## 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
Dynamical structures of high-frequency financial data
✍ Scribed by Kyungsik Kim; Seong-Min Yoon; SooYong Kim; Ki-Ho Chang; Yup Kim; Sang Hoon Kang
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 156 KB
- Volume
- 376
- Category
- Article
- ISSN
- 0378-4371
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
## 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
## Abstract Recent research investigating the properties of high‐frequency financial data has suggested that the stochastic nonlinearity widely present in such data may be characterized by heterogeneous components in conditional volatility, and nonlinear dependence of threshold autoregressive form
## 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