Modelling multivariate volatilities via conditionally uncorrelated components

By Jianqing Fan, Mingjin Wang, Qiwei Yao. 2008

68 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/195d28550a2afea163557510e0a3737b535e0d84

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Statistics and Probability

Citations in the corpus

A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011, background): “on the volatilities and correlations; see, e.g. Tsay (2005) and Fan, Wang, and Yao (2008) .”
Principal component analysis for second-order stationary vector time series (2014, methodology): “ame manner as in Section 2.1. An estimator for Wy can be deﬁned as Wc y = X B∈B Xk0 k=1 ˆ 1 n−k Xn t=k+1 yty ′ tI(yt−k ∈ B) ˙ 2 , where B may consist of {u ∈ Rp : kuk 2 ≤ kytk 2} for t = 1,...,n. See Fan et al. (2008). We illustrate this idea with a real data example below. Example 7. Fig.22 are tim”
Consistently determining the number of factors in multivariate volatility modelling (2015, methodology): “To overcome the difficulties caused by overparametrization for multivariate GARCH(p, q) models, there are such proposals as those of Engle (2002), Fan, Wang, and Yao (2008), and Pan et al. (2011), among others.”
Dynamic Orthogonal Components for Multivariate Time Series (2011, methodology): “…GARCH model of Lin (1992), the full-factor GARCH model of Vrontos, Dellaportas, and Politis (2003), the orthogonal GARCH model of Alexander (2001), the generalized orthogonal GARCH model of van der Weide (2002), and the conditionally uncorrelated component (CUC) model of Fan, Wang, and Yao (2008).”
On the Forecasting Accuracy of Multivariate GARCH Models (2010, methodology): “(2003) and the conditionally uncorrelated components GARCH by Fan et al. (2008). The last category of models consists of nonlinear combinations of univariate GARCH models. They allow to specify separately N , possibly different, univariate models for the conditional variances, σ2 i,t, i = 1, ..., N ”
Generalized Thresholding of Large Covariance Matrices (2009, methodology): “Although relatively fast algorithms have been proposed by Friedman, Hastie, and Tibshirani (2008) and Rothman et al. (2008) for solving these penalized likelihood problems, they require computationally intensive methods and typically only provide a sparse estimate of the inverse, not of the…”
Multivariate Rotated ARCH Models (2014, methodology): “It is worth noting that Fan et al. (2008) make an important departure from the earlier OGARCH models by estimating U under the condition that the resulting components, et, are also conditionally uncorrelated.”
Efficient factor GARCH models and factor-DCC models (2009, methodology): “They are computed by the bootstrapping method described by Fan et al. (2005) (with 200 replications).y We use the Amari performance index Perr yFan et al. (2005) gave the bootstrapping procedure for computing standard errors (or confidence sets) of the parameters in factor GARCH models.”