4
Exploring Financial Time Series Data
In the previous chapters, we learned how to preprocess and visually explore financial time series data. This time, we will use algorithms and/or statistical tests to automatically identify potential issues (like outliers) and analyze the data for the existence of trends or other patterns (for example, mean-reversion).
We will also dive deeper into the stylized facts of asset returns. Together with outlier detection, those recipes are particularly important when working with financial data. When we want to build models/strategies based on asset prices, we have to make sure that they can accurately capture the dynamics of the returns.
Having said that, most of the techniques described in this chapter are ...
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