Noise reduction in a reputation index - Peter Mitic (International Journal of Financial Studies, 2018)
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.
Keywords: 330; reputation; reputation index; signal to noise; S/N; state-space; Kalman; time series; low pass filters; butterworth; moving average