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

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.

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Robust ν-support vector machine based on worst-case conditional value-at-risk minimization

Optimization Methods and Software ◽

10.1080/10556788.2011.614608 ◽

2012 ◽

Vol 27 (6) ◽

pp. 1025-1038 ◽

Cited By ~ 8

Author(s):

Yongqiao Wang

Keyword(s):

At Risk ◽

Support Vector Machine ◽

Value At Risk ◽

Conditional Value At Risk ◽

Support Vector ◽

Risk Minimization ◽

Worst Case

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Estimating value at risk with semiparametric support vector quantile regression

Computational Statistics ◽

10.1007/s00180-011-0283-z ◽

2011 ◽

Vol 27 (4) ◽

pp. 685-700 ◽

Cited By ~ 14

Author(s):

Jooyong Shim ◽

Yongtae Kim ◽

Jangtaek Lee ◽

Changha Hwang

Keyword(s):

At Risk ◽

Quantile Regression ◽

Value At Risk ◽

Support Vector

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.734-737.1711 ◽

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.

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

Lecture Notes in Computer Science - Internet and Network Economics ◽

10.1007/11600930_110 ◽

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

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Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

Asian Journal of Education and e-Learning ◽

10.24203/ajeel.v7i1.5679 ◽

2019 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Ashok Kumar Veerasamy ◽

Daryl D'Souza ◽

Rolf Lindén ◽

Mikko-Jussi Laakso

Keyword(s):

At Risk ◽

Support Vector Machine ◽

Formative Assessment ◽

Predictive Model ◽

At Risk Students ◽

Support Vector ◽

Final Exam ◽

Introductory Programming ◽

Assessment Scores ◽

Programming Knowledge

This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process.

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