Multiariate Wavelet-based sahpe preserving estimation for dependant observation

Antonio Cosma (), Olivier Scaillet and Rainer von Sachs ()

FAME Research Paper Series from International Center for Financial Asset Management and Engineering

Abstract: We present a new approach on shape preserving estimation of probability distribution and density functions using wavelet methodology for multivariate dependent data. Our estimators preserve shape constraints such as monotonicity, positivity and integration to one, and allow for low spatial regularity of the underlying functions. As important application, we discuss conditional quantile estimation for financial time series data. We show that our methodology can be easily implemented with B-splines, and performs well in a finite sample situation, through Monte Carlo simulations.

Keywords: Conditional quantile; time series; shape preserving wavelet estimation; B-splines; multivariate process (search for similar items in EconPapers)
JEL-codes: C14 C15 C32 (search for similar items in EconPapers)
Date: 2005-05
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (8)

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