Efficient modelling and forecasting with range based volatility models and its application

This paper considers an alternative method for fitting CARR models using the 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. Extensive simulation study is carried out to demonstrate its superiority relative to two other competitors: the linear estimating functions (LEF) and the maximum likelihood (ML). Results show that the CEF method is more efficient than the LEF and ML methods when the error distribution is mis-specified. Applying a real data set from financial market, we illustrate the applicability of the CEF method in practice and report some reliable forecast values for minimizing the risk in decision making process.

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