• SEMIPARAMETRIC ESTIMATION OF VOLATILITY

Abstract

Stochastic volatility models are considerable interest in empirical finance. We investigate the use of a semiparametric model for estimating volatility.  ARCH models are commonly used to estimate volatility. But there are situations where influence of some exogenous factors on volatility is seen in practice in addition to the ARCH component. In this paper a new model for volatility is presented which includes a regressors (exogenous variable) in addition to ARCH component. The regression part is estimated using nonparametric Kernel smoothing technique and ARCH component is estimated by parametric approach. Further two methods are connected to build a combination forecasting model by combining nonparametric estimator of the regression function and parametric estimator of the ARCH effect. The practical application of the proposed model for forecasting volatility is examined for a sample of gold price returns. The proposed model shows minimum mean square error compared with existing models.