  This paper considers an alternative method for fitting CARR models using
combined estimating functions (CEF) by showing its usefulness in applications
in economics and quantitative finance. The associated information matrix for
corresponding new estimates is derived to calculate the standard errors. A
simulation study is carried out to demonstrate its superiority relative to
other two competitors: linear estimating functions (LEF) and the maximum
likelihood (ML). Results show that CEF estimates are more efficient than LEF
and ML estimates when the error distribution is mis-specified. Taking a real
data set from financial economics, we illustrate the usefulness and
applicability of the CEF method in practice and report reliable forecast values
to minimize the risk in the decision making process.
