This is the second paper presenting noise-reduced, stable correlations for long-term risk measurement. We smooth time series using Singular Spectrum Analysis (SSA) and then form the correlations from these smoothed time series. These correlations have superior time stability and are cleaned of...

We introduce a methodology from geophysics, Singular Spectrum Analysis (SSA), to obtain stable, noise-cleaned correlations for long term risk (e.g. counterparty risk). SSA is applied to time series to smooth them in a robust manner. The SSA-smoothed time series are then used to obtain the...

In this article we consider the efficient estimation of the tail distribution of the maximum of correlated normal random variables. We show that the currently recommended Monte Carlo estimator has difficulties in quantifying its precision, because its sample variance estimator is an inefficient...

In this article we consider the efficient estimation of the tail distribution of the maximum of correlated normal random variables. We show that the currently recommended Monte Carlo estimator has difficulties in quantifying its precision, because its sample variance estimator is an inefficient...

Data cleaning in the real world has to cope with new data arriving (or failing to arrive) as time passes, and which may be bad data. We illustrate MSSA data cleaning with a real-time historical simulation on some problematic data. The example also serves to determine some MSSA algorithm...

This paper introduces a powerful method for detecting and fixing unphysical spikes in time series. The method utilizes Multiple Singular Spectrum Analysis (MSSA) to define local market trends used to identify outlier data spikes that are not caused by market movements, and then effectively...

We introduce a powerful method for cleaning time series - Multi-Channel Singular Spectrum Analysis (MSSA). “Cleaning” means filling data gaps and removing unphysical spikes, which are chronic problems. MSSA utilizes all available information in “time” and “space” with...