On the use of the Box-Cox transformation on conditional variance models

We investigate the effects of using the Box-Cox transformation on conditional variance specifications. By deriving its autocorrelation functions, we infer “rich” autocorrelation structures due to the existence of the specification parameter in this non-linear transformation. To illustrate transformation's effects on conditional variance models, we first generate its theoretical autocorrelation function and then investigate model's fit using real financial time-series data.