scholarly journals CAViaR-WS-based HAN: conditional autoregressive value at risk-water sailfish-based hierarchical attention network for emotion classification in COVID-19 text review data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
B. Venkateswarlu ◽  
V. Viswanath Shenoi ◽  
Praveen Tumuluru
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Emotion Classification ◽  
Attention Network ◽  
Conditional Autoregressive

Conditional autoregressive value-at-risk: all flavours of CAViaR

2020 ◽  
Vol 6 (3) ◽  
pp. 238
Author(s):  
Pedro Henrique Melo Albuquerque ◽  
Matheus Facure Alves ◽  
Maísa Cardoso Aniceto ◽  
Gustavo Monteiro Pereira
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Conditional Autoregressive

scholarly journals Predicting Conditional Autoregressive Value-at-Risk for Stock Markets during Tranquil and Turbulent Periods

2015 ◽  
Vol 04 (03) ◽  
pp. 168-186 ◽  
Author(s):  
Anastassios A. Drakos ◽  
Georgios P. Kouretas ◽  
Leonidas Zarangas
Keyword(s):  
At Risk ◽  
Stock Markets ◽  
Value At Risk ◽  
Conditional Autoregressive

Several Extended CAViaR Models and Their Applications to the VaR Forecasting of the Security Markets

2016 ◽  
Vol 20 (4) ◽  
pp. 590-596
Author(s):  
Xiaorong Yang ◽  
◽  
Chun He ◽  
Jie Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Security Market ◽  
Conditional Heteroskedasticity ◽  
Model Stability ◽  
Threshold Autoregressive ◽  
Security Markets ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Out Of Sample ◽  
Conditional Autoregressive

The conditional autoregressive Value-at-Risk (CAViaR) model, as a conditional autoregressive specification for calculating the Value-at-Risk (VaR) of the security market, has been receiving more and more attentions in recent years. As asymmetry may have a significant influence on the markets and the returns may have an autoregressive mean, this study proposes some extended CAViaR models, including asymmetric indirect threshold autoregressive conditional heteroskedasticity (TARCH) model and indirect generalized autoregressive conditional heteroskedasticity (GARCH) model with an autoregressive mean. We also present two types of CAViaR-Volatility models by adding the volatility term as an exogenous explanatory variable. Our empirical results indicate that extended models perform more effectively on out-of-sample predictions, as both forecasting effect and model stability have been improved. In addition, we find that the forecasting effect is better at the lower quantile (1%) than at the higher quantile (5%); a possible explanation is that extreme market information has more impact on VaR. In addition, there is negative correlation between volatility and VaR; VaR decreases as volatility increases.

Assessing value at risk with CARE, the Conditional Autoregressive Expectile models

2009 ◽  
Vol 150 (2) ◽  
pp. 261-270 ◽  
Author(s):  
Chung-Ming Kuan ◽  
Jin-Huei Yeh ◽  
Yu-Chin Hsu
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Conditional Autoregressive

Conditional Autoregressive Value-at-Risk: all flavors of CAViaR.

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Gustavo Monteiro ◽  
Maísa Aniceto ◽  
Matheus Facure ◽  
Pedro Henrique Albuquerque
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Conditional Autoregressive

Extremwerttheorie und Value-at-Risk

2015 ◽  
Vol 44 (5) ◽  
pp. 259-267
Author(s):  
Frank Schuhmacher ◽  
Benjamin R. Auer
Keyword(s):  
At Risk ◽  
Value At Risk

Value at Risk

Controlling ◽  
2004 ◽  
Vol 16 (7) ◽  
pp. 425-426
Author(s):  
Mischa Seiter ◽  
Sven Eckert
Keyword(s):  
At Risk ◽  
Value At Risk

Stress Testing in a Value at Risk Framework

CFA Digest ◽  
1999 ◽  
Vol 29 (2) ◽  
pp. 76-78
Author(s):  
Thomas J. Latta
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stress Testing ◽  
A Value ◽  
Risk Framework
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