13 research outputs found
On the choice of a genetic algorithm for estimating GARCH models
The GARCH models have been found difficult to build by classical
methods, and several other approaches have been proposed in literature,
including metaheuristic and evolutionary ones. In the present paper we employ
Genetic Algorithms to estimate the parameters of GARCH(1,1) models,
assuming a fixed computational time (measured in number of fitness function
evaluations) that is variously allocated in number of generations, number of
algorithm restarts and number of chromosomes in the population, in order to
gain some indications about the impact of each of these factors on the estimates.
Results from this simulation study show that if the main purpose is to
reach a high quality solution with no time restrictions the algorithm should
not be restarted and an average population size is recommended, while if the
interest is focused on driving rapidly to a satisfactory solution then for moderate
population sizes it is convenient to restart the algorithm, even if this
means to have a small number of generations