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
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
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
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.
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