dimensional copulas. This method uses the structure of canonical vines until a certain level and a multivariate copula. We show the use of this method with factors in a financial application. In the third chapter we propose a new dynamic model for volatility and dependence in high dimensions where the dependence structure is modelled with a dynamic canonical vine copula (CAVA). We show that once the stock returns are conditioned on the market and the sector returns, most of the dependence has been captured adequately. We find that many of the restrictions imposed by the Dynamic Conditional Correlation (DCC) model are not fulfilled. Moreover the CAVA model performs better than the DCC in terms of Value-at-Risk. Finally, in the fourth chapter we introduce a dynamic model of dependence based on a D-vine copula and we analyze if the dependence structure is constant over time and if it is asymmetric. We use two different data set, six exchange rates and five Asian equity indexes. We find that in both data set the dependence structure is time varying. Moreover, the dependence structure is symmetric for the exchange rates, whereas for the Asian equity indexes it is asymmetric.Doctorat en sciences économiques (ECON 3)--UCL, 200

Multivariate volatility models using copulas

In this thesis we use the notion of copulas in order to create flexible multivariate volatility models that can capture some stylized facts presented in the financial data, such as leptokurtosis, skewness and asymmetric dependence. In the first chapter we investigate multivariate regime-switching models of copulas. We provide further evidence on asymmetric dependence in international financial returns. We find that canonical vine models with asymmetric copulas perform better than models that impose symmetric dependence. These findings have important for financial implications in risk management and portfolio selection. In the second chapter we propose a new method for the construction of flexible large-dimensional copulas. This method uses the structure of canonical vines until a certain level and a multivariate copula. We show the use of this method with factors in a financial application. In the third chapter we propose a new dynamic model for volatility and dependence in high dimensions where the dependence structure is modelled with a dynamic canonical vine copula (CAVA). We show that once the stock returns are conditioned on the market and the sector returns, most of the dependence has been captured adequately. We find that many of the restrictions imposed by the Dynamic Conditional Correlation (DCC) model are not fulfilled. Moreover the CAVA model performs better than the DCC in terms of Value-at-Risk. Finally, in the fourth chapter we introduce a dynamic model of dependence based on a D-vine copula and we analyze if the dependence structure is constant over time and if it is asymmetric. We use two different data set, six exchange rates and five Asian equity indexes. We find that in both data set the dependence structure is time varying. Moreover, the dependence structure is symmetric for the exchange rates, whereas for the Asian equity indexes it is asymmetric.Doctorat en sciences économiques (ECON 3)--UCL, 200

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

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

Asymmetric CAPM dependence for large dimensions: The canonical vine autoregressive copula model. ECORE Discussion Paper

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 mode