In this paper, by considering a model-based approach for conditional moment estimation, a nonparametric test was performed to study the long-memory property of higher moments. We considered the daily returns of the stocks included in the S&P500 index in the last ten years (for the period running from the 1st of January 2011 to the 1st of January 2021). We found that mean and skewness were characterized by short memory, while variance and shape had long memory. These results have deep implications in terms of asset allocation, option pricing and market efficiency evaluation.
Giacalone, M., Panarello, D. (2022). A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments. MATHEMATICS, 10(5), 1-21 [10.3390/math10050707].
A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments
Giacalone, Massimiliano;Panarello, Demetrio
2022
Abstract
In this paper, by considering a model-based approach for conditional moment estimation, a nonparametric test was performed to study the long-memory property of higher moments. We considered the daily returns of the stocks included in the S&P500 index in the last ten years (for the period running from the 1st of January 2011 to the 1st of January 2021). We found that mean and skewness were characterized by short memory, while variance and shape had long memory. These results have deep implications in terms of asset allocation, option pricing and market efficiency evaluation.| File | Dimensione | Formato | |
|---|---|---|---|
|
28.pdf
accesso aperto
Tipo:
Versione (PDF) editoriale
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione
414.97 kB
Formato
Adobe PDF
|
414.97 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
