Kernel smoothing is a moving average smoother that uses a weight function, also referred to as kernel, to average the observations. The kernel smoothing function is estimated by

\[\hat f_t = \sum_{i=1}^b w_i(t)x_i\text{,}\]


\[w_i(t) = \frac{K \left(\frac{t-i}{b}\right)}{\sum_{i=1}^bK\left(\frac{t-i}{b}\right)}\]

are the weights and \(K(\cdot)\) is a kernel function, typically the normal kernel, \(K(z) = \frac{1}{\sqrt{2 \pi}}\exp(-z^2/2)\). The wider the bandwidth, \(b\), the smoother the result. In R we apply the ksmooth() function for kernel smoothing.


dt <- index(
y <- coredata(
plot(dt, y, type = 'l', 
     col = 'gray', xlab = "", ylab = "",
     main = 'Kernel Smoothing')

lines(ksmooth(dt, y, "normal", bandwidth = 1), 
      col = 'red', type = 'l')
lines(ksmooth(dt, y, "normal", bandwidth = 25), 
      col = 'green', type = 'l')

       legend = c('b=1','b=25'), 
       col = c('red', 'green'),
       lty = 1,
       cex = 0.6)