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On the goodness-of-fits of the generalized lambda distribution on high-frequency stock index returns

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  • Peterson Owusu Junior
  • Nagaratnam Jeyasreedharan
  • Imhotep Paul Alagidede

Abstract

In this paper, we investigate the goodness-of-fit of the flexible four-parameter generalized Lambda Distribution (GLD) for high-frequency 5-min returns sampled from the DJI30 Index. Applying Moment Matching (MM) and Maximum Likelihood Estimation (MLE) techniques, we highlight the significance of the higher-order parameters of the GLD distribution to depict the asymmetric and fat-tailed behaviour observed in high-frequency returns data. We also show and explain why the MLE consistently outperforms the MM; especially in the presence of “outliers”. Finally, we use lambda-space scatterplots to introduce, clarify and discuss additional stylized facts of high-frequency index returns not found in the extant high-frequency literature.

Suggested Citation

  • Peterson Owusu Junior & Nagaratnam Jeyasreedharan & Imhotep Paul Alagidede, 2022. "On the goodness-of-fits of the generalized lambda distribution on high-frequency stock index returns," Cogent Economics & Finance, Taylor & Francis Journals, vol. 10(1), pages 2095764-209, December.
    • Handle: RePEc:taf:oaefxx:v:10:y:2022:i:1:p:2095764
    DOI: 10.1080/23322039.2022.2095764
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