banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary … and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and …

Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of … accuracy the SVM has a lower model risk than the Logit on average and displays a more robust performance. This result holds …

banks in order to measure their client's degree of risk, and for rms to operate successfully. The SVM with evolutionary … and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and …

We propose a new method to estimate the empirical pricing kernel based on option data. We estimate the pricing kernel …-neutral density is approximated by a weighted kernel density estimator with varying unknown weights for di erent observations, and the … subjective density is approximated by a kernel density estimator with equal weights. We represent the European call option price …

We propose a new method to estimate the empirical pricing kernel based on option data. We estimate the pricing kernel …-neutral density is approximated by a weighted kernel density estimator with varying unknown weights for different observations, and … the subjective density is approximated by a kernel density estimator with equal weights. We represent the European call …

the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables …