Preface
As many countries struggle to recover from the recent global financial crisis, one thing clear is that we do not want to suffer another crisis like this in the future. We must study the past in order to prevent future financial crisis. Financial data of the past few years thus become important in empirical study. The primary objective of the revision is to update the data used and to reanalyze the examples so that one can better understand the properties of asset returns. At the same time, we also witness many new developments in financial econometrics and financial software packages. In particular, the Rmetrics now has many packages for analyzing financial time series. The second goal of the revision is to include R commands and demonstrations, making it possible and easier for readers to reproduce the results shown in the book.
Collapses of big financial institutions during the crisis show that extreme events occur in clusters; they are not independent. To deal with dependence in extremes, I include the extremal index in Chapter 7 and discuss its impact on value at risk. I also rewrite Chapter 7 to make it easier to understand and more complete. It now contains the expected shortfall, or conditional value at risk, for measuring finanical risk.
Substantial efforts are made to draw a balance between the length and coverage of the book. I do not include credit risk or operational risk in this revision for three reasons. First, effective methods for assessing credit risk require further study. Second, the data are not widely available. Third, the length of the book is approaching my limit.
A brief summary of the added material in the third edition is:
1. To update the data used throughout the book.
2. To provide R commands and demonstrations. In some cases, R programs are given.
3. To reanalyze many examples with updated observations.
4. To introduce skew distributions for volatility modeling in Chapter 3.
5. To investigate properties of recent high-frequency trading data and to add applications of nonlinear duration models in Chapter 5.
6. To provide a unified approach to value at risk (VaR) via loss function, to discuss expected shortfall (ES), or equivalently the conditional value at risk (CVaR), and to introduce extremal index for dependence data in Chapter 7.
7. To discuss application of cointegration to pairs trading in Chapter 8.
8. To study applications of dynamic correlation models in Chapter 10.
I benefit greatly from constructive comments of many readers of the second edition, including students, colleagues, and friends. I am indebted to them all. In particular, I like to express my sincere thanks to Spencer Graves for creating the FinTS package for R and Tom Doan of ESTIMA and Eugene Gath for careful reading of the text. I also thank Kam Hamidieh for suggestions concerning new topics for the revision. I also like to thank colleagues at Wiley, especially Jackie Palmieri and Stephen Quigley, for their support. As always, the revision would not be possible without the constant encouragement and unconditional love of my wife and children. They are my motivation and source of energy. Part of my research is supported by the Booth School of Business, University of Chicago.
Finally, the website for the book is: http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3.
Ruey S. Tsay
Booth School of Business, University of Chicago
Chicago, Illinois