  We investigate the dynamic behavior of the stationary random process defined
by a central complex Wishart (CW) matrix ${\bf{W}}(t)$ as it varies along a
certain dimension $t$. We characterize the second-order joint cdf of the
largest eigenvalue, and the second-order joint cdf of the smallest eigenvalue
of this matrix. We show that both cdfs can be expressed in exact closed-form in
terms of a finite number of well-known special functions in the context of
communication theory. As a direct application, we investigate the dynamic
behavior of the parallel channels associated with multiple-input
multiple-output (MIMO) systems in the presence of Rayleigh fading. Studying the
complex random matrix that defines the MIMO channel, we characterize the
second-order joint cdf of the signal-to-noise ratio (SNR) for the best and
worst channels. We use these results to study the rate of change of MIMO
parallel channels, using different performance metrics. For a given value of
the MIMO channel correlation coefficient, we observe how the SNR associated
with the best parallel channel changes slower than the SNR of the worst
channel. This different dynamic behavior is much more appreciable when the
number of transmit ($N_T$) and receive ($N_R$) antennas is similar. However, as
$N_T$ is increased while keeping $N_R$ fixed, we see how the best and worst
channels tend to have a similar rate of change.
