Kernel Ridge Regression with Odd Kernels in Financial Time Series Prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00181847" target="_blank" >RIV/68407700:21230/11:00181847 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Kernel Ridge Regression with Odd Kernels in Financial Time Series Prediction

Popis výsledku v původním jazyce

A new type of kernels called odd kernels is proposed to model dependencies by odd functions. Odd kernels could be simply constructed from the most of traditional kernels. Although usability of odd kernels is wider, we deals with their application to thefinancial time series prediction which was our primary motivation for construction of odd kernels. Applicability of odd kernels is demonstrated on high frequency time series from four real futures contracts. Kernel ridge regression with Gaussian kernel and its odd counterpart is applied to the prediction of short-time price differences. Results of conducted experiments show that frequently used Gaussian kernel can be outperformed by odd Gaussian kernel. Moreover, the results support the hypothesis of price differences symmetry.

Název v anglickém jazyce

Kernel Ridge Regression with Odd Kernels in Financial Time Series Prediction

Popis výsledku anglicky

A new type of kernels called odd kernels is proposed to model dependencies by odd functions. Odd kernels could be simply constructed from the most of traditional kernels. Although usability of odd kernels is wider, we deals with their application to thefinancial time series prediction which was our primary motivation for construction of odd kernels. Applicability of odd kernels is demonstrated on high frequency time series from four real futures contracts. Kernel ridge regression with Gaussian kernel and its odd counterpart is applied to the prediction of short-time price differences. Results of conducted experiments show that frequently used Gaussian kernel can be outperformed by odd Gaussian kernel. Moreover, the results support the hypothesis of price differences symmetry.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

POSTER 2011 - 15th International Student Conference on Electrical Engineering

ISBN

978-80-01-04806-1

ISSN

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e-ISSN

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Počet stran výsledku

1

Strana od-do

1

Název nakladatele

ČVUT, Fakulta elektrotechnická

Místo vydání

Praha

Místo konání akce

Prague

Datum konání akce

12. 5. 2011

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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