Value-at-Risk prediction for the Brazilian stock market: A comparative study between Parametric Method, Feedforward and LSTM Neural Network

Author(s):  
Daniel Reghin ◽  
Fabio Lopes
Author(s):  
Eric Kwame Austro Gozah ◽  
Eric Neebo Wiah ◽  
Albert Buabeng ◽  
Paul Yaw Addai Yeboah

2017 ◽  
Vol 4 (4) ◽  
pp. 84 ◽  
Author(s):  
Lan-Ya Ma ◽  
Zi-Yu Li

In this paper, we address the issue that the financial institutes need to identify the risk of margin trading, and we analyze the volatility and value at risk of China’s Shanghai-Shenzhen 300 Index before and since the inception of margin trading policy. We first analyze the statistical attributes of the logarithmic return series. Then we build the GJR-GARCH to model the difference of volatility and leverage effect of the two sample time series. After that, we calculate the dynamic value at risk based on the parametric method. Moreover, we apply the filtered historical simulation with the help of Bootstrap technique to obtain the pathway of return and finally calculate the value at risk under the two circumstances. In the end, we propose some reasonable policies to financial risk management department.


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