## Estimation and hypothesis testing in EViews example 2 the CAPM

This exercise will estimate and test some hypotheses about the CAPM beta for several US stocks. First, Open a new workfile to accommodate monthly data commencing in January 2002 and ending in April 2007. Then import the Excel file 'capm.xls'. The file is organised by observation and contains six columns of numbers plus the dates in the first column, so in the 'Names for series or Number if named in file' box, type 6. As before, do not import the dates so the data start in cell B2. The monthly...

## Ustb3m Ustb6m Ustb1y Ustb3y Ustb5y Ustb10y

And click OK, then name the group Interest by clicking the Name tab. The group will now appear as a set of series in a spreadsheet format. From within this window, click View Principal Components. Screenshot 3.2 will appear. There are many features of principal components that can be examined, but for now keep the defaults and click OK. The results will appear as in the following table. Principal Components Analysis Date 08 31 07 Time 14 45 Sample 1986M03 2007M04 Included observations 254...

- A - 2
- A definition of exogeneity
- A multivariate GARCH model for the CAPM with timevarying covariances
- A note on regime switching models and forecasting accuracy
- A note on the t and the normal distributions
- A special type of hypothesis test the tratio
- A strategy for constructing econometric models and a discussion of modelbuilding philosophies
- Uti
- A1 Derivation of the OLS coefficient estimator in the bivariate case
- A1 Introduction
- A2 ao axu2tl Pa856
- A2 Characteristics of probability distributions
- A2 Derivation of the OLS standard error estimators for the intercept and slope in the bivariate case
- A3 Properties of logarithms
- A4 Differential calculus
- A5 Matrices
- A6 The eigenvalues of a matrix
- Adopting the wrong functional form
- Ai ft a2 Ii ft as Hi 04 a4 Gi 05 a5 Di u Vi
- An application of a simultaneous equations approach to modelling bidask spreads and trading activity
- An application of principal components to interest rates
- An example of how to simulate the price of a financial option
- An example of Monte Carlo simulation in econometrics deriving a set of critical values for a Dickey Fuller test
- An example of the use of a simple ttest to test a theory in finance can US mutual funds beat the market
- An introduction to statistical inference
- Antithetic variates
- Mathematical derivations of CLRM results
- A brief introduction to factor models and principal components analysis
- Appendix Parameter estimation using maximum likelihood
- Appendix The maximum likelihood estimator for logit and probit models
- ARCH model procedures
- ARIMA modelling
- ARMA processes
- Assumption 1
- Assumption 2 varut a2
- Assumption 3 covu Uj 0 for i j
- Assumption 4 the xt are nonstochastic
- Assumption 5 the disturbances are normally distributed
- Asymmetric GARCH models
- Autoregressive conditionally heteroscedastic ARCH models
- Autoregressive processes
- Autoregressive volatility models
- Ayt f yti ut
- Bayesian statistics
- Bootstrapping
- Building ARMA models the Box Jenkins approach
- Cambridge University Press
- Can the original coefficients be retrieved from the ns
- Can UK unit trust managers beat the market
- Censored and truncated dependent variables
- Censored dependent variable models
- Chaos in financial markets
- Choice of computer software
- Choosing between the logit and probit models
- Classifying the errors that can be made using hypothesis tests
- Cointegration
- Cointegration between international bond markets
- Cointegration between international bond markets a multivariate approach
- Cointegration between international bond markets a univariate approach
- Cointegration in international bond markets conclusions
- Computational techniques for options pricing and other uses
- Conclusions
- Consider the following simultaneous equations system
- Consistency
- Control variates
- Copulas and quantile regressions
- Covered and uncovered interest parity
- Criticisms of Dickey Fuller and Phillips Perrontype tests
- Crossequation restrictions for VAR lag length selection
- D
- Data and results
- Data mining and the true size of the test
- Definition of cointegration Engle and Granger 1987
- Determinants of sovereign credit ratings
- Determining the number of restrictions m
- Determining whether a forecast is accurate or not
- Disadvantages of the simulation approach to econometric or financial problem solving
- Does the VAR include contemporaneous terms
- Dummy variables for seasonality in EViews
- Dynamic hedge ratios
- E u
- E07
- E2
- Efficiency
- Equilibrium correction or error correction models
- Erford C Ersandp
- Ermsoft C
- Ersandp Erford
- Estimating a timevarying hedge ratio for FTSE stock index returns
- Estimating multivariate GARCH models using EViews
- Estimating simple piecewise linear functions
- Estimating the autocorrelation coefficients for up to 12 lags
- Estimating the variance of the error term a2
- Estimation of just identified and overidentified systems using 2SLS
- Estimation of limited dependent variable models
- Estimation options
- Eviews Principal Component
- Example - 2 3 4 5 6 7 8 9
- Examples of possible cointegrating relationships in finance
- Exponential smoothing
- Exponentially weighted moving average models
- Extensions to the basic GARCH model
- Fail C Age English Female Workexperience Agrade Belowbgrade Pgdegree Year2004 Year2005 Year2006 Year2007
- Finance theory and time series analysis
- Financial econometrics the future
- Forecasting covariances and correlations
- Forecasting in econometrics
- Forecasting spot returns
- Forecasting the future value of an ARp process
- Forecasting the future value of an MAq process
- Forecasting with time series versus structural models
- Fundamentals of Markov switching models
- Further reading
- G X 1 g X 1 g X gg X 1 g X g g X 1g X g g X 1 g X
- GARCH11 Dynamic forecasts 2 years ahead
- Generalised Arch Garch models
- Generates The Data
- Getting started
- Getting the data
- GJR and Egarch in EViews
- Goodness of fit measures for linear dependent variable models
- Goodness of fit statistics
- Hedonic pricing models
- Higher moment models
- Historical volatility
- How are the parameters the elements of the vector calculated in the generalised case
- How might the finished project look
- How might the option pricetrading volume and the bidask spread be related
- Hypothesis testing in EViews example 1 hedging revisited
- Hypothesis testing some concepts
- I
- Implied volatility models
- Impulse responses and variance decompositions
- Inclusion of an irrelevant variable
- Indirect least squares ILS
- Info - 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
- Information criteria for ARMA model selection
- Information criteria for VAR lag length selection
- Instrumental variables
- Introduction - 2
- Introduction and motivation
- Introduction what are panel techniques and why are they used
- Investigating banking competition using a fixed effects model
- J2ytiP Ed yii 1 P
- J3
- Ji
- L 1 01L 02 L2 0qLq
- Leadlag and longterm relationships between spot and futures markets
- Learning Outcomes - 2 3 4 5
- Likelihood ratio tests
- Limitations of ARCHq models
- Linearity and possible forms for the regression function
- Llf
- Longmemory models
- Lspot C Lfutures
- M
- M111995m12
- M11992m12
- M11995m12
- Market microstructure
- Methodology - 2
- Methods of parameter estimation in cointegrated systems
- Mi M2 uT M2 3A2
- Model estimation for multivariate GARCH
- Modelling seasonality in financial data
- Models for volatility
- Monte Carlo simulations
- Motivation
- Motivations - 2
- Motivations an excursion into nonlinearity land 3
- Moving average processes
- Multicollinearity
- Multinomial linear dependent variables
- Multivariate GARCH models
- N - 2 3
- N2
- Neural network models
- Next
- Nonindependent data
- Nonnegativity constraints
- Nonnormality and maximum likelihood
- Notation
- Ok
- Omission of an important variable
- Panel data application to credit stability of banks in Central and Eastern Europe
- Panel data with EViews
- Precision and standard errors
- Presentational issues
- Pricing Asian options using EViews
- Problems with VARs
- Properties of the OLS estimator
- Purchasing power parity
- Q n1226
- Random number generation
- Random number reusage across experiments
- Resample the data
- Review questions - 2 3 4 5 6 7
- S
- S2
- Sample acf and pacf plots for standard processes
- Scalar Mcrrs
- Second Edition
- Selecting the topic
- Short Position
- Simple linear regression in EViews estimation of an optimal hedge ratio
- Simulating the price of a financial option using a fattailed underlying process
- Simulating the price of an Asian option
- Simultaneous equations bias
- Simultaneous equations in finance
- Smpl110000
- So how can simultaneous equations models be validly estimated
- Some comments on the standard error estimators
- Some more definitions and terminology
- Some more terminology onestepahead versus multistepahead forecasts and rolling versus recursive samples
- Some notation and concepts
- Sources of data used in this book
- Specification tests in the context of Markov switching and threshold autoregressive models a cautionary note
- Sponsored or independent research
- Statement of the order condition
- Statistical distributions for diagnostic tests
- Statistical versus financial or economic loss functions
- Step
- Stochastic volatility models revisited
- Summary of the book
- T - 2 3 4 5 6
- Tail models
- Testing for a unit root
- Testing for and estimating cointegrating systems using the Johansen technique based on VARs
- Testing for ARCH effects
- Testing for ARCH effects in exchange rate returns using EViews
- Testing for cointegration and modelling cointegrated systems using EViews
- Testing for cointegration in regression a residualsbased approach
- Testing for nonlinearity
- Testing multiple hypotheses the Ftest
- Testing nonlinear restrictions or testing hypotheses about nonlinear models
- Testing the expectations hypothesis of the term structure of interest rates
- Tests for asymmetries in volatility
- Tests of nonnested hypotheses
- The assumptions underlying the classical linear regression model
- The constant term
- The data
- The data generating process the population regression function and the sample regression function
- The diagonal VECH model
- The difference between insample and outofsample forecasts
- The Egarch model
- The estimation of conditional betas
- The exact significance level
- The final word
- The fixed effects model
- The GJR model
- The linear probability model
- The logit model
- The models and results
- The partial autocorrelation function
- The pecking order hypothesis revisited the choice between financing methods
- The population and the sample
- The probability distribution of the least squares estimators
- The probit model
- The random effects model
- The relationship between the t and the Fdistributions
- The research proposal
- The test of significance and confidence interval approaches always give the same conclusion
- The unconditional variance under a GARCH specification
- The variance equation
- Threshold autoregressive models
- Threshold models and the dynamics of the FTSE 100 index and index futures markets
- Ti
- Timefixed effects models
- To
- Triangular systems
- Truncated dependent variable models
- Ts LL s 01 2 Yo
- Types of nonlinear models
- Uk
- Unbiasedness
- Uncovered interest parity
- Uses of GARCHtype models including volatility forecasting
- Using a logit to test the pecking order hypothesis
- Using information criteria to decide on model orders
- Variance reduction techniques
- VARs with exogenous variables
- Vech
- Vector autoregressive models
- Volatility forecasting some examples and results from the literature
- Vt
- What determines whether an equation is identified or not
- What happens if IV or 2SLS are used unnecessarily
- What is an empirical research project and what is it for
- What panel techniques are available
- What was not covered in the book
- Which criterion should be preferred if they suggest different model orders
- Why are tests for nonstationarity necessary
- Working papers and literature on the internet
- X
- X0
- YiA faio iPii yitA m Yn yit2 yst a20J vfti P22 y2ii Y2i Y22Vy2t2
- Yt fi Xf2 ut
- Yt ut 0i uti 02u2 0i ut3 587
- Yt yti ut