Forecasting of density functions with an application to cross-sectional and intraday returns

Piotr Kokoszka, Hong Miao, Alexander Petersen, Hanlin Shang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper is concerned with the forecasting of probability density functions. Density functions are nonnegative and have a constrained integral, and thus do not constitute a vector space. The implementation of established functional time series forecasting methods for such nonlinear data is therefore problematic. Two new methods are developed and compared to two existing methods. The comparison is based on the densities derived from cross-sectional and intraday returns. For such data, one of our new approaches is shown to dominate the existing methods, while the other is comparable to one of the existing approaches.
Original languageEnglish
Pages (from-to)1304-1317
Number of pages14
JournalInternational Journal of Forecasting
Volume35
Issue number4
DOIs
Publication statusPublished - 1 Oct 2019
Externally publishedYes

Keywords

  • Compositional data analysis
  • Constrained functional time series
  • Density function forecasting
  • Log quantile density transformation

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