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书籍名称:Financial Econometrics Using Stata
出版社:Stata Press
作者: Simona Boffelli and Giovanni Urga
语种: 英文
页数: 272
开本: 胶版纸
纸张:272 I S B N: 978-1-59718-214-0
装订: 平装


Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial economists. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results. After providing an intuitive introduction to time-series analysis and the ubiquitous autoregressive moving-average (ARMA) model, the authors carefully cover univariate and multivariate models for volatilities. Chapters on risk management and analyzing contagion show how to define, estimate, interpret, and perform inference on essential measures of risk and contagion. The authors illustrate every topic with easily replicable Stata examples and explain how to interpret the results from these examples. The authors have a unique blend of academic and industry training and experience. This training produced a practical and thorough approach to each of the addressed topics.


    List of figures
    Notation and typography
    1 Introduction to financial time series
    1.1 The object of interest
    1.2 Approaching the dataset
    1.3 Normality
    1.4 Stationarity
    1.4.1 Stationarity tests
    1.5 Autocorrelation
    1.5.1 ACF
    1.5.2 PACF
    1.6 Heteroskedasticity
    1.7 Linear time series
    1.8 Model selection
    1.A How to import data
    2 ARMA models
    2.1 Autoregressive (AR) processes
    2.1.1 AR(1)
    2.1.2 AR(p)
    2.2 Moving-average (MA) processes
    2.2.1 MA(1)
    2.2.2 MA(q)
    2.2.3 Invertibility
    2.3 Autoregressive moving-average (ARMA) processes
    2.3.1 ARMA(1,1)
    2.3.2 ARMA(p,q)
    2.3.3 ARIMA
    2.3.4 ARMAX
    2.4 Application of ARMA models
    2.4.1 Model estimation
    2.4.2 Postestimation
    2.4.3 Adding a dummy variable
    2.4.4 Forecasting
    3 Modeling volatilities, ARCH models, and GARCH models
    3.1 Introduction
    3.2 ARCH models
    3.2.1 General options
    3.2.2 Additional options
    The het() option
    The maximize_options options
    3.2.3 Postestimation
    3.3 ARCH(p)
    3.4 GARCH models
    3.4.1 GARCH(p,q)
    3.4.2 GARCH in mean
    3.4.3 Forecasting
    3.5 Asymmetric GARCH models
    3.5.1 SAARCH
    3.5.2 TGARCH
    3.5.3 GJR–GARCH
    3.5.4 APARCH
    3.5.5 News impact curve
    3.5.6 Forecasting comparison
    3.6 Alternative GARCH models
    3.6.1 PARCH
    3.6.2 NGARCH
    3.6.3 NGARCHK
    4 Multivariate GARCH models
    4.1 Introduction
    4.2 Multivariate GARCH
    4.3 Direct generalizations of the univariate GARCH model of Bollerslev
    4.3.1 Vech model
    4.3.2 Diagonal vech model
    4.3.3 BEKK model
    4.3.4 Empirical application
    Data description
    Dvech model
    4.4 Nonlinear combination of univariate GARCH—common features
    4.4.1 Constant conditional correlation (CCC) GARCH
    Empirical application
    4.4.2 Dynamic conditional correlation (DCC) model
    Dynamic conditional correlation Engle (DCCE) model
    Empirical application
    Dynamic conditional correlation Tse and Tsui (DCCT)
    4.5 Final remarks
    5 Risk management
    5.1 Introduction
    5.2 Loss
    5.3 Risk measures
    5.4 VaR
    5.4.1 VaR estimation
    5.4.2 Parametric approach
    5.4.3 Historical simulation
    5.4.4 Monte Carlo simulation
    5.4.5 Expected shortfall
    5.5 Backtesting procedures
    5.5.1 Unilevel VaR tests
    The unconditional coverage test
    The independence test
    The conditional coverage test
    The duration tests
    6 Contagion analysis
    6.1 Introduction
    6.2 Contagion measurement
    6.2.1 Cross-market correlation coefficients
    Empirical exercise
    6.2.2 ARCH and GARCH models
    Empirical exercise
    Markov switching
    6.2.3 Higher moments contagion
    Empirical exercise
    Glossary of acronyms
    Author index
    Subject index