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

Piotr Kokoszka*, Hong Miao, Alexander Petersen, Han Lin Shang

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    25 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

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