Analysis of Financial Time Series
$23.11
Analysis of Financial Time Series provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.
Introduction:
The subject of financial time series analysis has attracted substantial attention in recent years, especially with the 2003 Nobel awards to Professors Robert Engle and Clive Granger. At the same time, the field of financial econometrics has undergone various new developments, especially in high-frequency finance, stochastic volatility, and software availability.
There is a need to make the material more complete and accessible for advanced undergraduate and graduate students, practitioners, and researchers. The main goals in preparing this second edition have been to bring the book up to date both in new developments and empirical analysis, and to enlarge the core material of the book by including consistent covariance estimation under heteroscedasticity and serial correlation, alternative approaches to volatility modeling, financial factor models, state-space models, Kalman filtering, and estimation of stochastic diffusion models. The book therefore has been extended to 12 chapters and substantially revised to include S-Plus commands and illustrations. Many empirical demonstrations and exercises are updated so that they include the most recent data.
Contents:
- Financial Time Series and Their Characteristics
- Linear Time Series Analysis and Its Applications
- Conditional Heteroscedastic Models
- Nonlinear Models and Their Applications
- High-Frequency Data Analysis and Market Microstructure
- Continuous-Time Models and Their Applications
- Extreme Values, Quantiles, and Value at Risk
- Multivariate Time Series Analysis and Its Applications
- Principal Component Analysis and Factor Models
- Multivariate Volatility Models and Their Applications
- State-Space Models and Kalman Filter
- Markov Chain Monte Carlo Methods with Applications
Analysis of Financial Time Series By Ruey S. Tsay pdf
| Author(s) | |
|---|---|
| Format | |
| Pages | 713 |
| Publication Year | 2010 |
7 reviews for Analysis of Financial Time Series
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Kase Sampson (verified owner) –
Purchased for a time series class and I cannot get through it. Too complicated and too many equations. I resort to almost any other reference and use this only as required.
Haley Phelps (verified owner) –
very technical and detailed contents for those in the industry, the best reading on the subject of time series!
Noa Frederick (verified owner) –
NICE!
Meilani Reyes (verified owner) –
This book is not for you if you are just starting learn about Time Series analysis or Econometrics. This is way too math heavy and absolutely no attempt made to explain the concepts.
Ahmir Bauer (verified owner) –
If you are in the industry of math finance. This is the must-read book in time series.
Ariah Moyer (verified owner) –
Very well explained book. Also attached branch of examples with R studio codes.
Hamza Cannon (verified owner) –
Tsay does an outstanding job taking commonly taught time series concepts and explaining them in a different way. He starts with review of the basics i.e., distributions, moments, processes, stationarity. He covers the functional form and properties of AR, MA, and ARMA models, as well as non-stationary processes (random walk), trend-stationary processes, unit-root tests (Dickey-Fuller), autocorrelation tests (Ljung Box), and seasonality. He naturally transitions into conditional hereoskedasticity models (ARCH/GARCH) and the many special cases (EGARCH/IGARCH/TGARCH/Stochastic vol.). He has a great section on nonlinear models, focusing on threshold and smooth transition AR models, Markov switching models, non-parametric models (kernel regression), and state-space models.
His section on big/high-frequency data and market microstructure should be required reading. He covers nonsynchronous trading (and how it may induce serial or cross-correlation), bid-ask bounce, duration models, and the Eps effect.
The section on continuous-time stochastic processes is extremely clear (more than other texts). It explains the Wiener process, Ito process (and Ito’s lemma), the Black Scholes Merton differential equation, the Black Scholes Merton pricing formula (risk-neutral proof), and jump-diffusion models.
Tsay also includes an entire section on Value at Risk (parametric, historical, econometric), extreme value theory (Generalized Pareto and Peaks-over-thresholds), and the extremal index.
Multivariate topics are covered, including vector autoregression (with Cholesky regularization), vector moving average, vector ARMA, cointegration, cointegrated VAR, vector error correction, and threshold cointegration. Multivariate volatility models are also mentioned, including multivariate GARCH and BEKK model.
He explains principal component analysis in connection with factor models, including the Barra factor model and the Fama-French model. He also goes deeper into state space models, giving a full explanation of the local trend model and deriving the Kalman filter (as well as smoothing algorithms). At the end of the text, he has some advanced approaches to dealing with missing values and outliers, giving a brief overview of Bayesian inference and Markov chain Monte Carlo (Gibbs, Metropolis-Hastings, Griddy gibbs, etc.). He includes Monte Carlo applications, such as Markov switching and stochastic volatility models.