An Exponentially Weighted Moving Average $($EWMA$)$ model is commonly used for estimating volatility. The EWMA model updates the conditional variance as:

$\backslash$sigma $^2\_$t $=\backslash$lambda $*\backslash$sigma $^2\_\{$t$-1\}+(1-\backslash$lambda $)*\backslash$epsi $^2\_\{$t$-1\}$

Where $\backslash$lambda is the smoothing parameter $(0<\backslash$lambda $<1),\backslash$sigma $^2\_$t is the conditional variance at time t$,$ and $\backslash$epsi $^2\_\{$t$-1\}$ is the squared return at time t$-1.$ What happens to the model's sensitivity to recent returns when $\backslash$lambda is increased?

a$.$ Sensitivity to recent returns increases.

b$.$ Sensitivity to recent returns decreases.

c$.$ Sensitivity to recent returns remains constant.

d$.$ The effect of $\backslash$lambda on sensitivity to recent returns cannot be determined.