Ruey S. Tsay's Analysis of Financial Time Series Reviewed By Manoj Rengarajan of Bookpleasures.com
- By Manoj Rengarajan
- Published January 20, 2011
- Business
Manoj Rengarajan
Reviewer Manoj
Rengarajan holds a Master of Financial Engineering - University of
California, Berkeley and he works in the investment management
industry and specializes in providing economic and investment outlook
and strategy for global equity and government bond markets. He has an
educational background in financial engineering, business, and
engineering, and professional interests include business,
finance, economics, technology and related areas.
Author: Ruey S. Tsay
Publisher:
Wiley
ISBN: 978-0-470-41435
The past thirty years have seen a
tremendous development of time series techniques that have found use
in analyzing financial and economic data. The Analysis of Financial
Time Series by Ruey Tsay is a comprehensive book that addresses the
theory behind the key time series techniques as well as the practical
use of these techniques using S-plus and R, an open source
software.
Starting with a basic introduction to financial time
series and their characteristics, there is a treatment of basic
definitions, distributions and their empirical properties, analyzing
volatility processes, and R packages.
Key concepts of
stationarity, correlation and auto correlation, simple auto
regressive models and their properties, moving average models and
their estimation, and seasonal models are described carefully and
clearly.
One of the key parameters modeled with respect to
financial time series is volatility. There is a clear description of
the characteristic of volatility in financial time series followed by
how volatility can be modeled by conditional heteroskedastic models.
Estimation and testing of ARCH/ GARCH models along with their
weaknesses are covered. Other variants of GARCH are also covered.
Non
linear modeling of financial data is demonstrated in the context of
U.S. examples using R. The coverage is detailed and includes tests
and forecasting.
High frequency analysis and market
micro-structure goes into details about non-synchronous trading,
empirical characteristics, and a range of price changes and duration
models.
The section on continuous time models and applications not
only covers the background on basic stochastic processes but goes
into detailing jump diffusion processes.
Risk management is one of
the key financial areas where time series techniques have found
significant applications. This section covers the basic value at risk
(VaR) and RiskMetrics framework as well as provides an econometric
approach to VaR calculation and advanced concepts such as the extreme
value theory.
In the context of multivariate time series, trading
strategy applications are brought out highlighting the use of vector
moving average and co-integration models. There is also a good
treatment of multivariate volatility models and their
implementation.
Factor models, an important technique used in
quantitative finance, are given a full treatment with macroeconomic
factor models and fundamental factor models.
The coverage of the
book is comprehensive. It starts from basic time series techniques
and finishes with advanced concepts such as state space models and
MCMC methods. There is a balance between the theoretical background
necessary to appreciate the nuances and the practical aspect of
implementation.
More importantly it gives insights about what time
series models can’t address. The book has an excellent supporting
website which has all the programs and data sets which helps to
internalize the concepts. Finally, teaching professionals should find
the solutions manual as a valuable tool to explain concepts and to
ensure understanding.