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._