Augmented Dickey Fuller Test for Trading Beginners: A Trading Guide
The Augmented Dickey Fuller (ADF) test is used in finance and economics to assess whether a time series is stationary. It helps discover patterns and shocks in data series, making it valuable in predicting and trading. We’ll explain the ADF test for trading newbies in this post.
Define stationarity.
Stationarity must be understood before discussing the ADF test. Stationary time series have constant mean, variance, and autocovariance. A non-stationary time series shows trends, cycles, or other patterns throughout time.
The ADF Test
The ADF test determines time series stationary or non-stationary. It extends the Dickey Fuller test, which examines the null hypothesis that an autoregressive model has a unit root. The ADF test accounts for serial correlation and endogeneity by adding lagged dependent variable differences.
Non-stationarity is the null hypothesis of the ADF test, which assumes a unit root. Alternative hypothesis: time series is stationary. The ADF test generates the test statistic and compares it to critical values to assess series stationarity using statistical methods.
Results Interpretation
The ADF test compares the test statistic to crucial values at 1%, 5%, and 10% confidence levels. When the test statistic is below the crucial value, we reject the null hypothesis and conclude the time series is stationary. However, if the test statistic exceeds the crucial threshold, we cannot reject the null hypothesis, suggesting non-stationarity.
Use in Trading
The ADF test is used in trading for several objectives. It helps traders determine whether a time series has a long-term trend or random walk. Mean-reversion algorithms may be used on stationary time series to predict their mean. If a time series is non-stationary, traders may follow trends and capitalize on large price changes.
ADF testing is also useful in pairs trading, when two linked stocks are traded concurrently. To detect whether price spreads between two securities are stationary or mean-reverting, traders employ the ADF test. If spreads are constant, traders may benefit from mean deviations.
Sources and References:
1. ADF Test – Wikipedia: https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test
2. Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 55(2), 251-276.