Foreword Preface Acknowledgments 1 Synopsis 2 Introduction to finance 3 Derivative securities 4 Hamiltonians and stock options 5 Path integrals and stock options 6 Stochastic interest rates' Hamiltonians and path integrals 7 Quantum field theory of forward interest rates 8 Empirical forward interest rates and field theory models 9 Field theory of Treasury Bonds' derivatives and hedging 10 Field theory Hamiltonian of forward interest rates 11 Conclusions Brief glossary of financial terms Brief glossary of physics terms List of main symbols References Index
5 Univariate time series modelling and forecasting 206 5.1 Introduction 206 5.2 Some notation and concepts 207 5.3 Moving average processes 211 5.4 Autoregressive processes 215 5.5 The partial autocorrelation function 222 5.6 ARMA processes 223 5.7 Building ARMA models: the Box--Jenkins approach 230 5.8 Constructing ARMA models in EViews 234 5.9 Examples of time series modelling in finance 239 5.10 Exponential smoothing 241 5.11 Forecasting in econometrics 243 5.12 Forecasting using ARMA models in EViews 256 5.13 Estimating exponential smoothing models using EViews 258
2.10 A special type of hypothesis test: the t-ratio 65 2.11 An example of the use of a simple t-test to test a theory in finance: can US mutual funds beat the market? 67 2.12 Can UK unit trust managers beat the market? 69 2.13 The overreaction hypothesis and the UK stock market 71 2.14 The exact significance level 74 2.15 Hypothesis testing in EViews -- example 1: hedging revisited 75 2.16 Estimation and hypothesis testing in EViews -- example 2: the CAPM 77 Appendix: Mathematical derivations of CLRM results 81 3 Further development and analysis of the classical linear regression model 88 3.1 Generalising the simple model to multiple linear regression 88 3.2 The constant term 89 3.3 How are the parameters (the elements of the β vector) calculated in the generalised case? 91 3.4 Testing multiple hypotheses: the F-test 93 3.5 Sample EViews output for multiple hypothesis tests 99 3.6 Multiple regression in EViews using an APT-style model 99 3.7 Data mining and the true size of the test 105 3.8 Goodness of fit statistics 106 3.9 Hedonic pricing models 112 3.10 Tests of non-nested hypotheses 115 Appendix 3.1: Mathematical derivations of CLRM results 117 Appendix 3.2: A brief introduction to factor models and principal components analysis 120
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