Modelling multivariate volatilities via conditionally uncorrelated components
@article{Fan2005ModellingMV, title={Modelling multivariate volatilities via conditionally uncorrelated components}, author={Jianqing Fan and Mingjin Wang and Qiwei Yao}, journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, year={2005}, volume={70} }
Summary. We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix‐valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the estimated CUCs has been…
63 Citations
Modelling Multivariate Volatilities by Common Factors : An Innovation Expansion Method
- Mathematics
- 2010
We consider a framework for modelling conditional variance (volatility) of a multivariate time series by common factors. We estimate the factor loading space and the number of factors by a stepwise…
MODELLING MULTIVARIATE VOLATILITY PROCESSES USING TEMPORAL INDEPENDENT COMPONENT ANALYSIS
- Economics
- 2007
Forecasting temporal dependence in second order moments of returns is a relevant problem in many contexts of financial econometrics. It is commonly accepted that financial volatilities move together…
Bayesian inference of multivariate rotated GARCH models with skew returns
- Mathematics, EconomicsCommun. Stat. Simul. Comput.
- 2021
Abstract Bayesian inference is proposed for volatility models, targeting financial returns, which exhibit high kurtosis and slight skewness. Rotated GARCH models are considered which can accommodate…
Multivariate volatility models
- Business
- 2007
Correlations between asset returns are important in many financial applications. In recent years, multivariate volatility models have been used to describe the time-varying feature of the…
Multivariate Rotated ARCH Models
- Mathematics
- 2012
This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic…
Modeling Multivariate Volatilities via Latent Common Factors
- Economics
- 2016
A dimension-reduction method to model a multivariate volatility process and to estimate a lower-dimensional space, to be called the volatility space, within which the dynamics of the multi-dimensional volatility process is confined.
On the Robustness of the Principal Volatility Components
- Economics, MathematicsJournal of Empirical Finance
- 2019
It is shown that outliers have a devastating effect on the construction of the principal volatility components and on the forecast of the conditional covariance matrix and consequently in economic and financial applications based on this forecast.
Robust Methods in Time Series Models with Volatility
- Mathematics
- 2011
Volatility in time series data is often accounted into the model by postulating a conditionally heteroskedastic variance. In-sample prediction maybe satisfactory but the out-sample prediction is…
Consistently determining the number of factors in multivariate volatility modelling
- Mathematics
- 2015
Consistently determining the number of factors plays an important role in factor modelling for volatility of multivariate time series. In this paper, the modelling is firstly extended to handle the…
A multivariate generalized independent factor GARCH model with an application to financial stock returns
- Economics, Computer Science
- 2008
The GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models, which are fitted to the volatility of each IC.
References
SHOWING 1-10 OF 81 REFERENCES
Dynamic Conditional Correlation
- Mathematics
- 2002
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…
Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH Models
- Mathematics
- 2000
Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of returns. A new class of multivariate models called dynamic conditional…
Multivariate stochastic variance models
- Mathematics
- 1994
Changes in variance, or volatility, over time can be modelled using the approach based on autoregressive conditional heteroscedasticity (ARCH). However, the generalizations to multivariate series can…
The likelihood of multivariate GARCH models is ill-conditioned
- Economics
- 1999
The likelihood of multivariate GARCH models is ill-conditioned because of two facts. First, financial time series often display high correlations, implying that an eigenvalue af the conditional…
Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model
- Economics, Mathematics
- 1990
A multivariate time series model with time varying conditional variances and covariances, but constant conditional correlations is proposed. In a multivariate regression framework, the model is…
Testing for Linear Autoregressive Dynamics under Heteroskedasticity
- Economics, Mathematics
- 2000
A puzzling characteristic of asset returns for various frequencies is the often observed positive autocorrelation at lag one. To some extent this can be explained by standard asset pricing models…
Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing
- Mathematics
- 2001
A new representation of the diagonal Vech model is given using the Hadamard product. Sufficient conditions on parameter matrices are provided to ensure the positive definiteness of covariance…
A Note on Diagnosing Multivariate Conditional Heteroscedasticity Models
- Mathematics
- 1999
In this paper we consider several tests for model misspecification after a multivariate conditional heteroscedasticity model has been fitted. We examine the performance of the recent test due to Ling…
Least absolute deviations estimation for ARCH and GARCH models
- Mathematics
- 2003
Hall & Yao (2003) showed that, for ARCHsGARCH, i.e. autoregressive conditional heteroscedasticsgeneralised autoregressive conditional heteroscedastic, models with heavy-tailed errors, the…