Autocorrelation and Serial Dependence

Autocorrelation measures the linear relationship between a time series and its delayed values. It quantifies how a past observation affects a future observation. Positive autocorrelation indicates per

Autocorrelation measures the linear relationship between a time series and its lagged versions. It quantifies how much a past value influences a future value. This statistical property informs mean re

The Partial Autocorrelation Function (PACF) measures the direct link between an observation and a past version of itself. It removes the impact of intermediate observations. This isolates the direct e

Negative autocorrelation is the statistical mark of mean reversion. It describes a tendency for a financial series to reverse direction. A positive return today suggests a negative return tomorrow. A

The Ljung-Box test determines if a time series shows serial correlation. It analyzes residuals from a financial model. Strong serial correlation in residuals indicates model weakness. It suggests unca

The Durbin-Watson statistic identifies autocorrelation in regression residuals. Autocorrelation happens when residual errors connect with previous errors. This breaks a main assumption of ordinary lea

Autocorrelation measures a security's price relationship with its past prices. This relationship changes across different timeframes. A stock might show positive autocorrelation on daily charts but ne

Financial markets show predictable patterns. These patterns repeat over specific timeframes. Seasonal autocorrelation describes this. It refers to the correlation of a time series with its past values

Autocorrelation measures the linear relationship between a variable's current value and its past values. In mean reversion, positive autocorrelation at short lags indicates persistence. Negative autoc

Autocorrelation measures a security's price relationship with its past values. Positive autocorrelation means past price movements predict future movements in the same direction. Negative autocorrelat