A Portmanteau Test for Multivariate GARCH when the Conditional Mean is an ECM: Theory and Empirical Applications

2005 ◽  
pp. 125-151
Author(s):  
Chor-yiu Sin
Keyword(s):  
Multivariate Garch ◽  
Conditional Mean

scholarly journals Volatility Linkages between Equity Markets of Pakistan, India, Singapore and Hong Kong: A GARCH BEKK Approach

2012 ◽  
Vol 4 (1) ◽  
pp. 47-54
Author(s):  
Muhammad Junaid Iqbal ◽  
Afsheen Abrar . ◽  
Nagina Jamil . ◽  
Abid Ali Shah . ◽  
AhsanulHaqSatti .
Keyword(s):  
Hong Kong ◽  
Stock Exchange ◽  
Equity Markets ◽  
Multivariate Garch ◽  
Volatility Spillover ◽  
Investment Portfolio ◽  
Conditional Mean ◽  
Time Index ◽  
Strait Time ◽  
Volatility Linkages

The purpose of current study is to explore the volatility linkages between four Asian equity markets, which arePakistan (Karachi Stock Exchange), India (Bombay Stock Exchange), Hong Kong (Hang Sang Index) and Singapore (Strait Time Index). We estimate Multivariate GARCH BEKK model using weekly returns from January 2000 to August 2011.Direct evidences of linkages are found among all markets with respect to conditional mean returns and volatility.Own volatility spillover is found greater than cross volatility spillover in all emerging and developed economies.The insinuation of this study is that overseas investors may take advantage from the decrease of uncertainty by accumulating the stocks in the emerging markets to their investment portfolio.

scholarly journals Long Memory Process in Asset Returns with Multivariate GARCH Innovations

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Imène Mootamri
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
Asset Returns ◽  
Multivariate Garch ◽  
Time Varying ◽  
Memory Process ◽  
Conditional Moment ◽  
Conditional Mean ◽  
Long Memory Process

The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long-term dependence in stock returns. More precisely, the long-term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process, and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confirm the presence of long memory property in the conditional mean of all stock returns.

scholarly journals Two-Dimensional Sampling-Recovery Algorithm of a Realization of Gaussian Processes on the Input and Output of Linear Systems

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1079
Author(s):  
Vladimir Kazakov ◽  
Mauro A. Enciso ◽  
Francisco Mendoza
Keyword(s):  
Gaussian Processes ◽  
Research Method ◽  
General Algorithm ◽  
Two Dimensional ◽  
Recovery Algorithm ◽  
Conditional Mean ◽  
Quality Of Recovery ◽  
Input And Output ◽  
Statistical Relationships

Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.

scholarly journals Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 282
Author(s):  
Mabel Morales-Otero ◽  
Vicente Núñez-Antón
Keyword(s):  
Infant Mortality ◽  
Count Data ◽  
Spatial Dependence ◽  
Poisson Model ◽  
Mortality Rates ◽  
Estimation Methods ◽  
Neighborhood Structure ◽  
Specific Context ◽  
Count Data Models ◽  
Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

scholarly journals Networks in risk spillovers: A multivariate GARCH perspective

2021 ◽  
Author(s):  
Monica Billio ◽  
Massimiliano Caporin ◽  
Lorenzo Frattarolo ◽  
Loriana Pelizzon
Keyword(s):  
Multivariate Garch
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