摘要：This paper proposes a new kind of non-linear hybrid model. In the proposed model, mean non-linearity is represented by using an adaptive neuro-fuzzy inference system (ANFIS) whereas variance is represented using a conditional self-regressive heteroscedastic component. The mathematical formula for this type of model is shown and a method to estimate it is proposed. In addition, a specification strategy is developed for the proposed model, based on a battery of statistical soft transaction regression (STR) tests and on verosimility radius testing. As a case study, the IBM stock closing price series dynamics were modeled, which is commonly used as a benchmark in the literature on time series. Results indicate that the model developed represents the dynamics of the studied series better than other models with similar characteristics.
其他摘要：This paper proposes a new kind of non-linear hybrid model. In the proposed model, mean non-linearity is represented by using an adaptive neuro-fuzzy inference system (ANFIS) whereas variance is represented using a conditional self-regressive heteroscedastic component. The mathematical formula for this type of model is shown and a method to estimate it is proposed. In addition, a specification strategy is developed for the proposed model, based on a battery of statistical soft transaction regression (STR) tests and on verosimility radius testing. As a case study, the IBM stock closing price series dynamics were modeled, which is commonly used as a benchmark in the literature on time series. Results indicate that the model developed represents the dynamics of the studied series better than other models with similar characteristics.
关键词：ANFIS;ARCH;heteroscedasticity;time series;non-linear models;ANFIS;ARCH;heterocedasticidad;series temporales;modelos no lineales;ANFIS;ARCH;hetere cedasticidade;séries temporais;modelos não lineais
其他关键词：ANFIS; ARCH; heteroscedasticity; time series; non-linear models
