| Title |
Mixed-stable models: an application to high-frequency financial data / |
| Authors |
Belovas, Igoris ; Sakalauskas, Leonidas ; Starikovičius, Vadimas ; Sun, Edward W |
| DOI |
10.3390/e23060739 |
| Full Text |
|
| Is Part of |
Entropy.. Basel : MDPI. 2021, vol. 23, iss. 6, art. no. 739, p. [1-12].. ISSN 1099-4300. eISSN 1099-4300 |
| Keywords [eng] |
mixed-stable models ; high-frequency data ; stock index returns |
| Abstract [eng] |
The paper extends the study of applying the mixed-stable models to the analysis of large sets of high-frequency financial data. The empirical data under review are the German DAX stock index yearly log-returns series. Mixed-stable models for 29 DAX companies are constructed employing efficient parallel algorithms for the processing of long-term data series. The adequacy of the modeling is verified with the empirical characteristic function goodness-of-fit test. We propose the smart- ∆ method for the calculation of the α-stable probability density function. We study the impact of the accuracy of the computation of the probability density function and the accuracy of ML-optimization on the results of the modeling and processing time. The obtained mixed-stable parameter estimates can be used for the construction of the optimal asset portfolio. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
