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

By Ruey S. Tsay

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

The moment variation of this severely acclaimed textual content offers a accomplished and systematic creation to monetary econometric versions and their functions in modeling and predicting monetary time sequence information. This most up-to-date version keeps to stress empirical monetary information and makes a speciality of real-world examples. Following this procedure, readers will grasp key points of monetary time sequence, together with volatility modeling, neural community functions, marketplace microstructure and high-frequency monetary facts, continuous-time types and Ito's Lemma, price 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 monetary time sequence facts, 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. routines were completely up-to-date and accelerated and contain the most up-tp-date facts, offering readers with extra possibilities to place the types and techniques into perform. one of the new fabric additional to the textual content, readers will find:

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

The instruments supplied during this textual content reduction readers in constructing a deeper knowing of economic markets via firsthand event in operating with monetary facts. this is often a fantastic textbook for MBA scholars in addition to a reference for researchers and execs in enterprise and finance.

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Market indexes and individual stocks. The returns are for daily and monthly sample intervals and are in percentages. The data spans and sample sizes are also given in the table. From the table, we make the following observations. (a) Daily returns of the market indexes and individual stocks tend to have high excess kurtosis. For monthly series, the returns of market indexes have higher excess kurtosis than individual stocks. (b) The mean of a daily return series is close to zero, whereas that of a monthly return series is slightly larger.

For more information about the asymptotic distribution of sample autocorrelations, see Fuller (1976, Chapter 6) and Brockwell and Davis (1991, Chapter 7). Testing Individual ACF For a given positive integer , the previous result can be used to test Ho : ρ = 0 versus Ha : ρ = 0. The test statistic is t-ratio = ρˆ (1 + 2 −1 2 i=1 ρˆi )/T . If {rt } is a stationary Gaussian series satisfying ρj = 0 for j > , the t-ratio is asymptotically distributed as a standard normal random variable. Hence, the decision rule of the test is to reject Ho if |t-ratio| > Zα/2 , where Zα/2 is the 100(1 − α/2)th percentile of the standard normal distribution.

T . For each asset i, let rit be its log return at time t. The log returns under study are {rit ; i = 1, . . , N ; t = 1, . . , T }. One can also consider the simple returns {Rit ; i = 1, . . , N ; t = 1, . . , T } and the log excess returns {zit ; i = 1, . . , N ; t = 1, . . , T }. 1 Review of Statistical Distributions and Their Moments We briefly review some basic properties of statistical distributions and the moment equations of a random variable. Let R k be the k-dimensional Euclidean space.

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