Autocorrelation (the property of an autoregressive time series) is one of the most common effects in financial time series, and also one of the biggest innovations to come out of time series analysis in the last 100 years. It describes the phenomena of future values being dependent on current and past values as well as new information. Autocorrelation also leads to fat tails and tail risk, which can sink your algorithm if you assume underlying distributions are not autoregressive. Because there is so much autocorrelation in finance we should understand how to effectively model it. This notebook covers the idea behind autocorrelation, describes properties of autoregressive time series, and shows how to fit a model to the data.
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