50. A risk analyst is tasked with calculating the variance of returns for a stock index for the upcoming trading day. To accomplish this, the analyst utilizes a GARCH (1,1) model, which stands for Generalized Autoregressive Conditional Heteroskedasticity, and is essential for predicting future volatility based on past data. The model is represented by the following equation: $o_n = \alpha r_{n-1} + \beta o_{n-1} + v_i,$ In this context, $o_n$ represents the index variance on day $n$, $r_{n-1}$ represents the return on day $n-1$, and $o_{n-1}$ represents the volatility on day $n-1$. Given that the expected value of the return remains constant over time, identify the combination of values for $\alpha$ and $\beta$ that would ensure a stable GARCH (1,1) process.