Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195685 
Authors: 
Year of Publication: 
2018
Citation: 
[Journal:] International Journal of Financial Studies [ISSN:] 2227-7072 [Volume:] 6 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-18
Publisher: 
MDPI, Basel
Abstract: 
Assuming that a time series incorporates 'signal' and 'noise' components, we propose a method to estimate the extent of the 'noise' component by considering the smoothing properties of the state-space of the time series. A mild degree of smoothing in the state-space, applied using a Kalman filter, allows for noise estimation arising from the measurement process. It is particularly suited in the context of a reputation index, because small amounts of noise can easily mask more significant effects. Adjusting the state-space noise measurement parameter leads to a limiting smoothing situation, from which the extent of noise can be estimated. The results indicate that noise constitutes approximately 10% of the raw signal: approximately 40 decibels. A comparison with low pass filter methods (Butterworth in particular) is made, although low pass filters are more suitable for assessing total signal noise.
Subjects: 
reputation
reputation index
signal to noise
S/N
state-space
Kalman
time series
low pass filters
butterworth
moving average
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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