Cross Correlation Analysis: Analysing Currency Pairs in Python

When working with a time series, one important thing we wish to determine is whether one series “causes” changes in another. In other words, is there a strong correlation between a time series and another given a number of lags? The way we can detect this is through measuring cross correlation.

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Kalman Filter: Modelling Time Series Shocks with KFAS in R

We have already seen how time series models such as ARIMA can be used to make time series forecasts. While these models can prove to have high degrees of accuracy, they have one major shortcoming – they do not account for “shocks”, or sudden changes in a time series. Let’s see how we can potentially alleviate this problem using a model known as the Kalman Filter.

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Cross-Correlation of Currency Pairs In R (ccf)

When working with a time series, one important thing we wish to determine is whether one series “causes” changes in another. In other words, is there a strong correlation between a time series and another given a number of lags? The way we can detect this is through measuring cross-correlation.

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Chow Test For Structural Breaks in Time Series

A Chow test is designed to determine whether a structural break in a time series exists. That is to say, a sharp change in trend in a time series that merits further study. For instance, a structural break in one series can give useful clues as to whether such a change is being propagated across other variables – assuming that there is a significant correlation between them under normal circumstances.

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Serial Correlation: Durbin-Watson and Cochrane-Orcutt Remedy

Serial correlation (also known as autocorrelation) is a violation of the Ordinary Least Squares assumption that all observations of the error term in a dataset are uncorrelated. In a model with serial correlation, the current value of the error term is a function of the one immediately previous to it:

  et = ρe(t-1) + ut
   
  where e = error term of equation in question; ρ = first-order autocorrelation coefficient; u = classical (not serially correlated error term)

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