Chapter 8 This covers the important topic of volatility and correlation modelling and forecasting . This chapter starts by discussing in general terms the issue of non-linearity in financial time series . The class of ARCH (AutoRegressive Conditionally Heteroscedastic) models and the motivation for this formulation are then discussed. Other models are also presented, including extensions of the basic model such as GARCH, GARCH-M, EGARCH and GJR formulations . Examples of the huge number of applications are discussed, with particular reference to stock returns. Multivariate GARCH models are described, and applications to the estimation of conditional betas and time-varying hedge ratios, and to financial risk measurement, are given. Chapter 9 This discusses testing for and modelling regime shifts or switches of behaviour in financial series that can arise from changes in government policy, market trading conditions or microstructure , among other causes. This chapter introduces the Markov switching approach to dealing with regime shifts. Threshold autoregression is also discussed, along with issues relating to the estimation of such models. Examples include the modelling of exchange rates within a managed floating environment, modelling and forecasting the gilt--equity yield ratio, and models of movements of the difference between spot and futures prices. Chapter 10 This new chapter focuses on how to deal appropriately with longitudinal data -- that is, data having both time series and cross-sectional dimensions. Fixed effect and random effect models are explained and illustrated by way of examples on banking competition in the UK and on credit stability in Central and Eastern Europe. Entity fixed and time-fixed effects models are elucidated and distinguished. Chapter 11 The second new chapter describes various models that are appropriate for situations where the dependent variable is not continuous. Readers will learn how to construct, estimate and interpret such models, and to distinguish and select between alternative specifications. Examples used include a test of the pecking order hypothesis in corporate finance and the modelling of unsolicited credit ratings. Chapter 12 This presents an introduction to the use of simulations in econometrics and finance . Motivations are given for the use of repeated sampling, and a distinction is drawn between Monte Carlo simulation and bootstrapping .The reader is shown how to set up a simulation, and examples are given in options pricing and financial risk management to demonstrate the usefulness of these techniques. Chapter 13 This offers suggestions related to conducting a project or dissertation in empirical finance. It introduces the sources of financial and economic data available on the Internet and elsewhere, and recommends relevant online information and literature on research in financial markets and financial time series. The chapter also suggests ideas for what might constitute a good structure for a dissertation on this subject, how to generate ideas for a suitable topic, what format the report could take, and some common pitfalls. Chapter 14 This summarises the book and concludes. Several recent developments in the field, which are not covered elsewhere in the book, are also mentioned. Some tentative suggestions for possible growth areas in the modelling of financial time series are also given.
Points to consider when reading a published paper (1) Does the paper involve the development of a theoretical model or is it merely a technique looking for an application so that the motivation for the whole exercise is poor? (2) Are the data of ‘good quality’? Are they from a reliable source? Is the size of the sample sufficiently large for the model estimation task at hand? (3) Have the techniques been validly applied? Have tests been conducted for possible violations of any assumptions made in the estimation of the model? (4) Have the results been interpreted sensibly? Is the strength of the results exaggerated? Do the results actually obtained relate to the questions posed by the author(s)? Can the results be replicated by other researchers? (5) Are the conclusions drawn appropriate given the results, or has the importance of the results of the paper been overstated?
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