Customer Reviews

An Introduction to High-Frequency Finance

5 star:		(4)
4 star:		(1)
3 star:		(3)
2 star:		(0)
1 star:		(0)

 

 

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.

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.

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.

There are some useful results in this book. It has an especially good section on statistical techniques for data cleanup and making sure you have a clean tick series. Also, there are some interesting pieces on how to handle the discontinuous nature of tick data as opposed to what you see with daily data. Unfortunately, the book is written in a style that is hard to follow so that even standard results seem somewhat obscure the way the authors present them. Also, the notation is a pain, as noted by other reviewers they use a kind of computer notation rather than standard statistical notation making many of the forumulae much more difficult to read than they should be. Overall, a good book but it suffers from a poor writing style and is getting to be somewhat dated.

Michel Dacorogna and the team at the former Olsen & Associates are well-known experts in the field of foreign exchange rate data analysis, and their book provides us with a vast, useful source of information. Unfortunately for students and other beginners, the book is written like a compilation of papers and review articles, the opposite of pedagogical, and with an awful choice of 'computerese' notation (MA(t,n)=sum(EMA(t',k)... etc) that makes Boudhaud-Potters look easy in comparison. More to the point, even their noncomputerese notation is difficult to follow. I hope for a very different second edition written pedagogically for students of this growing and important field. On the positive side, data analyses are performed using logarithmic returns, not price increments. Workers in the field who consult this text will find it helpful.

The tools described in this book are actually in mid frequency, not exactly high frequency. And they are specific for FX. If you are looking for truly high frequency strategies for equities, this book is not for you.

Many type of error the book list are frequently occur in FX data.
This book give good guide on how to filter them.

The book covers a wide range of topics related to high-frequency data in Finance. There is a very detailed approach to tackle a huge amount of data and to deal with its based stylized facts. The book triggers the reader's desire to update his knowledge in the field of finance.