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Noise Reduction in a Reputation Index

Author

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  • Peter Mitic

    (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)

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.

Suggested Citation

  • Peter Mitic, 2018. "Noise Reduction in a Reputation Index," IJFS, MDPI, vol. 6(1), pages 1-18, February.
  • Handle: RePEc:gam:jijfss:v:6:y:2018:i:1:p:19-:d:130658
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    References listed on IDEAS

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    1. Peter Mitic, 2017. "Standardised Reputation Measurement," Papers 1705.09955, arXiv.org.
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    Cited by:

    1. Leunglung Chan, 2018. "Editorial for Special Issue “Finance, Financial Risk Management and their Applications”," IJFS, MDPI, vol. 6(4), pages 1-3, October.

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