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This paper considers an alternative method for fitting CARR models usingcombined estimating functions (CEF) by showing its usefulness in applicationsin economics and quantitative finance. The associated information matrix forcorresponding new estimates is derived to calculate the standard errors. Asimulation study is carried out to demonstrate its superiority relative toother two competitors: linear estimating functions (LEF) and the maximumlikelihood (ML). Results show that CEF estimates are more efficient than LEFand ML estimates when the error distribution is mis-specified. Taking a realdata set from financial economics, we illustrate the usefulness andapplicability of the CEF method in practice and report reliable forecast valuesto minimize the risk in the decision making process.

Efficient Modelling & Forecasting with range based volatility models and application