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Most Helpful Customer Reviews
33 of 39 people found the following review helpful:
5.0 out of 5 stars
More Than An Introduction,
By A Customer
This review is from: An Introduction to High-Frequency Finance (Hardcover)
This one of the few books on high frequency finance is a most welcome to the literature. The book is useful not only for people who are new to the subject but also for researchers in the field since it is a most uniform treatment of many topics. From adaptive data cleaning (chapter 4) to intraday and weekly seasonality (chapter 6) and real time trading models (chapter 11), it covers a broad range of topics specific to high frequency financial time series analysis. Chapters on volatility modeling (Chapter 8), forecasting (chapter 9) and correlation and multivariate risk (chapter 10) are enlightening especially for risk exposure analysis and risk management purposes. Finally, the the extensive bibliography is a precious source for those who would like to explore certain topics in detail. I highly recommend it for practitioners as well as researchers in the field.
3 of 3 people found the following review helpful:
5.0 out of 5 stars
Definitely mid-frequency in this day and age,
Amazon Verified Purchase(What's this?)
This review is from: An Introduction to High-Frequency Finance (Hardcover)
This book doesn't deal with true high-frequency trading, where it is more about execution than anything else. The book IS ten years old when I write this, so high frequency trading has taken on a different meaning, so no false advert here.
That said, it is a great treatment of the practical issues of handling large, heterogeneous financial data sets and their statistics. I haven't seen their methodology and framework anywhere else, although there are some really good treatments of irregularly spaced financial data (Hautsch, Engle). The authors are prolific in this area, in particular, the use of tick data to build better volatility models and the use of seasonality (business time scale) and stochastic time (see intrinsic time). They also present a good way to use higher frequency homogeneous data to effectively filter historical volatility computations that makes them more robust when the data is interpolated or sparse. The best part is that they bring everything together for use in multivariate cases and for forecasting/trading. Overall, this is a great book, that doesn't have many peers (if any). I can't recommend it enough. Minor downsides: (1) I also agree with the other reviewers on the notation, although it doesn't bother me that much personally. (2) Would be nice to see some type of flowchart for an implementation of the methods in Ch. 6 and later, like they did in Ch. 4. (3) No explicit mention of duration and/or point processes, although it is implicit in many of their techniques. This one might be a little unfair because one can't expect the authors to survey the entire body of literature.
6 of 8 people found the following review helpful:
4.0 out of 5 stars
modelling financial instruments,
By
This review is from: An Introduction to High-Frequency Finance (Hardcover)
The book gives an indepth statistical modelling of important financial events, that have time dependency. It is suitable for the financial analyst who wants a semi-empirical approach.
For some quantities, like foreign exchange data, there is a comparison between fully empirical results and various theoretical models. What is investigated are such behaviours like scaling laws, for the absolute returns as a function of frequency. Here, it has been empirically observed that scalings do exist for FX rates. Whenever possible, the book gives rigorous results, often encapsulated in theorems relating to distributions of independently distributed random variables. The reader should have a background in statistics, with the equivalent of several years of undergraduate courses.
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