ADF tests for a unit root. Autocorrelation function of a unit root process does not make sense. For example let

$$y_{t+1}=y_t+\epsilon_{t+1}$$

Here $\epsilon_t$ is i.i.d white noise. Then the one period autocovariance is

$$Cov(y_{t+1},y_{t})=Cov(y_t+\epsilon_{t+1},y_{t})=Var(y_t)$$

For a unit root process $Var(y_t) \rightarrow \infty$ as $t\rightarrow \infty$ and this autocovariance is not well defined and the sample autocorrelations grow as the number of observations increases. If your data features a unit root you should not look at autocorrelations.

Note that prices are rather non-stationary because of a time trend. Here it would be equally wrong to look at autocorrelation functions. But you can solve most of these issues by taking differences. But all in all: only look at autocorrelations if your variable is stationary.

ADF tests for a unit root. Autocorrelation function of a unit root process does not make sense. For example let

$$y_{t+1}=y_t+\epsilon_{t+1}$$

Here $\epsilon_t$ is i.i.d white noise. Then the one period autocovariance is

$$Cov(y_{t+1},y_{t})=Cov(y_t+\epsilon_{t+1},y_{t})=Var(y_t)$$

For a unit root process $Var(y_t) \rightarrow \infty$ as $t\rightarrow \infty$. This autocovariance is hence not well defined and the sample autocorrelations grow as the sample length increases  . If your data features a unit root you should not look at autocorrelations.

Note that prices are rather non-stationary because of a time trend. Here it would be equally wrong to look at autocorrelation functions. But you can solve most of these issues by taking differences. But all in all: only look at autocorrelations if your variable is stationary.

ADF tests for a unit root. Autocorrelation function of a unit root process does not make sense because the process variance tends to infinity and autocorrelations tend to 1. Note that prices are rather non-stationary because of a time trend. Here it would be equally wrong to look at autocorrelation functions. But you can solve most of these issues by taking differences.

ADF tests for a unit root. Autocorrelation function of a unit root process does not make sense. For example let

$$y_{t+1}=y_t+\epsilon_{t+1}$$

Here $\epsilon_t$ is i.i.d white noise. Then the one period autocovariance is

$$Cov(y_{t+1},y_{t})=Cov(y_t+\epsilon_{t+1},y_{t})=Var(y_t)$$

For a unit root process $Var(y_t) \rightarrow \infty$ as $t\rightarrow \infty$ and this autocovariance is not well defined and the sample autocorrelations grow as the number of observations increases. If your data features a unit root you should not look at autocorrelations. 

Note that prices are rather non-stationary because of a time trend. Here it would be equally wrong to look at autocorrelation functions. But you can solve most of these issues by taking differences. But all in all: only look at autocorrelations if your variable is stationary.