Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis
Figure 1
(a, b) Plots showing the normalised pdfs of (a) kurtosis () and (b) skewness (), obtained using a sliding-window of length 50 data points, as an average of all G10 currency pairs, covering a period of 8 hours in case of 0.25 second and 0.5 second sampled data and 2 years in case of 0.5 hour sampled data. The plots clearly show the heavy-tailed, skewed, nature of FX returns at all three sampling frequencies. (c, d) An example of the normalised pdf plots showing the average kurtosis and skewness values, respectively, for synthetic data generated using Pearson type IV distributions. The data has properties similar to the average of the distributions presented in (a) and (b). The vertical lines indicate the kurtosis () and skewness () values for a Gaussian distribution.
