Noise Reduction in a Reputation Index

International Journal of Financial Studies

Peter Mitic¹ 

[1] Santander UK, 2 Triton Square, Regent’s Place, London NW1 3AN, UK;

关键词: reputation;    reputation index;    signal to noise;    S/N;    state-space;    Kalman;    time series;    low pass filters;    butterworth;    moving average;   

DOI  :  10.3390/ijfs6010019

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown