Ilya Archakov
Biographic Data
| ID | 8880034 |
|---|---|
| NAME | Ilya Archakov |
| GIVEN NAMES | Ilya |
| FAMILY NAME | Archakov |
| SIGNATURE | ARCHAKOV I |
| AFFILIATIONS | York University and University of Vienna |
| ORCID | 0009-0001-0871-2622 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Cluster GARCH
We introduce a novel multivariate GARCH model with flexible convolution-t distributions that is applicable in high-dimensional systems. The model is called Cluster GARCH because it can accommodate cluster structures in the conditional correlation matrix and in tail dependencies. The expressions for the log-likelihood function and its derivatives are tractable, and the latter facilitate a score-driven model for the dynamic correlation structure. W…
A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices
We obtain a canonical representation for block matrices. The representation facilitates simple computation of the determinant, the matrix inverse, and other powers of a block matrix, as well as the matrix logarithm and the matrix exponential. These results are particularly useful for block covariance and block correlation matrices, where evaluation of the Gaussian log-likelihood and estimation are greatly simplified. We illustrate this with an em…
No prominent works on this page.
A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices
We obtain a canonical representation for block matrices. The representation facilitates simple computation of the determinant, the matrix inverse, and other powers of a block matrix, as well as the matrix logarithm and the matrix exponential. These results are particularly useful for block covariance and block correlation matrices, where evaluation of the Gaussian log-likelihood and estimation are greatly simplified. We illustrate this with an em…
Cluster GARCH
We introduce a novel multivariate GARCH model with flexible convolution-t distributions that is applicable in high-dimensional systems. The model is called Cluster GARCH because it can accommodate cluster structures in the conditional correlation matrix and in tail dependencies. The expressions for the log-likelihood function and its derivatives are tractable, and the latter facilitate a score-driven model for the dynamic correlation structure. W…
Mathematics (2 works) · Algorithm (1 work) · Applied Mathematics (1 work) · Applied Mathematics (1 work) · Autoregressive conditional heteroskedasticity (1 work) · Block (permutation group theory) (1 work) · Block matrix (1 work) · Canonical correlation (1 work) · Canonical form (1 work) · Combinatorics (1 work)