Download Analysis of Financial Time Series, Second Edition by Ruey S. Tsay(auth.), Walter A. Shewhart, Samuel S. PDF

By Ruey S. Tsay(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.)

ISBN-10: 0471690740

ISBN-13: 9780471690740

Offers statistical instruments and methods had to comprehend ultra-modern monetary markets

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

The writer starts off with the fundamental features of monetary time sequence information, atmosphere the root for the 3 major issues:

  • Analysis and alertness of univariate monetary time sequence
  • Return 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. routines were completely up-to-date and accelerated and comprise the most up-tp-date info, supplying readers with extra possibilities to place the versions and strategies into perform. one of the new fabric further to the textual content, readers will locate:

  • Consistent covariance estimation less than heteroscedasticity and serial correlation
  • Alternative ways to volatility modeling
  • Financial 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 monetary markets via firsthand event in operating with monetary facts. this can be an amazing textbook for MBA scholars in addition to a reference for researchers and pros in company and finance.

Content:
Chapter 1 monetary Time sequence and Their features (pages 1–23):
Chapter 2 Linear Time sequence research and Its purposes (pages 24–96):
Chapter three Conditional Heteroscedastic versions (pages 97–153):
Chapter four Nonlinear versions and Their functions (pages 154–205):
Chapter five High?Frequency info research and marketplace Microstructure (pages 206–250):
Chapter 6 Continuous?Time types and Their purposes (pages 251–286):
Chapter 7 severe Values, Quantile Estimation, and cost in danger (pages 287–338):
Chapter eight Multivariate Time sequence research and Its functions (pages 339–404):
Chapter nine critical part research and issue versions (pages 405–442):
Chapter 10 Multivariate Volatility versions and Their functions (pages 443–489):
Chapter eleven State?Space versions and Kalman clear out (pages 490–542):
Chapter 12 Markov Chain Monte Carlo tools with purposes (pages 543–600):

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Additional info for Analysis of Financial Time Series, Second Edition

Example text

S. 1. The AIC obtained from the Finmetrics module of S-Plus also identifies an AR(3) model. Here the criterion value has been adjusted so that the minimum AIC is zero. 378 > ord$order [1] 3 Parameter Estimation For a specified AR(p) model in Eq. 9), the conditional least squares method, which starts with the (p + 1)th observation, is often used to estimate the parameters. Specifically, conditioning on the first p observations, we have rt = φ0 + φ1 rt−1 + · · · + φp rt−p + at , t = p + 1, . . , T , which is in the form of a multiple linear regression and can be estimated by the least squares method.

The model in Eq. 14) is too general to be of practical value. However, it provides a general framework with respect to which an econometric model for asset returns rit can be put in a proper perspective. , the distribution of {r1t , . . , rNt }). , the distribution of {ri1 , . . , riT } for a given asset i). In this book, we focus on both. In the univariate analysis of Chapters 2–7, our main concern is the joint distribution of {rit }Tt=1 for asset i. To this end, it is useful to partition the joint distribution as F (ri1 , .

Analysis of Financial Time Series, Second Edition Copyright  2005 John Wiley & Sons, Inc. 24 By Ruey S. 1 STATIONARITY The foundation of time series analysis is stationarity. A time series {rt } is said to be strictly stationary if the joint distribution of (rt1 , . . , rtk ) is identical to that of (rt1 +t , . . , rtk +t ) for all t, where k is an arbitrary positive integer and (t1 , . . , tk ) is a collection of k positive integers. In other words, strict stationarity requires that the joint distribution of (rt1 , .

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