By Ruey S. Tsay

ISBN-10: 0471746185

ISBN-13: 9780471746188

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

The moment version of this seriously acclaimed textual content presents a complete and systematic advent to monetary econometric versions and their functions in modeling and predicting monetary time sequence information. This most recent variation maintains to stress empirical monetary info and makes a speciality of real-world examples. Following this strategy, readers will grasp key points of monetary time sequence, together with volatility modeling, neural community purposes, industry microstructure and high-frequency monetary information, continuous-time versions and Ito's Lemma, worth in danger, a number of returns research, monetary issue versions, and econometric modeling through computation-intensive methods.

The writer starts off with the fundamental features of economic time sequence facts, atmosphere 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 re-creation 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 multiplied and comprise the most up-tp-date facts, supplying readers with extra possibilities to place the versions and techniques into perform. one of the new fabric additional to the textual content, readers will find:

constant covariance estimation less 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 figuring out of monetary markets via firsthand event in operating with monetary info. this is often an amazing textbook for MBA scholars in addition to a reference for researchers and execs in enterprise and finance.

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**Additional resources for Analysis of Financial Time Series**

**Example text**

Let P (X ∈ A, Y ∈ B) be the probability that X is in the subspace A ⊂ R k and Y is in the subspace B ⊂ R q . For most of the cases considered in this book, both random vectors are assumed to be continuous. Joint Distribution The function FX,Y (x, y; θ ) = P (X ≤ x, Y ≤ y; θ ), where x ∈ R p , y ∈ R q , and the inequality “≤” is a component-by-component operation, is a joint distribution function of X and Y with parameter θ. Behavior of X and Y is characterized by FX,Y (x, y; θ ). If the joint probability density function fx,y (x, y; θ ) of X and Y exists, then FX,Y (x, y; θ ) = x y −∞ −∞ fx,y (w, z; θ) dz dw.

2. Answer the following questions: (a) What is the average annual log return over the data span? (b) Assume that there were no transaction costs. 00 on the S&P composite index at the beginning of 1975, what was the value of the investment at the end of 2003? 4. 1. Use the 5% signiﬁcance level to perform the following tests. (a) Test the null hypothesis that the skewness measure of the returns is zero. (b) Test the null hypothesis that the excess kurtosis of the returns is zero. 5. Daily foreign exchange rates (spot rates) can be obtained from the Federal Reserve Bank in Chicago.

78). These asymptotic properties can be used to test the normality of asset returns. Given an asset return series {r1 , . . , rT }, to test the skewness of the returns, we consider the null hypothesis Ho : S(r) = 0 versus the alternative hypothesis Ha : S(r) = 0. The t-ratio statistic of the sample skewness in Eq. 12) is ˆ S(r) t= √ . 6/T The decision rule is as follows. Reject the null hypothesis at the α signiﬁcance level, if |t| > Zα/2 , where Zα/2 is the upper 100(α/2)th quantile of the standard normal distribution.

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