TY - JOUR
T1 - Forecasting of density functions with an application to cross-sectional and intraday returns
AU - Kokoszka, Piotr
AU - Miao, Hong
AU - Petersen, Alexander
AU - Shang, Han Lin
N1 - Publisher Copyright:
© 2019 International Institute of Forecasters
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Compositional data analysis
KW - Constrained functional time series
KW - Density function forecasting
KW - Log quantile density transformation
UR - http://www.scopus.com/inward/record.url?scp=85069707124&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2019.05.007
DO - 10.1016/j.ijforecast.2019.05.007
M3 - Article
SN - 0169-2070
VL - 35
SP - 1304
EP - 1317
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 4
ER -