Hybrid method of using neural networks and ARMA model to forecast value at risk (VAR) in the Chinese stock market

2008 ◽  
Vol 11 (6) ◽  
pp. 1093-1108
Author(s):  
Hae-Ching Chang ◽  
Jian-Hsin Chou ◽  
Cheng-Te Chen ◽  
Chin-Shan Hsieh
Keyword(s):  
Neural Networks ◽  
At Risk ◽  
Stock Market ◽  
Hybrid Method ◽  
Value At Risk ◽  
Arma Model ◽  
Chinese Stock Market

scholarly journals Estimating value-at-risk for chinese stock market by switching regime ARCH model

2006 ◽  
Vol 2 (2) ◽  
pp. 145-163
Author(s):  
W.C. Ip ◽  
◽  
H. Wong ◽  
Jiazhu Pan ◽  
Keke Yuan ◽  
...  
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Value At Risk ◽  
Chinese Stock Market ◽  
Arch Model ◽  
Switching Regime

scholarly journals On the factors of Bitcoin’s value at risk

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ji Ho Kwon
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Trading Volume ◽  
Corporate Bond ◽  
Policy Uncertainty ◽  
Chinese Stock Market ◽  
Stock Market Index ◽  
Market Index ◽  
The Us ◽  
Search Index

AbstractThis study investigates the factors of Bitcoin’s tail risk, quantified by Value at Risk (VaR). Extending the conditional autoregressive VaR model proposed by Engle and Manganelli (2004), I examine 30 potential drivers of Bitcoin’s 5% and 1% VaR. For the 5% VaR, quantity variables, such as Bitcoin trading volume and monetary policy rate, were positively significant, but these effects were attenuated when new samples were added. The 5% VaR responds positively to the Internet search index and negatively to the fluctuation of returns on commodity variables and the Chinese stock market index. For the 1% VaR, variables related to the macroeconomy play a key role. The consumer sentiment index exerts a strong positive effect on the 1% VaR. I also find that the 1% VaR has positive relationships with the US economic policy uncertainty index and the fluctuation of returns on the corporate bond index.

scholarly journals Portfolio Optimization for Stock Market in Ghana Using Value-at-Risk (VaR)

2020 ◽  
Vol 5 (3) ◽  
pp. 61
Author(s):  
Eric Kwame Austro Gozah ◽  
Eric Neebo Wiah ◽  
Albert Buabeng ◽  
Paul Yaw Addai Yeboah
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Portfolio Optimization ◽  
Value At Risk

scholarly journals Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

2013 ◽  
Vol 21 (4) ◽  
pp. 316-336 ◽  
Author(s):  
Georgios Sermpinis ◽  
Jason Laws ◽  
Christian L. Dunis
Keyword(s):  
Neural Networks ◽  
At Risk ◽  
Value At Risk

The Influences of Book-to-Price Ratio and Stock Capitalization on Value-at-Risk Estimation in Taiwan Stock Market

2018 ◽  
Vol 56 (5) ◽  
pp. 1055-1072
Author(s):  
Tsung-Che Wu ◽  
Hung-Hsi Huang ◽  
Ching-Ping Wang ◽  
Yi-Lin Zhong
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Value At Risk ◽  
Risk Estimation ◽  
Price Ratio
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