Time Series: Applications to FinanceElements 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. |
Contents
Introduction | 1 |
Probability Models | 15 |
Autoregressive Moving Average Models | 26 |
Copyright | |
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a₁ absolutely summable algorithm Applications AR(p ARCH ARIMA ARMA model asymptotic autocovariance Brockwell and Davis causal coefficient cointegration component compute conditional correlation covariance data set ddacc defined Definition denotes Diagnostics differenced discussed distribution dlinc1 econometric equation error example Figure fitted model GARCH model given Kalman filter Let Xt likelihood function linear matrix mean method minimized moving average nonstationary observation one-step-ahead P-value PACF parameters Partial ACF periodogram polynomial Portmanteau Portmanteau statistics prediction process Xt random variables recursively regression sample seasonal Second Edition sequence series analysis series plot smoothing space model space representation spectral density spectral density function SPLUS program Standardized Residuals stationary process stochastic process stochastic volatility Theorem trend tsplot uncorrelated unit root univariate VAR(p variance vector white noise Wiener process Y₁ Yn+1 zero