@article{(Open Science Index):https://publications.waset.org/pdf/10005813,
	  title     = {Normalizing Logarithms of Realized Volatility in an ARFIMA Model},
	  author    = {G. L. C. Yap},
	  country	= {},
	  institution	= {},
	  abstract     = {Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process. The Gaussianity assumption simplifies the parameter estimation using the Whittle approximation. Nonetheless, this assumption may not be met in the finite samples and there may be a need to normalize the financial series. Based on the empirical indices S&P500 and DAX, this paper examines the performance of the linear volatility model pre-treated with normalization compared to its existing counterpart. The empirical results show that by including normalization as a pre-treatment procedure, the forecast performance outperforms the existing model in terms of statistical and economic evaluations.
},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {10},
	  number    = {12},
	  year      = {2016},
	  pages     = {609 - 614},
	  ee        = {https://publications.waset.org/pdf/10005813},
	  url   	= {https://publications.waset.org/vol/120},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 120, 2016},
	}