## Abstract The Xinanjiang model, which is a conceptual rainfallโrunoff model and has been successfully and widely applied in humid and semiโhumid regions in China, is coupled by the physically based kinematic wave method based on a digital drainage network. The kinematic wave Xinanjiang model (KWX
Forecasting and trading strategies based on a price trend model
โ Scribed by Josephine W. C. Kwan; K. Lam; Mike K. P. So; Philip L. H. Yu
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 164 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0277-6693
No coin nor oath required. For personal study only.
โฆ Synopsis
In this paper, we consider the price trend model in which it is assumed that the time series of a security's prices contain a stochastic trend component which remains constant on each of a sequence of time intervals, with each interval having random duration. A quasi-maximum likelihood method is used to estimate the model parameters. Optimal one-step-ahead forecasts of returns are derived. The trading rule based on these forecasts is constructed and is found to bear similarity to a popular trading rule based on moving averages. When applying the methods to forecast the returns of the Hang Seng Index Futures in Hong Kong, we ยฎnd that the performance of the newly developed trading rule is satisfactory.
๐ SIMILAR VOLUMES
We present here a mathematical formula for the directional distribution of migratory birds if they use a vector navigation/clock-and-compass strategy to "nd their winter quarters. It is based on mathematical expectation theory and shows that a simple parabola can describe the expected geographical s
This paper discusses the practical application of identiรฟcation in cointegrated systems. It will argue that in a common realistic modelling situation of a limited data set and the theory requirements of a fairly rich model, the techniques proposed in the existing literature are almost impossible to
This short communication comments on a series of papers using artificial neural networks published by Guessasma and co-workers in structures journals. The issues discussed include the size of the database for training a neural network, database enlargement for training a neural network, and extrapol