Mitic, Peter;
(2018)
Noise Reduction in a Reputation Index.
International Journal of Financial Studies
, 6
(1)
, Article 19. 10.3390/ijfs6010019.
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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.
| Type: | Article |
|---|---|
| Title: | Noise Reduction in a Reputation Index |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/ijfs6010019 |
| Publisher version: | https://doi.org/10.3390/ijfs6010019 |
| Language: | English |
| Additional information: | © The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | reputation; reputation index; signal to noise; S/N; state-space; Kalman; time series; low pass filters; butterworth; moving average |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163497 |
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