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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/282942165 The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications Article in Quantitative Finance · November 2015 DOI: 10.1080/14697688.2015.1080486 CITATION READS 1 1,564 1 author: Surekha K.B. Rao Indiana University Northwest 24 PUBLICATIONS 87 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: “It Pays to Go Bayes” View project All content following this page was uploaded by Surekha K.B. Rao on 26 January 2016. The user has requested enhancement of the downloaded file. Quantitative Finance ISSN: 1469-7688 (Print) 1469-7696 (Online) Journal homepage: http://www.tandfonline.com/loi/rquf20 The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications K. Surekha Rao To cite this article: K. Surekha Rao (2015) The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications, Quantitative Finance, 15:11, 1773-1775, DOI: 10.1080/14697688.2015.1080486 To link to this article: http://dx.doi.org/10.1080/14697688.2015.1080486 Published online: 14 Sep 2015. Submit your article to this journal Article views: 99 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=rquf20 Download by: [108.235.17.94] Date: 21 November 2015, At: 09:03 Quantitative Finance, 2015 Vol. 15, No. 11, 1773–1775, http://dx.doi.org/10.1080/14697688.2015.1080486 Downloaded by [108.235.17.94] at 09:03 21 November 2015 Book review © 2014, Wiley The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications, by Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev and Bala G. Arshanapalli with Markus Hoechstoetter, Wiley (2014). ISBN 978-1-118-57320-4. Econometrics has traditionally been the application of statistical methods to the building of models for economic policy and forecasting and for the empirical veriﬁcation of economic theories. When one applies these models and methods of econometrics to ﬁnancial data, one is entering the realm of ﬁnancial econometrics. Since the turn of the century, there has been a remarkable growth in the ﬁeld of ﬁnancial econometrics to the extent that most new developments in the theory of econometrics in the context of time series data were largely the result of its application to ﬁnancial data. Developments of new techniques and technologies have given a new dimension to the analysis of ﬁnancial data in the last couple of decades. This led to new books on ﬁnancial econometrics and time series analysis of ﬁnancial data. Most of these early books on ﬁnancial econometrics were naturally inclined towards developing the latest theoretical developments and presenting advanced techniques. Unfortunately, because of the relatively cutting-edge nature of the new work, several of these good books on ﬁnancial data analysis modelling and ﬁnancial econometrics (such as those by Campbell et al. (1996), Gourieroux and Jasiak (2001), and those written by permutations and combinations of subsets of the authors of the book under review) remained inaccessible to most practicing asset managers and security analysts. The Basics of Financial Econometrics makes a welcome change in this landscape. This book explains a lot of ﬁnancial and statistical jargon in a way accessible to the layman and provides basic tools and techniques that can be used with most available software without the need for the user to learn complex mathematical and statistical formulae. The book, rather than starting with the usual heavy dose of econometric theory, tries to initiate the reader with real problems and helps build necessary skills to apply the methodologies described in the book to real-world ﬁnancial problems. Even the most uninitiated, non-quantitative Downloaded by [108.235.17.94] at 09:03 21 November 2015 1774 Book review applied ﬁnancial economist will feel comfortable with the intuitive style. The emphasis on practical applications is reﬂected in the plan of the book. Roughly half of the 400+ pages are devoted to explaining concepts from ﬁnance, statistics and econometrics, including 90 pages of appendices on different topics. The second half of the book—the part that makes it most valuable—has excellent examples that apply econometric techniques to real ﬁnancial data, providing model speciﬁcation, parameter estimation and a detailed stepby-step explanation that is most helpful for applied ﬁnancial analysts and fund managers. Here is a detailed analysis of why this book is a very good introduction to applied and basic ﬁnancial econometrics. Chapter 2 on the simple linear regression model has two excellent examples; one dealing with estimating the characteristic line of a fund or a stock and the second example deals with hedging. In the second example, even a non-expert in futures can follow the step-by-step guide in order to calculate the minimum risk hedge ratio and the number of contracts to be sold. The authors explain the terminology: hedging, hedge ratio and number of equivalent market index units, and then explain in detail why a particular hedge was not a perfect hedge. After reviewing this example, the reader can appreciate not only why hedging requires the use of econometric analysis but also why it is not as simple or straightforward as it is often presented in textbooks. The third chapter develops multiple regression models. They are then applied to estimating empirical duration, predicting Treasury yields, estimating Sharpe benchmarks for evaluating fund manager investment style and testing the market efﬁciency hypothesis. In each case, the authors use ﬁnancial market data and provide estimates for the models and their interpretation. The limitations of the models are highlighted. Chapters 4, 5 and 6 are extensions of linear models in a variety of directions. They discuss issues of multicollinearity and choice of variables in model building, speciﬁcation testing and the more advanced tools that should be in the econometric toolkit of a portfolio manager and ﬁnancial analyst. The authors introduce just about enough tests for verifying the assumptions and the right kind of variables that can be carried out by practically any standard econometric software or a statistical regression package. In a model to predict corporate bond yield spreads, for example, you would need to know how to capture the effect of the bond ratings in addition to the coupon rate, the coverage ratio and other factors that are known to impact spreads. The fundamentals of time series analysis are the subject of Chapter 5 which covers the properties of times series and their decomposition with applications to the returns of the S&P 500 index. To deal with such situations in multiple regression models with categorical variables, the authors introduce the use of dummy variables in Chapter 6. However, there is only a limited discussion of dependent categorical variables that necessitate logit and probit models. This may be partly because the most appropriate estimation method used in such situations is the maximum likelihood method, a method covered later in the book. Most ﬁnancial data do not ﬁt into the ideal bell-shaped (i.e. normal) data distribution. To the contrary, real-world ﬁnancial data are skewed or fat tailed, requiring that a fund manager or analyst move beyond the usual regression models with mean predictions to the world of quantile regressions. This topic, covered with excellent description and motivation, is detailed in Chapter 7 along with two realworld examples. The ﬁrst example shows how to identify a portfolio manager’s investment style; the second example explains how management would determine the mixture of debt and equity for a corporation’s capital structure—this, by the way, is one of the few examples not from the ﬁeld of asset management but from ﬁnancial management. If the data have outliers and you are looking for robust results, you have to move beyond linear regression to robust regression estimation that uses Huber or Tukey weighting functions to compute weighted least squares. Chapter 8 describes all the necessary steps to obtain a ‘resistant beta’. Econometric methods have been extensively applied to the analysis of ﬁnancial time series data. Chapters 9, 10 and 11 deal with these topics. In Chapter 9, the authors provide an extensive discussion with examples of autoregressive, moving average and autoregressive moving average models for forecasting returns. If the data show some pattern, technical analysts can follow a step-by-step guide for the entire process of identiﬁcation, estimation, diagnostic testing and forecasting. A nice feature of this chapter is the way the authors introduce vector autoregressive models, leading into the next chapter, Chapter 10, on cointegration. It is generally well known that there are common trends among economic and ﬁnancial variables, such as long-term relationships and occasional ﬁnancial bubbles. This chapter explains how and when a ﬁnancial analyst and a fund manager can systematically test for non-stationarity, random walk, order of lags, unit roots and cointegration (both Engle–Granger and Johansen–Juselius). Using Japanese stock index data, the authors help you identify problems of spurious regressions and obtain empirical results for the dividend growth model using S&P 500 index data. Their study of European stock price linkages using the cointegration methodology examines short-term and long-term dynamics. This is a perfect example of how this book explains the techniques meticulously, and at the same time describes nuances of various interpretations of tests and results. No book on ﬁnancial models can be complete without the study of the generalized autoregressive conditional heteroscedastic model and its various forms because the volatility of future returns is inherently asymmetric. This topic, covered in Chapter 11, provides enough details for the reader to acquire great insight into this topic without being overwhelmed with all the technicalities. Chapter 12 describes a key topic in asset management that fund managers need to understand: identiﬁcation of factors that drive asset returns. Although portfolio theory and asset pricing are topics well developed in ﬁnance textbooks, the authors discuss factor models from a statistical perspective. They suggest moving from the theory of factor models to the identiﬁcation of factors using two econometric methods: Downloaded by [108.235.17.94] at 09:03 21 November 2015 Book review factor analysis and principal components. The differences between these two techniques are explained. Chapter 13 describes advanced methods of model estimation such as the method of moments, instrumental variable regressions and maximum likelihood estimation methods. The last two chapters are focussed on the practice of ﬁnancial econometrics and provide an extensive discussion of implementation issues. These are topics rarely found in books at this level. Once the reader has learnt about econometric techniques described in the earlier chapters, the next important step in practice is to select the model. Chapter 14 explains how this can be done, covering topics such as machine learning, over-ﬁtting, survivorship bias and data snooping. The closing chapters also discuss what is modelling risk and how this can be mitigated. Once a model has been selected using the framework and directions provided in Chapter 14, the next chapter takes the reader through how ﬁnancial econometrics can be used to formulate and implement investment strategies. The section on estimation and modelling of expected returns, the signiﬁcance of out-of-sample prediction and need for minimizing tracking error provides a candid approach to real-life investment strategies. The discussion of the three phases of the quantitative research process given in this last chapter is a must-read for all practitioners. Non-specialists in ﬁnancial econometrics are currently faced with introductory books such as Brooks (2014) or books such as Gourieroux and Jasiak’s (2001), which are advanced reference books with a synthesis of ﬁnancial theory and with statistical methodology suited only for those with considerable mathematical and statistical expertise. The book under review is a good combination of a reference book for practitioners and a textbook for undergraduate students (without the coloured graphs, the practice problems and the solutions manual). It also strikes a happy medium between theory and practice of asset and risk management. In one way, it will remind many readers and practitioners of econometrics the readability and likeness of the book on Basics of Econometrics by Gujarati (1978, 2010) and in another way, it is quite like the book on Practice of Econometrics by Berndt (1996), a book that showed how to apply econometric techniques to a variety of empirical problems and to interpret the results. Now, I address some of the drawbacks. The number of authors (ﬁve) sometimes creates problems of unevenness. Fortunately, many of them have written extensively on the topics covered in this book—especially Fabozzi, who is a proliﬁc author of over 265 research papers and two dozen books, several on ﬁnance. Overall, the book is fairly consistent in its overall style and presentation. For example, every chapter begins with a few learning outcomes enumerated very clearly under the title ‘After reading the chapter you will understand …’ and at the end of every chapter, there are ‘Key Points’ that provide a nice executive summary report that help you recollect and appreciate what all you have just learned and connect back to the outcomes. For all this, there are times when the reader feels sudden shifts in depth and breadth of certain topics and some disconnect because of distinct styles. For example, when reading Chapter 2 (Simple Linear Regression), Chapter 7 View publication stats 1775 (Quantile Regressions) and Chapter 10 (Cointegration), there seems to be a more lucid explanation of concepts and description of results than in other parts of the book. While reading these chapters, the reader feels that someone is holding her hand all the way from beginning till the end of solving the problem and interpreting it in simple terms. And then there are few other chapters on more advanced topics such as Chapter 8 (Robust Regressions) and Chapter 12 (Factor Analysis and Principal Components Analysis), where the style is more rooted in mathematical formulae and theory, even though it is still presented at a basic level. At such times, the reader can feel a bit left on her own, and she must be aware of basic results in statistics and matrix algebra that would not have been required in the more tutorial chapters. As a consequence, the reader can feel like hitting some road bumps on an otherwise very smooth ride of learning and practicing the basic tenets of ﬁnancial econometrics. The second is the complete omission of any kind of reference to Bayesian methods even in those non-standard situations like testing for unit roots, where classical methods have less power or model selection, where there is no uniﬁed approach. For a pragmatic Bayesian like myself, I would have liked to have seen at least a chapter devoted to Bayesian methods. This would have given a choice to the practitioners in non-standard situations. This was even more surprising because two of the ﬁve authors, Rachev and Fabozzi, have co-authored a book titled Bayesian Methods in Finance (2008). Overall, this book is a useful introduction to the theory and practice of ﬁnancial econometrics. It is refreshing and fulﬁls a long-standing need for an introductory-level book on the econometrics of applied portfolio management and ﬁnancial analysis. In doing so, it helps to close the gap between portfolio selection theory and real-world asset management. References Berndt, E.R., Practice of Econometrics: Classic and Contemporary, 1996 (Addison-Wesley Longman Incorporated: Reading, MA). Brooks, C., Introductory Econometrics for Finance, 3rd edition, 2014 (Cambridge University Press: Cambridge, UK). Campbell, J.Y., Lo, A.W. and MacKinlay, A.C., The Econometrics of Financial Markets, 1996 (Princeton University Press: Princeton, NJ). Gourieroux, C. and Jasiak, J., Financial Econometrics: Problems, Models and Methods, 2001 (Princeton University Press: Princeton, NJ). Gujarati, D., Basic Econometrics, 1978, 2010 (McGraw-Hill: New York, NY). Rachev, S.T., Hsu, J.S.J. Bagasheva, B. S. and Fabozzi, F. J., Bayesian Methods in Finance, 2008 (Wiley: Hoboken, NJ). K. Surekha Rao School of Business and Economics, Indiana University Northwest, Gary, IN, USA © 2015, K. Surekha Rao