Searching for High-Frequency Trading Opportunities E-Book

Irene Aldridge

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Global Finance Executive Select
• Sprache: Englisch
• Veröffentlichungsjahr: 2011

Seitenzahl: 35

Beschreibung

Praise for High-Frequency Trading "A well thought out, practical guide covering all aspects of high-frequency trading and of systematic trading in general. I recommend this book highly." --Igor Tulchinsky, CEO, WorldQuant, LLC "For traditional fundamental and technical analysts, Irene Aldridge's book has the effect a first read of quantum physics would have had on traditional Newtonian physicists: eye-opening, challenging, and enlightening." --Neal M. Epstein, CFA, Managing Director, Research & Product Management, Proctor Investment Managers LLC Interest in high-frequency trading continues to grow, yet little has been published to help investors understand and implement high-frequency trading systems--until now. This book has everything you need to gain a firm grip on how high-frequency trading works and what it takes to apply this approach to your trading endeavors. Written by industry expert Irene Aldridge, High-Frequency Trading offers innovative insights into this dynamic discipline. Covering all aspects of high-frequency trading--from the formulation of ideas and the development of trading systems to application of capital and subsequent performance evaluation--this reliable resource will put you in a better position to excel in today's turbulent markets.

Leseprobe

Contents

Cover

Title Page

Copyright

CHAPTER 8: Searching for High-Frequency Trading Opportunities

STATISTICAL PROPERTIES OF RETURNS

LINEAR ECONOMETRIC MODELS

VOLATILITY MODELING

NONLINEAR MODELS

CONCLUSION

Copyright © 2010 by Irene Aldridge. All rights reserved.

Disclaimer. This content is excerpted from High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, by Irene Aldridge (978-0-470-56376-2, 2010), with permission from the publisher John Wiley & Sons. You may not make any other use, or authorize others to make any other use of this excerpt, in any print or non-print format, including electronic or multimedia.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

High-frequency trading relies on fast, almost instantaneous, execution of orders. Depending on the design of a particular systematic trading mechanism, even a second's worth of delay induced by hesitation or distraction on the part of a human trader can substantially reduce the system's profitability. That is the reason it is crucial to know and understand the field of econometrics.

This topic is by no means exhaustive; it is instead intended as a high-level refresher on the core econometric concepts applied to trading at high frequencies. Yet, readers relying on software packages with preconfigured statistical procedures may find the level of detail presented here to be sufficient for quality analysis of trading opportunities.

Concepts for identifying and modeling trading opportunities discussed in this chapter include:

• fundamental statistical estimators

• linear dependency identification methods

• volatility

• modeling techniques

• standard nonlinear approaches

Derived from Aldridge, Irene. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. Hoboken, NJ: John Wiley & Sons, 2010. 978-0-470-56376-2; 336 pp.

978-1-118-00631-3978-1-118-00630-6

CHAPTER 8

Searching for High-Frequency Trading Opportunities

This chapter reviews the most important econometric concepts used in the subsequent parts of the book. The treatment of topics is by no means exhaustive; it is instead intended as a high-level refresher on the core econometric concepts applied to trading at high frequencies. Yet, readers relying on software packages with preconfigured statistical procedures may find the level of detail presented here to be sufficient for quality analysis of trading opportunities. The depth of the statistical content should be also sufficient for readers to understand the models presented throughout the remainder of this book. Readers interested in a more thorough treatment of statistical models may refer to Tsay (2002); Campbell, Lo, and MacKinlay (1997); and Gouriéroux and Jasiak (2001).

This chapter begins with a review of the fundamental statistical estimators, moves on to linear dependency identification methods and volatility modeling techniques, and concludes with standard nonlinear approaches for identifying and modeling trading opportunities.

STATISTICAL PROPERTIES OF RETURNS

According to Dacorogna et al. (2001, p. 121), “high-frequency data opened up a whole new field of exploration and brought to light some behaviors that could not be observed at lower frequencies.” Summary statistics about aggregate behavior of data, known as “stylized facts,” help distill particularities of high-frequency data. Dacorogna et al. (2001) review stylized facts for foreign exchange rates, interbank money market rates, and Eurofutures (futures on Eurodollar deposits).

Financial data is typically analyzed using returns. A return is a difference between two subsequent price quotes normalized by the earlier price level. Independent of the price level, returns are convenient for direct performance comparisons across various financial instruments. A simple return measure can be computed as shown in equation (8.1):

(8.1)

where Rt is the return for period t, Pt is the price of the financial instrument of interest in period t, and Pt–1 is the price of the financial instrument in period t