Loading...
Please wait, while we are loading the content...
Evolutionary Computation to Estimate Volatility
| Content Provider | Semantic Scholar |
|---|---|
| Author | Huamańı, Luis A. Navarro |
| Copyright Year | 2018 |
| Abstract | A performance evaluation study is implemented between the methods of Genetic Algorithms with Floating Point representation and some traditional optimization methods, in the task of estimating the parameters of a GARCH (1,1) Normal process, using artificial data obtained by simulation. The results show that the approximate solutions obtained by means of Genetic Algorithms present a better stability and precision with respect to the traditional optimization methods. The choice of the initial point in numerical optimization methods is not a critical condition in the use of Genetic Algorithms as a method to find the solution. Finally, Genetic Algorithm method is illustrated in the finding of the solution of the vector of parameters of the likelihood function of a GARCH (1,1) t-Student model, using data of rates of exchange returns of the Sol against to the Dollar. KeywordsGenetic Algorithms, Statistical Inference, GARCH Digital Object Identifier (DOI):http://dx.doi.org/10.18687/LACCEI2018.1.1.436 ISBN: 978-0-9993443-1-6 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.laccei.org/LACCEI2018-Lima/full_papers/FP436.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
