Darbe siekiama aprašyti periodinį ilgos atminties finansinių laiko eilučių elgesį. Remiantis anksčiau sukurtais modeliais, siūlomas h-faktorių Gegenbauer-LARCH modelis, kuris į LARCH tipo proceso sąlyginės dispersijos lygtį įtraukia apibendrintą ilgos atminties filtrą, paremtą Gegenbauer polinomais. Darbe pateikiama anksčiau sukurtų modelių, skirtų finansinių aktyvų grąžų kintamumo modeliavimui, apžvalga. Remiantis ankstesnėmis idėjomis ir darbais, sukonstruojamas naujas Gegenbauer-LARCH modelis, kuriam tikrinama kovariacijos stacionarumo sąlyga. Pateikiamos modeliuotos h-faktorių Gegenbauer-LARCH proceso trajektorijos. Sukurtas modelis taikomas realiems Euro-Dolerio valiutų kurso duomenims. Identifikuotas modelio parametrai vertinami LUDE algoritmu, kuris maksimizuoja didžiausio tikėtinumo funkciją. Atliekama modelio adekvatumo analizė. Darbo pabaigoje pateikiamos išvados ir rekomendacijos. / On the ground of previous works and ideas a new class of models which describe long memory periodic behaviour in a time varying volatility of financial returns is introduced. Generalised periodic long-memory filters, based on Gegenbauer polynomials, are included into volatility equation of LARCH model and capture long memory periodic behaviour of the data. Thus, a new type of model called h-factor Gegenbauer-LARCH is presented. Moreover, a covariance stationarity condition is checked for one factor Gegenbauer-LARCH model. Also, generated processes are demonstrated. Furthermore, h-factor Gegenbauer-LARCH model is applied to Euro-Dollar hourly exchange rate returns. Identified model is estimated by means of LUDE algorithm which maximizes maximum likelihood function. The adequasy of the model is checked by reviewing residuals behaviour. Concerning empirical results the following conclusion is drawn: • Although model captures specific characteristics of the data such as slowly decaying periodic behaviour of autocorrelation function and pronounced peaks in periodogram but residuals analysis shows that model should be improved. Bordignon, Caporin, Lisi suggest that all possible frequencies were included to the model because higher frequencies might not be obvious from autocorrelation function or periodogram. However, we face computer capability problem. As a matter of fact, we cannot estimate a more complex model. Inclusion of autoregresive coefficients into the model did not... [to full text]
| Identifer | oai:union.ndltd.org:LABT_ETD/oai:elaba.lt:LT-eLABa-0001:E.02~2008~D_20090908_201757-95006 |
| Date | 08 September 2009 |
| Creators | Osipavičiūtė, Aušra |
| Contributors | Leipus, Remigijus, Vilnius University |
| Publisher | Lithuanian Academic Libraries Network (LABT), Vilnius University |
| Source Sets | Lithuanian ETD submission system |
| Language | Lithuanian |
| Detected Language | English |
| Type | Master thesis |
| Format | application/pdf |
| Source | http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20090908_201757-95006 |
| Rights | Unrestricted |
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