American Academic Scientific Research Journal for Engineering, Technology, and Sciences
Abstract
This paper proposes an improvement to the long memory stochastic volatility (LMSV) model to forecast volatility using high frequency data. To allow frequency domain quasi-maximum likelihood (FDQML) estimation, we suggest a parsimonious normalization procedure that avoids repetitive parameter estimation. This resultantly produces more efficient parameter estimation as less estimation error is involved. Besides, a de-trending procedure is proposed prior to the de-seasonalization procedure to improve the identification of seasonal patterns. We compare the performance of volatility forecasts by the proposed refined FDQML-LMSV model with the existing LMSV and the linear long memory model that fits to logarithms of realised volatility. The empirical results show that the proposed method outperforms the existing models statistically, and the output of the proposed method improves the accuracy and efficiency in value-at-risk forecasting
