Abstract
The daily asset (log) return is considered to consist of two parts, the positive and negative jump. These jumps are determined by the arrival of positive and negative news in the market, are not observable and their differences define the asset returns. In order to estimate the jumps, the basic discrete time-homogeneous linear Kalman filter is applied, in which all the noises are assumed to be normally distributed. Then, under the assumption that the estimated jumps have to be non-negative, the method of their pdfs’ truncation, according to the non-negativity constraints, is used, and it is accompanied by appropriate scaling. The fitting of the model is justified by examining the fitting of the estimated returns to the empirical ones. For that purpose, the daily Nasdaq returns during the three year period 2006-2008 are used.
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Theodosiadou, O., Tsaklidis, G. Estimating the Positive and Negative Jumps of Asset Returns Via Kalman Filtering. The Case of Nasdaq Index. Methodol Comput Appl Probab 19, 1123–1134 (2017). https://doi.org/10.1007/s11009-016-9532-5
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DOI: https://doi.org/10.1007/s11009-016-9532-5