Robust Kalman filter for high-frequency financial data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10382870" target="_blank" >RIV/00216208:11320/18:10382870 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31140/18:00052450
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-00350-0_4" target="_blank" >http://dx.doi.org/10.1007/978-3-030-00350-0_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-00350-0_4" target="_blank" >10.1007/978-3-030-00350-0_4</a>

Result language
angličtina
Original language name
Robust Kalman filter for high-frequency financial data
Original language description
The robust recursive algorithm for the parameter estimation and the volatility prediction in GARCH models is proposed. The suggested technique applies the principles of the robustified Kalman filtering. It seems to be useful for (high-frequency) financial time series contaminated by additive outliers. In particular, it can be effective in the risk control and regulation when the prediction of volatility is the main concern since it is capable of distinguishing and correcting outlaid bursts of volatility. This conclusion is confirmed by simulations and real data examples presented herein.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Communications in Computer and Information Science
ISBN
978-3-030-00349-4
ISSN
1865-0929
e-ISSN
neuvedeno
Number of pages
13
Pages from-to
42-54
Publisher name
SPRINGER
Place of publication
Switzerland
Event location
Medellín
Event date
Oct 17, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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