Download Analysis of Financial Time Series by Ruey S. Tsay PDF

By Ruey S. Tsay

Offers statistical instruments and strategies had to comprehend today's monetary markets

The moment variation of this seriously acclaimed textual content presents a entire and systematic creation to monetary econometric versions and their functions in modeling and predicting monetary time sequence info. This newest version keeps to stress empirical monetary info and makes a speciality of real-world examples. Following this process, readers will grasp key elements of monetary time sequence, together with volatility modeling, neural community purposes, industry microstructure and high-frequency monetary info, continuous-time versions and Ito's Lemma, price in danger, a number of returns research, monetary issue types, and econometric modeling through computation-intensive methods.

The writer starts off with the fundamental features of economic time sequence info, environment the basis for the 3 major topics:

research and alertness of univariate monetary time series
go back sequence of a number of assets
Bayesian inference in finance methods

This new version is a completely revised and up-to-date textual content, together with the addition of S-Plus® instructions and illustrations. workouts were completely up-to-date and extended and comprise the most up-tp-date facts, delivering readers with extra possibilities to place the versions and strategies into perform. one of the new fabric additional to the textual content, readers will find:

constant covariance estimation lower than heteroscedasticity and serial correlation
substitute methods to volatility modeling
monetary issue models
State-space models
Kalman filtering
Estimation of stochastic diffusion models

The instruments supplied during this textual content relief readers in constructing a deeper realizing of economic markets via firsthand adventure in operating with monetary information. this is often an incredible textbook for MBA scholars in addition to a reference for researchers and execs in company and finance.

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Extra resources for Analysis of Financial Time Series

Example text

International Statistical Review 55: 163–172. Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance 19: 425–442. Snedecor, G. W. and Cochran, W. G. (1980). Statistical Methods, 7th edition. Iowa State University Press, Ames, IA. CHAPTER 2 Linear Time Series Analysis and Its Applications In this chapter, we discuss basic theories of linear time series analysis, introduce some simple econometric models useful for analyzing financial time series, and apply the models to asset returns.

In most financial applications, T is relatively large so that the bias is not serious. Portmanteau Test Financial applications often require to test jointly that several autocorrelations of rt are zero. Box and Pierce (1970) propose the Portmanteau statistic Q∗ (m) = T m ρˆ 2 =1 as a test statistic for the null hypothesis Ho : ρ1 = · · · = ρm = 0 against the alternative hypothesis Ha : ρi = 0 for some i ∈ {1, . . , m}. Under the assumption that {rt } is an iid sequence with certain moment conditions, Q∗ (m) is asymptotically a chi-squared random variable with m degrees of freedom.

4 shows the ACFs of four stationary AR(2) models. 4B 2 )rt = at . 4. 35. 24 < 0, this particular AR(2) model contains two complex characteristic roots, and hence its ACF exhibits damping sine and cosine waves. The other three AR(2) models have real-valued characteristic roots. Their ACFs decay exponentially. 1. S. real gross national product (GNP), seasonally adjusted, from the second quarter of 1947 to the first quarter of 1991. 5 is also used in Chapter 4 as an example of nonlinear economic time series.

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