Method of Dynamic VaR and CVaR Risk Measures Forecasting for Long Range Dependent Time Series on the Base of the Heteroscedastic Model

ISSN Print: 2153-0653
ISSN Online: 2153-0661
www.scirp.net/journal/ica
E-mail: [email protected]

Citations

"Method of Dynamic VaR and CVaR Risk Measures Forecasting for Long Range Dependent Time Series on the Base of the Heteroscedastic Model"
written by Nataliya D. Pankratova, Nataliia G. Zrazhevska,
published by Intelligent Control and Automation, Vol.8 No.2, 2017

has been cited by the following article(s):

[1]	Quintile regression based approach for dynamical VaR and CVaR forecasting using metalog distribution
	2021

[2]	Generalized Approach for Estimating and Forecasting of Dynamical VaR and CVaR Based on Metalog Distribution
	2020 - Springer, 2020

[3]	Generalized Approach for Estimatingand Forecasting of Dynamical VaRand CVaR Based on Metalog Distribution
	2020

[4]	Методи і моделі прогнозування мір динамічних фондових ризиків
	2018

[5]	Construction of prediction of dynamic risk measures VaR and CVaR for financial time series with different volatility
	2017

[1]	Using Deep Learning Conditional Value‐at‐Risk Based Utility Function in Cryptocurrency Portfolio Optimisation International Journal of Finance & Economics, 2025 DOI:10.1002/ijfe.70012

[2]	Lecture Notes in Computational Intelligence and Decision Making Advances in Intelligent Systems and Computing, 2021 DOI:10.1007/978-3-030-54215-3_15