We calculate the Hurst exponent HΓ°tΓ of several time series by dynamical implementation of a recently proposed scaling technique: the detrending moving average (DMA). In order to assess the accuracy of the technique, we calculate the exponent HΓ°tΓ for artificial series, simulating monofractal Browni
Quantile smoothing in financial time series
β Scribed by Klaus Abberger
- Publisher
- Springer-Verlag
- Year
- 1997
- Tongue
- English
- Weight
- 619 KB
- Volume
- 38
- Category
- Article
- ISSN
- 0932-5026
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We present in this paper a neural network (NN) smoothing architecture for non-parametric estimation of the trend of a time series, observed at constant regular time intervals. The NN-smoother computes the trend in the state domain and minimizes a cost function with a regularization term. The regular
## Abstract We propose to decompose a financial time series into trend plus noise by means of the exponential smoothing filter. This filter produces statistically efficient estimates of the trend that can be calculated by a straightforward application of the Kalman filter. It can also be interprete