A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/5/707 |
| _version_ | 1827651149135609856 |
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| author | Massimiliano Giacalone Demetrio Panarello |
| author_facet | Massimiliano Giacalone Demetrio Panarello |
| author_sort | Massimiliano Giacalone |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:30:47Z |
| format | Article |
| id | doaj.art-14d155c63f984c37a4c11e367b029100 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T20:30:47Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-14d155c63f984c37a4c11e367b0291002023-11-23T23:22:26ZengMDPI AGMathematics2227-73902022-02-0110570710.3390/math10050707A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher MomentsMassimiliano Giacalone0Demetrio Panarello1Department of Economics and Statistics, University of Naples “Federico II”, 80126 Naples, ItalyDepartment of Statistical Sciences “Paolo Fortunati”, University of Bologna, 40126 Bologna, ItalyIn 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.https://www.mdpi.com/2227-7390/10/5/707generalized autoregressive scoreskewness and shapenonparametric testself-similaritylong-range dependencefinancial market |
| spellingShingle | Massimiliano Giacalone Demetrio Panarello A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments Mathematics generalized autoregressive score skewness and shape nonparametric test self-similarity long-range dependence financial market |
| title | A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments |
| title_full | A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments |
| title_fullStr | A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments |
| title_full_unstemmed | A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments |
| title_short | A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments |
| title_sort | nonparametric approach for testing long memory in stock returns higher moments |
| topic | generalized autoregressive score skewness and shape nonparametric test self-similarity long-range dependence financial market |
| url | https://www.mdpi.com/2227-7390/10/5/707 |
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