Time series: applications to finance
Elements of Financial Time Series fills a gap in the market in the area of financial time series analysis by giving both conceptual and practical illustrations. Examples and discussions in the later chapters of the book make recent developments in time series more accessible. Examples from finance are maximized as much as possible throughout the book.
* Full set of exercises is displayed at the end of each chapter.
* First seven chapters cover standard topics in time series at a high-intensity level.
* Recent and timely developments in nonstandard time series techniques are illustrated with real finance examples in detail.
* Examples are systemically illustrated with S-plus with codes and data available on an associated Web site.
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Autoregressive Moving Average Models
Estimation in the Time Domain
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algorithm Applications AR(p ARIMA ARMA model ARMA(p asymptotic AXt-i Brockwell and Davis canonical correlation causal chapter characteristic polynomial coefficient cointegrating vector cointegration common trends component compute conditional correlation covariance matrix data set ddacc defined Definition denotes Diagnostics differencing discussed distribution E(Xt equation example expression Figure fitted model forecast GARCH model GARCH(1 given invertible Kalman filter Let Yt likelihood function linear mean minimized model Yt moving average multivariate time series nonstationary observations P-value PACF parameters periodogram polynomial Portmanteau Portmanteau statistics process Yt recursively Regression satisfies seasonal Second Edition series analysis series plot series Xt simulate spectral density spectral density function Splus program standardized residuals stationary process Statistical Methods stochastic process stochastic volatility structure transformation uncorrelated unit root univariate values VAR(p variance white noise Xt-i Yt-i zero