Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2023.2180414
A new Copula-CoVaR approach incorporating the PSO-SVM for identifying systemically important financial institutions
Tingting Zhang
Zhenpeng Tang
Sažetak
The effective identification of systemically important financial
institutions (SIFIs) is key to preventing and resolving systemic
financial risks; thus, it is of great research significance for emerging
countries to supervise SIFIs and manage systemic financial
risks. Since traditional research on identifying SIFIs does not consider
emerging machine learning models, it is difficult to properly
fit the characteristics of actual financial institutions’ asset distribution.
This paper proposes a new method for measuring SIFIs, integrating
the PSO-SVM model into the Copula-CoVaR model. This
new PSO-SVM-Copula-CoVaR model is meant to evaluate China’s
SIFIs based on the publicly traded price data of Chinese listed
financial institutions. The empirical results show that, compared
with the traditional parameter method (GARCH model) and the
nonparametric method (kernel density estimation), the marginal
distribution estimation method using the PSO-SVM method can
better fit the distribution of an institution’s financial asset return
sequence. That is, the model proposed in this paper helps regulatory
authorities improve the list of SIFIs more reasonably and
implement effective regulatory measures.
Ključne riječi
Systemically important financial institutions; PSO-SVM; Copula-CoVaR model
Hrčak ID:
306541
URI
Datum izdavanja:
31.3.2023.
Posjeta: 403 *