Generalized Dynamic Factor Model + GARCH Exploiting multivariate information for univariate prediction
نویسندگان
چکیده
We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and standard GARCH performance on two samples up to 171 series, providing one-step-ahead volatility predictions of returns. The GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust with respect to different volatility proxies.
منابع مشابه
Dynamic Factor GARCH Multivariate Volatility Forecast for a Large Number of Series
We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common an...
متن کاملDynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the exibility of univariate GARCH models coupled with parsimonious parametric models for the c...
متن کاملGarch Models of Dynamic Volatility and Correlation
Economic and financial time series typically exhibit time varying conditional (given the past) standard deviations and correlations. The conditional standard deviation is also called the volatility. Higher volatilities increase the risk of assets, and higher conditional correlations cause an increased risk in portfolios. Therefore, models of time varying volatilities and correlations are essent...
متن کاملEfficient Factor GARCH Models and Factor-DCC Models
We reveal that in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on that, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; ...
متن کاملA Multivariate Perspective for Modelling and Forecasting Inflation’s Conditional Mean and Variance
We test the importance of multivariate information for modelling and forecasting inflation’s conditional mean and variance. In the literature, the existence of inflation’s conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of econ...
متن کامل