A Quantile-Data Mapping Model for Value-at-Risk Based on BP and Support Vector Regression

2005 ◽  
pp. 1094-1102
Author(s):  
Xiaodao Wu ◽  
Yanfeng Sun ◽  
Yanchun Liang
Keyword(s):  
At Risk ◽  
Support Vector Regression ◽  
Value At Risk ◽  
Support Vector ◽  
Data Mapping ◽  
Mapping Model

Estimating value at risk with semiparametric support vector quantile regression

2011 ◽  
Vol 27 (4) ◽  
pp. 685-700 ◽  
Author(s):  
Jooyong Shim ◽  
Yongtae Kim ◽  
Jangtaek Lee ◽  
Changha Hwang
Keyword(s):  
At Risk ◽  
Quantile Regression ◽  
Value At Risk ◽  
Support Vector

Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

2017 ◽  
Vol 67 ◽  
pp. 355-367 ◽  
Author(s):  
Heng-Guo Zhang ◽  
Chi-Wei Su ◽  
Yan Song ◽  
Shuqi Qiu ◽  
Ran Xiao ◽  
...  
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
High Dimensional ◽  
Mapping Model ◽  
Random Mapping

scholarly journals Combination of Value-at-Risk Models with Support Vector Machine

2009 ◽  
Vol 16 (5) ◽  
pp. 791-801
Author(s):  
Yong-Tae Kim ◽  
Joo-Yong Shim ◽  
Jang-Taek Lee ◽  
Chang-Ha Hwang
Keyword(s):  
At Risk ◽  
Support Vector Machine ◽  
Value At Risk ◽  
Support Vector ◽  
Risk Models

An Improved Historical Simulation Method to Estimate the Amount of Refined Oil Retail Value at Risk VaR

2013 ◽  
Vol 734-737 ◽  
pp. 1711-1718
Author(s):  
Yong Tao Wan ◽  
Zhi Gang Zhang ◽  
Lu Tao Zhao
Keyword(s):  
At Risk ◽  
Crude Oil ◽  
Value At Risk ◽  
Financial Risk ◽  
Rapid Development ◽  
Simulation Method ◽  
Support Vector ◽  
Refined Oil ◽  
Historical Simulation ◽  
Oil Market

The international crude oil market is complicated in itself and with the rapid development of China in recent years, the dramatic changes of the international crude oil market have brought some risk to the security of Chinas oil market and the economic development of China. Value at risk (VaR), an effective measurement of financial risk, can be used to assess the risk of refined oil retail sales as well. However, VaR, as a model that can be applied to complicated nonlinear data, has not yet been widely researched. Therefore, an improved Historical Simulation Approach, historical stimulation of genetic algorithm to parameters selection of support vector machine, HSGA-SVMF, in this paper, is proposed, which is based on an approach the historical simulation with ARMA forecasts, HSAF. By comparing it with the HSAF and HSGA-SVMF approach, this paper gives evidence to show that HSGA-SVMF has a more effective forecasting power in the field of amount of refined oil.

Extended Robust Support Vector Machine Based on Financial Risk Minimization

2014 ◽  
Vol 26 (11) ◽  
pp. 2541-2569 ◽  
Author(s):  
Akiko Takeda ◽  
Shuhei Fujiwara ◽  
Takafumi Kanamori
Keyword(s):  
At Risk ◽  
Support Vector Machine ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Support Vector ◽  
Risk Minimization ◽  
Hinge Loss ◽  
Margin Distribution

Financial risk measures have been used recently in machine learning. For example, [Formula: see text]-support vector machine ([Formula: see text]-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than [Formula: see text]-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM’s possibility of achieving a better prediction performance with proper parameter setting.

Value-at-risk support vector machine: stability to outliers

2013 ◽  
Vol 28 (1) ◽  
pp. 218-232 ◽  
Author(s):  
Peter Tsyurmasto ◽  
Michael Zabarankin ◽  
Stan Uryasev
Keyword(s):  
At Risk ◽  
Support Vector Machine ◽  
Value At Risk ◽  
Support Vector
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