scholarly journals DYNAMIC ASSET CORRELATIONS BASED ON VINES

2018 ◽  
Vol 35 (1) ◽  
pp. 167-197 ◽  
Author(s):  
Benjamin Poignard ◽  
Jean-David Fermanian
Keyword(s):  
Maximum Likelihood ◽  
Partial Correlation ◽  
Asset Returns ◽  
Multivariate Garch ◽  
Conditional Correlation ◽  
Correlation Matrices ◽  
New Family ◽  
Partial Correlations ◽  
Garch Processes ◽  
Quasi Maximum Likelihood

We develop a new method for generating dynamics of conditional correlation matrices of asset returns. These correlation matrices are parameterized by a subset of their partial correlations, whose structure is described by a set of connected trees called “vine”. Partial correlation processes can be specified separately and arbitrarily, providing a new family of very flexible multivariate GARCH processes, called “vine-GARCH” processes. We estimate such models by quasi-maximum likelihood. We compare our models with DCC and GAS-type specifications through simulated experiments and we evaluate their empirical performances.

scholarly journals ON MOMENT CONDITIONS FOR QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF MULTIVARIATE ARCH MODELS

2012 ◽  
Vol 29 (3) ◽  
pp. 545-566 ◽  
Author(s):  
Marco Avarucci ◽  
Eric Beutner ◽  
Paolo Zaffaroni
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Multivariate Garch ◽  
Moment Condition ◽  
Arch Models ◽  
Moment Conditions ◽  
True Parameter ◽  
Quasi Maximum Likelihood

This paper questions whether it is possible to derive consistency and asymptotic normality of the Gaussian quasi-maximum likelihood estimator (QMLE) for possibly the simplest multivariate GARCH model, namely, the multivariate ARCH(1) model of the Baba, Engle, Kraft, and Kroner form, under weak moment conditions similar to the univariate case. In contrast to the univariate specification, we show that the expectation of the log-likelihood function is unbounded, away from the true parameter value, if (and only if) the observable has unbounded second moment. Despite this nonstandard feature, consistency of the Gaussian QMLE is still warranted. The same moment condition proves to be necessary and sufficient for the stationarity of the score when evaluated at the true parameter value. This explains why high moment conditions, typically bounded sixth moment and above, have been used hitherto in the literature to establish the asymptotic normality of the QMLE in the multivariate framework.

scholarly journals Did Brexit change asset co-movements?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shrabani Saha ◽  
Anindya Sen ◽  
Christine Smith-Han ◽  
Dennis Wesselbaum
Keyword(s):  
Correlation Matrix ◽  
Garch Model ◽  
Asset Returns ◽  
Multivariate Garch ◽  
Financial Assets ◽  
Conditional Correlation ◽  
Content Type ◽  
Hedging Strategies ◽  
Multivariate Garch Model ◽  
Financial Portfolios

Purpose This paper aims to examine the impact of the Brexit referendum on the risk structure of financial asset prices. Co-movements are analysed using daily price returns of major stock and bond indices as well as commodities and exchange rates from June 2014 to June 2018. The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies. Design/methodology/approach The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. Findings It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. Research limitations/implications The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies. Originality/value To the best of the authors’ knowledge, research studying the underlying asset co-movements around Brexit does not exist.

scholarly journals RMT Assessments of the Market Latent Information Embedded in the Stocks' Raw, Normalized, and Partial Correlations

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Dror Y. Kenett ◽  
Yoash Shapira ◽  
Eshel Ben-Jacob
Keyword(s):  
New York ◽  
Random Matrix ◽  
Partial Correlation ◽  
Stock Exchange ◽  
Market Information ◽  
Correlation Matrices ◽  
Tel Aviv ◽  
Time Period ◽  
Partial Correlations ◽  
Tel Aviv Stock Exchange

We present here assessment of the latent market information embedded in the raw, affinity (normalized), and partial correlations. We compared the Zipf plot, spectrum, and distribution of the eigenvalues for each matrix with the results of the corresponding random matrix. The analysis was performed on stocks belonging to the New York and Tel Aviv Stock Exchange, for the time period of January 2000 to March 2009. Our results show that in comparison to the raw correlations, the affinity matrices highlight the dominant factors of the system, and the partial correlation matrices contain more information. We propose that significant stock market information, which cannot be captured by the raw correlations, is embedded in the affinity and partial correlations. Our results further demonstrate the differences between NY and TA markets.

scholarly journals RANK-BASED ESTIMATION FOR GARCH PROCESSES

2012 ◽  
Vol 28 (5) ◽  
pp. 1037-1064 ◽  
Author(s):  
Beth Andrews
Keyword(s):  
Maximum Likelihood ◽  
Garch Model ◽  
Maximum Likelihood Estimators ◽  
Dispersion Function ◽  
Model Parameters ◽  
True Parameter ◽  
Finite Samples ◽  
Garch Processes ◽  
Quasi Maximum Likelihood ◽  
The One

We consider a rank-based technique for estimating generalized autoregressive conditionally heteroskedastic (GARCH) model parameters, some of which are scale transformations of conventional GARCH parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given in Jaeckel (1972, Annals of Mathematical Statistics43, 1449–1458). They are useful for GARCH order selection and preliminary estimation. We give a limiting distribution for the rank estimators that holds when the true parameter vector is in the interior of its parameter space and when some GARCH parameters are zero. The limiting theory is used to show that the rank estimators are robust, can have the same asymptotic efficiency as maximum likelihood estimators, and are relatively efficient compared to traditional Gaussian and Laplace quasi-maximum likelihood estimators. The behavior of the estimators for finite samples is studied via simulation, and we use rank estimation to fit a GARCH model to exchange rate log-returns.

STRONG CONSISTENCY OF ESTIMATORS FOR MULTIVARIATE ARCH MODELS

1998 ◽  
Vol 14 (1) ◽  
pp. 70-86 ◽  
Author(s):  
Thierry Jeantheau
Keyword(s):  
Maximum Likelihood ◽  
Error Term ◽  
Strong Consistency ◽  
Asymptotic Properties ◽  
Maximum Likelihood Estimators ◽  
Multivariate Garch ◽  
Arch Models ◽  
Multivariate Garch Model ◽  
Quasi Maximum Likelihood ◽  
Derivatives Of

This paper deals with the asymptotic properties of quasi-maximum likelihood estimators for multivariate heteroskedastic models. For a general model, we give conditions under which strong consistency can be obtained; unlike in the current literature, the assumptions on the existence of moments of the error term are weak, and no study of the various derivatives of the likelihood is required. Then, for a particular model, the multivariate GARCH model with constant correlation, we describe the set of parameters where these conditions hold.

scholarly journals The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models

2020 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Dennis McFarland
Keyword(s):  
Network Model ◽  
Test Scores ◽  
Latent Variable ◽  
Partial Correlation ◽  
Network Models ◽  
Latent Variable Models ◽  
Correlation Matrices ◽  
Partial Correlations ◽  
Shared Variance

Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show that a network model provided better fit to matrices of partial correlations but latent variable models provided better fit to matrices of full correlations. This result is due to the fact that the use of partial correlations removes most of the covariance common to WAIS-IV tests. Modeling should be based on uncorrected correlations since these represent the majority of shared variance between WAIS-IV test scores.

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