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Int. J. Financial Stud. 2018, 6(1), 19;

Noise Reduction in a Reputation Index

Santander UK, 2 Triton Square, Regent’s Place, London NW1 3AN, UK
Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
Laboratoire d’Excellence sur la Régulation Financière (LabEx ReFi), 75272 Paris, France
Received: 1 January 2018 / Revised: 19 January 2018 / Accepted: 1 February 2018 / Published: 7 February 2018
(This article belongs to the Special Issue Finance, Financial Risk Management and their Applications)
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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. View Full-Text
Keywords: reputation; reputation index; signal to noise; S/N; state-space; Kalman; time series; low pass filters; butterworth; moving average reputation; reputation index; signal to noise; S/N; state-space; Kalman; time series; low pass filters; butterworth; moving average

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Mitic, P. Noise Reduction in a Reputation Index. Int. J. Financial Stud. 2018, 6, 19.

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