The Stochastic Volatility Model, Regime Switching and Value-at-Risk (VaR) in International Equity Markets

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ISSN Print: 2162-2434
ISSN Online: 2162-2442
www.scirp.org/journal/jmf
E-mail: jmf@scirp.org

"The Stochastic Volatility Model, Regime Switching and Value-at-Risk (VaR) in International Equity Markets"
written by Ata Assaf,
published by Journal of Mathematical Finance, Vol.7 No.2, 2017

has been cited by the following article(s):

[1]	MONETARY POLICY DYNAMICS IN NIGERIA: EMPIRICAL EVIDENCES FROM BAYESIAN VECTOR AUTOREGRESSION WITH STOCHASTIC VOLATILITY
	FUDMA JOURNAL OF SCIENCES, 2024

[2]	Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
	… Economics and Policy, 2023

[3]	Dynamic Risk Measurement by EVT based on Stochastic Volatility models via MCMC
	arXiv preprint arXiv:2201.09434, 2022

[4]	Heterojen otoregresif modeller yardımı ile gerçekleşen oynaklık tahmini: BIST 100 örneği
	2021

[5]	A comparative evaluation of VaR models using Monte Carlo simulations
	2020

[6]	Geleneksel olmayan para politikası uygulamaları döneminde Dolar-TL'nin volatilite dinamiklerinin incelenmesi: Asimetrik stokastik volatilite modeline dayalı analizler
	2020

[7]	Finansal Varlık Fiyatlarındaki Yüksek Oranlı Ani Değişimlerin (Price Jumps) Etkilerinin Analizi: Türkiye Örneği
	Van Yüzüncü Y?l üniversitesi ?ktisadi ve ?dari Bilimler Fakültesi Dergisi, 2020

[8]	Univariate and Multivariate Volatility Models for Portfolio Value at Risk
	2019

[9]	Asimetrik Stokastik Volatilite Modelinin BIST100 Endeksine Uygulanmasi.
	2019

[10]	Asimetrik Stokastik Volatilite Modelinin BIST100 Endeksine Uygulanması
	Iğdır Üniversitesi Sosyal Bilimler Dergisi, 2019

[11]	Analysis of Multistate Autoregressive Models
	2018

[1]	MONETARY POLICY DYNAMICS IN NIGERIA: EMPIRICAL EVIDENCES FROM BAYESIAN VECTOR AUTOREGRESSION WITH STOCHASTIC VOLATILITY FUDMA JOURNAL OF SCIENCES, 2024 DOI:10.33003/fjs-2024-0802-2309

[2]	Data-Driven Risk Measurement by SV-GARCH-EVT Model 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 2024 DOI:10.1109/DOCS63458.2024.10704237

[3]	Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis Journal of International Commerce, Economics and Policy, 2023 DOI:10.1142/S179399332350014X

[4]	Analysis of Multistate Autoregressive Models IEEE Transactions on Signal Processing, 2018 DOI:10.1109/TSP.2018.2811757

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