Explanation

The correct answer is AR(2) because:

• Partial Autocorrelation Function (PACF) measures the correlation between observations at different lags while controlling for the effects of intermediate lags
• In AR (Autoregressive) processes, the PACF cuts off after the order of the AR process
• When the PACF plot shows significant spikes at lag 1 and lag 2, but cuts off (becomes insignificant) after lag 2, this indicates an AR(2) process
• An AR(2) model would be: $Y_t = \phi_1 Y_{t-1} + \phi_2 Y_{t-2} + \epsilon_t$
• This behavior contrasts with MA (Moving Average) processes, where the ACF (Autocorrelation Function) cuts off after the order of the MA process

Therefore, the PACF cutting off after the second lag clearly suggests that an AR(2) model is the most appropriate regression approach for this security's time series data.