Add to ORKGModelling and forecasting high dimensional covariance matrices is a key challenge in data-richenvironments involving even thousands of time series since most of the available models sufferfrom the curse of dimensionality. In this paper, we challenge some popular multivariate GARCH(MGARCH) and Stochastic Volatility (MSV) models by fitting them to forecast the conditionalcovariance matrices of financial portfolios with dimension up to 1000 assets observed daily over a30-year time span. The time evolution of the conditional variances and covariances estimated bythe different models is compared and evaluated in the context of a portfolio selection exercise. Weconclude that, in a realistic context in which transaction costs are taken into accoun...
Large one-off events cause large changes in prices, but may not affect the volatility and correlatio...
This paper addresses the question of the selection of multivariate generalized autoregressive condit...
Abstract. The identication of the optimal forecasting model for multivariate volatility prediction ...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
This paper addresses the question of the selection of multivariate GARCH models in terms of variance...
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge st...
This paper injects factor structure into the estimation of time-varying, large-dimensional covarianc...
This paper analyses plethora of advanced multivariate econometric models, which forecast the mean an...
2014 - 2015Estimating and predicting joint second-order moments of asset portfolios is of huge impor...
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new est...
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new est...
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
In this paper, we develop the theoretical and empirical properties of a new class of multivariate GA...
Two crucial aspects to the problem of portfolio selection are the specification of the model for exp...
In this paper, we develop the theoretical and empirical properties of a new class of multi-variate G...
Large one-off events cause large changes in prices, but may not affect the volatility and correlatio...
This paper addresses the question of the selection of multivariate generalized autoregressive condit...
Abstract. The identication of the optimal forecasting model for multivariate volatility prediction ...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
This paper addresses the question of the selection of multivariate GARCH models in terms of variance...
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge st...
This paper injects factor structure into the estimation of time-varying, large-dimensional covarianc...
This paper analyses plethora of advanced multivariate econometric models, which forecast the mean an...
2014 - 2015Estimating and predicting joint second-order moments of asset portfolios is of huge impor...
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new est...
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new est...
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
In this paper, we develop the theoretical and empirical properties of a new class of multivariate GA...
Two crucial aspects to the problem of portfolio selection are the specification of the model for exp...
In this paper, we develop the theoretical and empirical properties of a new class of multi-variate G...
Large one-off events cause large changes in prices, but may not affect the volatility and correlatio...
This paper addresses the question of the selection of multivariate generalized autoregressive condit...
Abstract. The identication of the optimal forecasting model for multivariate volatility prediction ...
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