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xii | |
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xv | |
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xviii | |
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xx | |
| Preface |
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xxi | |
| Acknowledgements |
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xxv | |
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1 | (14) |
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1 | (1) |
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Is financial econometrics different from `economic econometrics'? Some stylised characteristics of financial data |
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2 | (2) |
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4 | (2) |
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Returns in financial modelling |
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6 | (2) |
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Steps involved in formulating an econometric model |
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8 | (2) |
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Some points to consider when reading articles in the empirical financial literature |
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10 | (1) |
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Outline of the remainder of this book |
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11 | (4) |
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Econometric packages for modelling financial data |
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15 | (27) |
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What packages are available? |
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15 | (1) |
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16 | (1) |
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Accomplishing simple tasks using the two packages |
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17 | (1) |
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18 | (13) |
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31 | (8) |
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39 | (3) |
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Appendix: economic software package suppliers |
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40 | (2) |
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A brief overview of the classical linear regression model |
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42 | (91) |
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What is a regression model? |
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42 | (1) |
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Regression versus correlation |
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43 | (1) |
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43 | (9) |
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52 | (3) |
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The assumptions underlying the classical linear regression model |
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55 | (1) |
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Properties of the OLS estimator |
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56 | (2) |
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Precision and standard errors |
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58 | (6) |
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An introduction to statistical inference |
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64 | (18) |
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Generalising the simple model to multiple linear regression |
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82 | (1) |
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83 | (2) |
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How are the parameters (the elements of the β vector) calculated in the generalised case? |
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85 | (3) |
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A special type of hypothesis test: the t-ratio |
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88 | (1) |
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Data mining and the true size of the test |
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89 | (1) |
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An example of the use of a simple t-test to test a theory in finance: can US mutual funds beat the market? |
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90 | (3) |
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Can UK unit trust managers beat the market? |
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93 | (2) |
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The overreaction hypothesis and the UK stock market |
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95 | (7) |
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Testing multiple hypotheses: the F-test |
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102 | (6) |
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Sample EViews and RATS instructions and output for simple linear regression |
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108 | (25) |
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Appendix: mathematical derivations of CLRM results |
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122 | (1) |
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Deriving the OLS coefficient estimator in the bivariate case |
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122 | (1) |
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Derivation of the OLS standard error estimators for the intercept and slope in the bivariate case |
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123 | (4) |
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Derivation of the OLS coefficient estimator in the multiple regression context |
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127 | (1) |
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Derivation of the OLS standard error estimator in the multiple regression context |
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128 | (5) |
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Further issues with the classical linear regression model |
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133 | (96) |
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Goodness of fit statistics |
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133 | (6) |
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139 | (3) |
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Tests of non-nested hypotheses |
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142 | (2) |
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Violations of the assumptions of the classical linear regression model |
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144 | (2) |
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146 | (1) |
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Assumption 2: var(ut) = σ2 < ∞ |
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147 | (8) |
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Assumption 3: cov(ui, uj) = 0 for i ≠ j |
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155 | (23) |
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Assumption 4: the xt are non-stochastic |
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178 | (1) |
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Assumption 5: the disturbances are normally distributed |
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178 | (12) |
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190 | (4) |
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Adopting the wrong functional form |
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194 | (3) |
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Omission of an important variable |
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197 | (1) |
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Inclusion of an irrelevant variable |
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198 | (1) |
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Parameter stability tests |
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198 | (10) |
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A strategy for constructing econometric models and a discussion of model-building philosophies |
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208 | (3) |
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Determinants of sovereign credit ratings |
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211 | (18) |
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Appendix: a brief introduction to principal components analysis |
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220 | (2) |
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An application of principal components to interest rates |
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222 | (3) |
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Calculating principal components in practice |
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225 | (4) |
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Univariate time series modelling and forecasting |
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229 | (73) |
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229 | (1) |
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Some notation and concepts |
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230 | (5) |
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235 | (4) |
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239 | (8) |
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The partial autocorrelation function |
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247 | (2) |
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249 | (6) |
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Building ARMA models: the Box--Jenkins approach |
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255 | (3) |
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Example: constructing ARMA models in EViews |
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258 | (10) |
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Estimating ARMA models with RATS |
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268 | (4) |
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Examples of time series modelling in finance |
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272 | (3) |
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275 | (2) |
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Forecasting in econometrics |
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277 | (14) |
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Forecasting using ARMA models in EViews |
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291 | (2) |
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Forecasting using ARMA models in RATS |
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293 | (2) |
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Estimating exponential smoothing models using EViews and RATS |
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295 | (7) |
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302 | (65) |
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302 | (2) |
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Simultaneous equations bias |
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304 | (2) |
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So how can simultaneous equations models be validly estimated? |
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306 | (1) |
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Can the original coefficients be retrieved from the πs? |
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306 | (3) |
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Simultaneous equations in finance |
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309 | (1) |
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A definition of exogeneity |
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310 | (3) |
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A special case: a set of equations that looks like a simultaneous equations system, but isn't |
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313 | (1) |
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Estimation procedures for simultaneous equations systems |
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313 | (4) |
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An application of a simultaneous equations approach in finance: modelling bid-ask spreads and trading activity in the S&P 100 index options market |
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317 | (6) |
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Simultaneous equations modelling using EViews and RATS |
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323 | (5) |
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328 | (2) |
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Vector autoregressive models |
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330 | (6) |
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Does the VAR include contemporaneous terms? |
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336 | (2) |
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Block significance and causality tests |
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338 | (2) |
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VARs with exogenous variables |
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340 | (1) |
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Impulse responses and variance decompositions |
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340 | (3) |
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An example of the use of VAR models: the interaction between property returns and the macroeconomy |
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343 | (8) |
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VAR estimation in RATS and EViews |
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351 | (16) |
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Modelling long-run relationships in finance |
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367 | (70) |
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Stationarity and unit root testing |
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367 | (16) |
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Testing for unit roots in EViews |
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383 | (3) |
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Testing for unit roots in RATS |
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386 | (1) |
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387 | (2) |
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Equilibrium correction or error correction models |
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389 | (2) |
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Testing for cointegration in regression: a residuals-based approach |
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391 | (2) |
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Methods of parameter estimation in cointegrated systems |
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393 | (2) |
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Lead-lag and long-term relationships between spot and futures markets |
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395 | (8) |
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Testing for and estimating cointegrating systems using the Johansen technique based on VARs |
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403 | (6) |
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409 | (2) |
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Cointegration between international bond markets |
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411 | (7) |
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Testing the expectations hypothesis of the term structure of interest rates |
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418 | (2) |
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Testing for cointegration and modelling cointegrated systems using EViews and RATS |
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420 | (17) |
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Modelling volatility and correlation |
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437 | (96) |
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Motivations: an excursion into non-linearity land |
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437 | (4) |
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441 | (1) |
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441 | (1) |
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Implied volatility models |
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442 | (1) |
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Exponentially weighted moving average models |
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442 | (2) |
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Autoregressive volatility models |
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444 | (1) |
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Autoregressive conditionally heteroscedastic (ARCH) models |
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445 | (7) |
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Generalised ARCH (GARCH) models |
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452 | (3) |
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Estimation of ARCH/GARCH models |
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455 | (13) |
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Extensions to the basic GARCH model |
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468 | (1) |
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469 | (1) |
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469 | (1) |
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470 | (1) |
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471 | (1) |
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Estimating GJR and EGARCH models using RATS |
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472 | (2) |
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Tests for asymmetries in volatility |
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474 | (6) |
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480 | (2) |
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Uses of GARCH-type models including volatility forecasting |
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482 | (8) |
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Testing non-linear restrictions or testing hypotheses about non-linear models |
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490 | (3) |
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Volatility forecasting: some examples and results from the literature |
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493 | (8) |
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Stochastic volatility models revisited |
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501 | (1) |
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Forecasting covariances and correlations |
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502 | (1) |
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Covariance modelling and forecasting in finance: examples of model uses |
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503 | (2) |
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Historical covariance and correlation |
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505 | (1) |
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Implied covariance models |
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505 | (1) |
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Exponentially weighted moving average models for covariances |
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506 | (1) |
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Multivariate GARCH models |
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506 | (4) |
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A multivariate GARCH model for the CAPM with time-varying covariances |
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510 | (2) |
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Estimating a time-varying hedge ratio for FTSE stock index returns |
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512 | (4) |
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Estimating multivariate GARCH models using RATS and EViews |
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516 | (17) |
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Appendix: parameter estimation using maximum likelihood |
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526 | (7) |
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533 | (44) |
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533 | (3) |
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Seasonalities in financial markets: introduction and literature review |
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536 | (1) |
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Modelling seasonality in financial data |
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537 | (8) |
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Estimating simple piecewise linear functions |
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545 | (1) |
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546 | (3) |
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An application of Markov switching models to the gilt--equity yield ratio |
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549 | (9) |
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Estimation of Markov switching models in RATS |
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558 | (1) |
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Threshold autoregressive models |
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559 | (2) |
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Estimation of threshold autoregressive models |
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561 | (2) |
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Specification tests in the context of Markov switching and threshold autoregressive models: a cautionary note |
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563 | (1) |
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An example of applying a SETAR model to the French franc--German mark exchange rate |
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564 | (3) |
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Threshold models and the dynamics of the FTSE 100 stock index and stock index futures market |
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567 | (4) |
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A note on regime switching models and forecasting accuracy |
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571 | (1) |
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Estimating threshold autoregressive models in RATS |
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571 | (6) |
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577 | (55) |
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577 | (1) |
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578 | (2) |
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Variance reduction techniques |
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580 | (5) |
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585 | (4) |
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589 | (1) |
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Disadvantages of the simulation approach to econometric or financial problem solving |
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590 | (2) |
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An example of the use of Monte Carlo simulation in econometrics: deriving a set of critical values for a Dickey--Fuller test |
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592 | (9) |
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An example of how to simulate the price of a financial option |
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601 | (11) |
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An example of the use of bootstrapping to calculate capital risk requirements |
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612 | (20) |
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Conducting empirical research or doing a project or dissertation in finance |
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632 | (13) |
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What is an empirical research project, and what is it for? |
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632 | (1) |
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633 | (3) |
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Working papers and literature on the Internet |
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636 | (1) |
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636 | (3) |
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Choice of computer software |
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639 | (1) |
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How might the finished project look? |
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639 | (4) |
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643 | (2) |
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Recent and future developments in the modelling of financial time series |
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645 | (10) |
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645 | (1) |
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What was not covered in the book |
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645 | (6) |
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Financial econometrics: the future? |
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651 | (3) |
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654 | (1) |
| Appendix 1 A review of some fundamental mathematical and statistical concepts |
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655 | (13) |
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655 | (1) |
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A.2 Characteristics of probability distributions |
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655 | (2) |
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A.3 Properties of logarithms |
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657 | (1) |
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A.4 Differential calculus |
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657 | (3) |
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660 | (5) |
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A.6 The eigenvalues of a matrix |
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665 | (3) |
| Appendix 2 Tables of statistical distributions |
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668 | (12) |
| References |
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680 | (13) |
| Index |
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693 | |
Other versions by this Author