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Parameter estimation for ARCH(1) models based on Kalman filter
2014
Applied Mathematical Sciences
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In this work, we propose a new estimate algorithm for the parameters of a ARCH(1) model without any assumptions about initial values which are important in QMLE method. This algorithm turns out to be very reliable in estimating the true parameter values of a given model. It combines maximum likelihood method, Kalman filter algorithm and the SPSA method. Simulation results demonstrate that the algorithm is viable and promising. Note that this work is not a special case of our paper on the GARCH
doi:10.12988/ams.2014.43164
fatcat:ve2ie7dqzjchvllx7gbhk45f6q