14 research outputs found
The social status of adult and juvenile sex offenders in prison: An analysis of the importation model
Exponential smoothing weighted correlations
In many practical applications, correlation matrices might be affected by the “curse of
dimensionality” and by an excessive sensitiveness to outliers and remote observations.
These shortcomings can cause problems of statistical robustness especially accentuated
when a system of dynamic correlations over a running window is concerned.
These drawbacks can be partially mitigated by assigning a structure of weights to
observational events. In this paper, we discuss Pearson’s ρ and Kendall’s
τ correlation matrices, weighted with an exponential smoothing,
computed on moving windows using a data-set of daily returns for 300 NYSE
highly capitalized companies in the period between 2001 and 2003. Criteria for
jointly determining optimal weights together with the optimal length of the running window
are proposed. We find that the exponential smoothing can provide more robust and reliable
dynamic measures and we discuss that a careful choice of the parameters can reduce the
autocorrelation of dynamic correlations whilst keeping significance and robustness of the
measure. Weighted correlations are found to be smoother and recovering faster from market
turbulence than their unweighted counterparts, helping also to discriminate more
effectively genuine from spurious correlations