Incluye referencias bibliográficas (páginas 412-445) e índice.

1. Introduction -- 2. Univariate linear stochastic models: basic concepts: 2.1. Stochastic processes. Ergodicity and stationary ; 2.2. Stochastic difference equations ; 2.3. ARMA processes ; 2.4. Linear stochastic processes ; 2.5. ARMA model building ; 2.6. Non-stationary processes and ARIMA models ; 2.7. ARIMA modelling ; 2.8. Seasonal ARIMA modeling ; 2.9. Forecasting using ARIMA models -- 3. Univariate linear stochastic models: testing for unit roots and alternative trend specifications: 3.1. Determining the order of integration of a time series ; 3.2. Testing for a unit root ; 3.3. Trend stationary versus difference stationary ; 3.4. Other approaches to testing for unit root ; 3.6. Segmented trends, structural breaks and smooth transitions ; 3.7. Stochastic unit root processes -- 4. Univariate linear stochastic models: further topics: 4.1. Decomposing time series: unobserved component models and signal extraction ; 42. Measures of persistence and trend reversion ; 4.3. Fractional integration and long memory processes -- 5. Univariate non-linear stochastic models: martingales, random walks, and modelling volatility: 5.1. Martingales, random walks and non-linearity ; 5.2. Testing the random hypothesis ; 5.3. Measures of volatility ; 5.4. Stochastic volatility ; 5.5. ARCH processes ; 5.6. Some models related to ARCH ; 5.7. The forecasting performance of alternative volatility models -- 6. Univariate non-linear stochastic models: further models and testing procedures: 6.14. Bilinear and related models ; 6.2. Regime-switching models: markov chains and smooth transition autoregression ; 6.3. Non-parametric and neural network models ; 6.4. Non-linear dynamics and chaos ; 6.5. Testing for non-linearity -- 7. Modelling return distributions: 7.1. Descriptive analysis of returns series ; 7.2. Two models for returns distributions ; 7.3. Determining the tail shape of returns distribution ; 7.4. Empirical evidence on tail indices ; 7.5. Testing for covariance stationarity ; 7.6. Modelling the central part of returns distributions ; 7.7. Data-analytic properties of absolute returns ; 7.8. Distributional properties of absolute returns ; 7.9. Summary and further extensions -- 8. Regression techniques for non-integrated financial time series: 8.1. Regression models ; 8.2. ARCH-in mean regression models ; 8.3. Misspecification testing ; 8.4. Robust estimation ; 8.5. The multivariate linear regression model ; 8.6. Vector autoregressions ; 8.7. Variance decompositions, innovation accounting and structural VARs ; 8.9. Multivariate GARCH models -- 9. Regression techniques for integrated financial time series: 9.1. Spurious regression ; 9.2. Cointegrated processes ; 9.3. Testing for cointegration in regression ; 9.4. Estimating cointegrating regressions ; 9.5. VARs with integrated variables ; 9.6. Causality testing in VECMs ; 9.7. Impulse response Asympotics in non-stationary VARs ; 9.8. Testing for a single long-run relationship ; 9.9. Common trends and cycles -- 10. Further topics in the analysis of integrated financial time series: 10.1. Present value models, excess volatility and cointegration ; 10.2. Generalizations and extension of cointegration and error correction models.