- We will send in 10–14 business days.
/images/books/13188034/9783639393927.jpg)
- Author: Philipp Jan Siegert
- Publisher: AV Akademikerverlag
- Year: 2012
- Pages: 92
- ISBN-10: 3639393929
- ISBN-13: 9783639393927
- Format: 15.2 x 22.9 x 0.6 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
Revision with unchanged content. Most banks and the recently upcoming hedge fund industry rely to a different extent on technical trading rules and technical analysis. The fact that these technical trading rules yield superior returns in practice raises several questions that will be examined in this book. First, one of the most crucial questions is in which assets technical trading rules perform extraordinarily well. This analysis is based on a risk-return approach with an assessment of the negative standard deviation of each asset as a risk indicator. Second, the statistical significance of technical trading is examined by using a simulation method known as bootstrap. Third, null models are simulated to answer the question to what extent autoregressive models and GARCH models are able to capture the dependencies in the future time series. Finally, a rule optimizer algorithm is developed to assess if any rule parameters yield superior returns over a wide range of assets.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 22d.00:41:51
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Philipp Jan Siegert
- Publisher: AV Akademikerverlag
- Year: 2012
- Pages: 92
- ISBN-10: 3639393929
- ISBN-13: 9783639393927
- Format: 15.2 x 22.9 x 0.6 cm, minkšti viršeliai
- Language: English English
Revision with unchanged content. Most banks and the recently upcoming hedge fund industry rely to a different extent on technical trading rules and technical analysis. The fact that these technical trading rules yield superior returns in practice raises several questions that will be examined in this book. First, one of the most crucial questions is in which assets technical trading rules perform extraordinarily well. This analysis is based on a risk-return approach with an assessment of the negative standard deviation of each asset as a risk indicator. Second, the statistical significance of technical trading is examined by using a simulation method known as bootstrap. Third, null models are simulated to answer the question to what extent autoregressive models and GARCH models are able to capture the dependencies in the future time series. Finally, a rule optimizer algorithm is developed to assess if any rule parameters yield superior returns over a wide range of assets.
Reviews