Work

On the Statistical GARCH Model for Managing the Risk by Employing a Fat-Tailed Distribution in Finance

Year: 2020

Type: article

Abstract: The Conditional Value-at-Risk (CVaR) is a coherent measure that evaluates the risk for different investing scenarios. On the other hand, since the extreme value distribution has been revealed to furnish better financial and economical data adjustment in contrast to the well-known normal distribution, we here employ this distribution in investigating explicit formulas for the two common risk measures, i.e., VaR and CVaR, to have better tools in risk management. The formulas are then employed under the generalized autoregressive conditional heteroskedasticity (GARCH) model for risk management as our main contribution. To confirm the theoretical discussions of this work, the daily returns of several stocks are considered and worked out. The simulation results uphold the superiority of our findings.

Source: Symmetry

DOI: 10.3390/sym12101698

Authors: H. Viet Long, H. Bin Jebreen, I. Dassios, D. Baleanu

Institutions: Ton Duc Thang University, King Saud University, University College Dublin, Çankaya University, Institute of Space Science - INFLPR Subsidiary

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,479

Funder: King Saud University