Parsimonious principle of GARCH models: a Monte-Carlo approach
Purpose – This paper is intended to test the robustness of the fitness of nested GARCH models. Design/methodology/approach – Both Monte-Carlo simulation data and real-world data are used in the paper. Likelihood-family tests are used to test in-sample fitness, while mean-squared prediction error is employed for out-sample prediction tests. Findings – The paper finds that, generally, the parsimonious principle is found to work well for both criteria. However, it is found that conflict exists between the two criteria: in-sample likelihood-family tests pay more attention to conditional distributions or are more sensitive to fat tail effects; while the out-sample criteria focus more on the accuracy of parameter estimation. Originality/value – The paper shows that complexity does not necessarily mean good fitness; sometimes, the simpler model can fit better, especially for real-world data.
Financial data processing, Monte Carlo simulation, Research methods
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