Modeling dynamic diurnal patterns in high frequency financial data

A spline-DCS model is developed to forecast the conditional distribution of high-frequency financial data with periodic behavior. The dynamic cubic spline of Harvey and Koopman (1993) is applied to allow diurnal patterns to evolve stochastically over time. An empirical application illustrates the practicality and impressive predictive performance of the model.

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Year of publication:	2013-06-04
Authors:	Ito, Ryoko
Institutions:	Faculty of Economics, University of Cambridge
Subject:	outlier \| robustness \| score \| calendar effect \| spline \| trade volume

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Series:	Cambridge Working Papers in Economics.
Type of publication:	Book / Working Paper
Classification:	C22 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010761905