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1926 to December applications asset returns assume autocorrelation bivariate Chapter coefficient cointegration component compute conditional distribution covariance matrix daily log returns deﬁned degrees of freedom denotes discuss econometric estimate Example excess returns explanatory variables extreme value theory factor model Figure fitted model forecast error forecast origin frml GARCH model Gaussian Gibbs sampling given IBM stock interest rate January Kalman filter lags likelihood function linear regression Ljung–Box statistics Markov maximum likelihood mean equation model in Eq monthly log returns multivariate nonlinear normal distribution observations obtain option p-value parameters plot posterior distribution price change probability quantile random variable return series returns of IBM S-Plus sample ACF sample mean serial correlations series analysis shows standard errors standardized residuals state-space model stochastic volatility stochastic volatility model stock returns threshold time-varying correlation transactions Tsay unit-root univariate vector Wiener process
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Course: Analysis of Financial Time Series
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