A quantitative risk analyst at a large financial institution is reviewing the existing model for estimating expected credit loss (ECL) reserves. Upon thoroughly examining the model, the analyst discovers that two key macroeconomic variables, MEV1 & MEV2, need an updated forecast. Before deciding which time series model to apply, the analyst uses statistical software to graph the autocorrelation function (ACF) and partial autocorrelation function (PACF) for each macroeconomic variable and generates the following graphs:

MEV1

[Graph showing ACF and PACF for MEV1 — ACF decays gradually, PACF cuts off after lag 1]

MEV2

[Graph showing ACF and PACF for MEV2 — ACF oscillates between positive and negative values, PACF spikes at lag 1 then drops to zero]

Based on the graphs above, and supposing that the analyst chose to estimate an AR(1) model, what are the most likely values of the AR parameter (φ) in each case?