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Multiariate Wavelet-based sahpe preserving estimation for dependant observation

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Author Info
Antonio Cosma ()
Olivier Scaillet ()
Rainer von Sachs ()

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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.

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File URL: http://www.swissfinanceinstitute.ch/rp144.pdf
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Publisher Info
Paper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp144.

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Date of creation: May 2005
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Handle: RePEc:fam:rpseri:rp144

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Related research
Keywords: Conditional quantile; time series; shape preserving wavelet estimation; B-splines; multivariate process;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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This page was last updated on 2009-11-19.

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