Weighted Kernel Regression for Predicting Changing Dependencies

  • Steven Busuttil
  • Yuri Kalnishkan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4701)


Consider the online regression problem where the dependence of the outcome y t on the signal x t changes with time. Standard regression techniques, like Ridge Regression, do not perform well in tasks of this type. We propose two methods to handle this problem: WeCKAAR, a simple modification of an existing regression technique, and KAARCh, an application of the Aggregating Algorithm. Empirical results on artificial data show that in this setting, KAARCh is superior to WeCKAAR and standard regression techniques. On options implied volatility data, the performance of both KAARCh and WeCKAAR is comparable to that of the proprietary technique currently being used at the Russian Trading System Stock Exchange (RTSSE).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Steven Busuttil
    • 1
  • Yuri Kalnishkan
    • 1
  1. 1.Computer Learning Research Centre and Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, TW20 0EXUnited Kingdom

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