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@article{153121,
        author = {Dr. Ashwini Kumari},
        title = {Partial GARCH Models for Forecasting Volatility},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {5},
        pages = {608-614},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153121},
        abstract = {This paper deals with the forecasting of volatility using the partial GARCH model. Volatility is also influenced by some exogenous factors such as climate, scientific development, growth rate of population, political situation of a country, demonetisation etc. The influences of exogenous factors are considered in partial GARCH model. 
In this paper we modify the GARCH model by incorporating the influence of exogenous factors for improving accuracy in forecasting volatility. To study the impact of exogenous factor in volatility, we introduce the nonparametric component in the GARCH model. An improved forecasting model for Volatility is developed by combining GARCH with Nonparametric functional estimate. The performance of the proposed estimator is compared with GARCH estimator through a simulation study. Simulation study reveals that proposed model shows minimum mean square forecasting error compare to the existing model.},
        keywords = {ARCH,  GARCH, Partial linear models,Volatility.},
        month = {},
        }