𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Evaluating interval forecasts of high-frequency financial data

✍ Scribed by Michael P. Clements; Nick Taylor

Publisher
John Wiley and Sons
Year
2003
Tongue
English
Weight
228 KB
Volume
18
Category
Article
ISSN
0883-7252

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression‐based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated. Copyright © 2003 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

Forecasting high-frequency financial dat
Forecasting high-frequency financial data with the ARFIMA–ARCH model
✍ Michael A. Hauser; Robert M. Kunst 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 168 KB

## Abstract Financial data series are often described as exhibiting two non‐standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the

Evaluating forecasting performance for i
Evaluating forecasting performance for interval data
✍ Hui-Li Hsu; Berlin Wu 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 469 KB

## Interval time series Mean squared error of interval Mean relative interval error Mean ratio of exclusive-or a b s t r a c t From the overlapping parts and the non-overlapping parts of the actual intervals and the forecast intervals, it should be defined a criterion which is more efficient to eva

Forecasting using high-frequency data: a
Forecasting using high-frequency data: a comparison of asymmetric financial duration models
✍ Qi Zhang; Charlie X Cai; Kevin Keasey 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 123 KB

## Abstract The first purpose of this paper is to assess the short‐run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomial–Autoregressive Conditional Duration (ACM‐ACD) model is better than the Asymmetric Autore

Predicting financial volatility: High-fr
Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility
✍ Martin Martens; Jason Zein 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 170 KB

## Abstract Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historical __daily__ returns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and l