Abstract :
Three computational methods are applied to traditional VaR model at present, including delta positive, Monte Carlo simulation and history simulation, however, some defects exist in the traditional methods such as fat tail, nonlinearity, big estimated error, complexity of the calculations, etc. In this paper, SVM theory is applied to VaR model by choosing Gaussian normal distribution function as kernel function. The new VaR model overcomes the defects, and is effective in approximating and generalizing compared with traditional ones; therefore, it is a significant complement to VaR system.
Keywords :
Gaussian distribution; finance; normal distribution; risk management; support vector machines; Gaussian normal distribution function; VaR model; kernel function; support vector machine; value at risk; Computational modeling; Cybernetics; Gaussian distribution; History; Kernel; Machine learning; Reactive power; Risk management; Support vector machines; Tail; Simulation; Support vector machine; VaR model;
