Expected shortfall estimation using kernel machines

다국어 초록 (Multilingual Abstract)

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR),restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and sup-port vector expectile regression (SVER). These kernel machines have obvious advan-tages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underly-ing probability distribution of errors. Through numerical studies on two artificial and two real data sets we show their effectiveness on the estimation performance at various confidence levels.

참고문헌 (Reference)

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