Understanding Autoregressive Model Stationarity

Consider a time series dataset exhibiting autoregressive behavior defined by the model:

$$Y_t = \phi_1 Y_{t-1} + \phi_2 Y_{t-2} + \varepsilon_t$$

where $Y_t$ is the value of the time series at time $t$, $\phi_1$ and $\phi_2$ are autoregressive coefficients, and $\varepsilon_t$ is a white noise error term. Suppose the estimated parameters from this model are $\phi_1 = 0.6$ and $\phi_2 = 0.2$. Based on this model, which of the following statements correctly describes the behavior of the time series?