@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}, }