Thursday, July 2, 2020

Outstanding New Financial Econometrics Book

Financial Econometric Modelling, by Stan Hurn, Vance L. Martin, Peter C. B. Phillips, and Jun Yu, has now been published by Oxford University Press, here.

It's very, very well done.  The depth, breadth, and clarity of coverage are exceptional.  It's completely up to date, very "2020".  See for yourself; the front and back matter appear below (sans formatting).

Financial Econometric Modelling

Stan Hurn, Vance L. Martin, Peter C. B. Phillips, and Jun Yu
November 12, 2019

Preface

Financial econometrics is an exciting young discipline that began to take on
its present form around the turn of the millennium. The subject brings financial
theory and econometric methods together with the power of data
to advance our understanding of the global financial universe upon which
all modern economies depend. Two major developments underscored its
rapid growth and expanding capabilities. First, the massive importance of
well-functioning financial markets to the global economy and to global financial
stability was universally acknowledged following the dot-com bubble of
the late 1990s in the United States and the global financial crisis of 2008 coupled
with its prolonged aftermath. Second, modern methods of econometrics
emerged that proved equal to some of the special challenges presented by financial
data and the ideas of financial theory.

Among the most significant of these challenges are the complex interdependencies
of financial, commodity, and real estate markets, the dynamic and
spatial linkages within financial data, the random wandering behaviour of asset
prices, anomalies such as financial bubbles and market crashes in the data,
the difficulties in modelling rapidly changing volatility in financial returns,
the growth in high dimensional ultra-high frequency data, and the attention
to market microstructure effects that all such data require. While not entirely
unique to financial data, these challenges presented the econometrics profession
with the need to re-fashion methods, develop new tools of inference,
and tackle a wide selection of new empirical goals associated with a growing
number of financial instruments and vast data sets being generated in the
financial world.

This book, like the subject itself, is motivated by all of these challenges. We
seek to provide a broad and gentle introduction to this rapidly developing
subject of financial econometrics where theory, measurement, and data play
equal roles in our development and where empirical applications occupy a
central position. Our target audiences are intermediate and advanced undergraduate
students, honours students who wish to learn about financial econometrics,
and postgraduate students with limited backgrounds in econometrics
who are doing masters courses designed to offer an introduction to finance
and its applications. We hope the book will also prove useful to practitioners
in the industry as an introductory reference source for relevant tools and
approaches to modern empirical work in finance.
Throughout the book special emphasis is placed on the exposition of core
concepts, their illustration using relevant financial data sets, and a hands-on
approach to learning by doing that involves practical implementation. The
guiding principle we have adopted is that only by working through plenty of
applications and exercises can a coherent understanding of the properties of
financial econometric models, interrelationships with the underlying finance
theory, and the role of econometric tools of inference be achieved.

Our philosophy has been to write a book on financial econometrics, not an
econometrics text that illustrates techniques with datasets drawn from finance.
Our goal is centred on the subject of financial econometrics explaining
how evidenced-based research in applied finance is conducted. Econometrics
is viewed as the vehicle that makes the ideas and theories about financial
markets face the reality of observations.
To ensure the book is self contained for a first course in financial econometrics,
some foundational theory and methods of relevant econometric technique
are provided. But the methods covered in this book travel along a customised
path designed to ease the reader’s transition from concepts and methods
to empirical work. The book tracks its way forward from data to modelling
through to estimation, inference, and prediction.
A consistent thematic throughout the book is to motivate each topic with
the presentation of relevant data. The journey begins with data and a simple
grounding in regression and inference. From this foundation, it moves on to
more advanced financial econometric methods that open up empirical applications
with many different types of data from various financial markets. The
path promises to keep readers motivated throughout their journey by means
of many examples and to reinforce their learning by extensive data-based exercises.
Several introductory Appendices are included to assist students with
limited mathematics and no econometric background in understanding more
technical aspects of the discussion particularly in the second half of the book.

Organisation of the Book
Part I – Fundamentals – is designed to form the basis of a semester long first
course in financial econometrics directed at an introductory level. Technical
difficulty is kept to an absolute minimum with an emphasis on the data, financial
concepts, appropriate econometric methodology, and the intuition
that draws these essential components of modelling together. Methodology
is largely confined to descriptive methods and ordinary least squares regression,
a choice that limits the extent of the analysis and promotes heuristic discussion
on some topics which are revisited later in the book for a more complete
and rigorous development.
In Part II – Methods – the level of difficulty steps up slightly in treating the
relevant econometric estimation methods of instrumental variables, generalised
method of moments, and maximum likelihood. These core estimators
are used extensively throughout the second half of the book and knowledge
of them is a key asset in working through the later material. Also included in
Part II are methods that deal with panel data and models with latent factors.
A second course in financial econometrics might usefully begin with these
five chapters, taking Part I as a given foundation.
Part III – Topics – introduces a number of special topics in financial econometiv
rics, covering models of volatility, financial market microstructure, the econometrics
of options, and methods relating to extreme values and copulas. One
of the dominant features of financial time series is their volatility. Financial
theory and empirical experience both demonstrate that there is often much
less to explain in the levels of financial returns than there is to explain in their
variation. Accordingly, three chapters of the book are devoted to modelling
volatility. These chapters treat parametric univariate and multivariate models
of volatility and introduce the more recent nonparametric modelling approach
that is based on market realised volatility measured using high frequency
data.
As in any project of this nature, sacrifices were made to keep the length of the
book manageable. Some topics, for instance, are treated by example and illustration
within a chapter rather than by devoting an entire chapter to their
development. As a result the book is rich in real world examples drawn from
financial markets for stocks, fixed income securities, exchange rates, derivatives,
and real estate. As such, the coverage is intended to be extensive while
not treating every topic in the same depth.
Computation
A fundamental principle guiding the inclusion of material in this book is
whether the methods are available for easy implementation. In consequence,
all results reported in the book may be reproduced using existing software
packages like Stata and EViews.1 This choice is intended to enhance the usefulness
of the material for beginning students. In some cases the programming
languages in these packages need to be used to achieve full implementation
of the illustrations. Of course, for those who actively choose to learn by
programming themselves, the results are also reproducible in any of the common
matrix programming languages, such as R2. The numerical computations
reported in the book are primarily rounded versions of the results generated
using Stata.
The data files are all available for download from the book’s companion website
(https://global.oup.com/academic/instructors/finects) in Stata
format (.dta), EViews format (.wf1), comma delimited files (.csv), and as Excel
spreadsheets (.xlsx).
1Stata is the copyright of StataCorp LP www.stata.com and EViews is the copyright of IHSInc.
www.eviews.com.
2R is a free software environment for statistical computation and graphics which is part of the
GNU Project, see www.r-project.org.

Acknowledgements
Many colleagues, students, and research assistants have read and commented
on parts of the book and in some cases even taught from early versions of the
book. We are particularly grateful to Ahmad Bahir, Kit Baum, Jimmy Chan,
Han Chen, Ye Chen, Xiaohong Chen, Jieyang Chong, Adam Clements, Fulvio
Corsi, Mark Doolan, Ren´ee Fry-McKibbin, Zhuo Huang, Marko Krause,
Bei Luo, Cheng Liu, Andrew Patton, Shuping Shi, Daniel Smith, Chrismin
Tang, Timo Ter¨asvirta, Stephen Thiele, Tomasz Wo´zniak and Lina Xu. A special
thank you goes to Annastiina Silvennoinen and Glen Wade who were relentless
in picking up typographical errors and factual inconsistencies, as well
as suggesting alternative ways of presenting material. All remaining errors
are the responsibility of the authors.
Stan Hurn, Vance L. Martin, Peter C. B. Phillips and Jun Yu
June 2019

Contents

I Fundamentals 1
1 Prices and Returns 3
1.1 What Is Financial Econometrics? . . . . . . . . . . . . . . . . . . 3
1.2 Financial Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Equity Prices and Returns . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Stock Market Indices . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.5 Bond Yields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2 Financial Data 29
2.1 A First Look at the Data . . . . . . . . . . . . . . . . . . . . . . . 30
2.2 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3 Percentiles and Value at Risk . . . . . . . . . . . . . . . . . . . . 45
2.4 The Efficient Market Hypothesis . . . . . . . . . . . . . . . . . . 48
2.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3 Linear Regression 57
3.1 The Capital Asset Pricing Model . . . . . . . . . . . . . . . . . . 58
3.2 A Multifactor CAPM . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3 Properties of Ordinary Least Squares . . . . . . . . . . . . . . . . 67
3.4 Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.5 Measuring Portfolio Performance . . . . . . . . . . . . . . . . . . 80
3.6 Minimum Variance Portfolios . . . . . . . . . . . . . . . . . . . . 82
3.7 Event Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4 Stationary Dynamics 95
4.1 Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.2 Univariate Time Series Models . . . . . . . . . . . . . . . . . . . 98
4.3 Autocorrelation and Partial Autocorrelations . . . . . . . . . . . 103
4.4 Mean Aversion and Reversion in Returns . . . . . . . . . . . . . 107
4.5 Vector Autoregressive Models . . . . . . . . . . . . . . . . . . . . 109
4.6 Analysing VARs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.7 Diebold-Yilmaz Spillover Index . . . . . . . . . . . . . . . . . . . 126
4.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5 Nonstationarity 133
5.1 The RandomWalk with Drift . . . . . . . . . . . . . . . . . . . . 134
5.2 Characteristics of Financial Data . . . . . . . . . . . . . . . . . . 137
5.3 Dickey-Fuller Methods and Unit Root Testing . . . . . . . . . . . 140
5.4 Beyond the Simple Unit Root Framework . . . . . . . . . . . . . 147
5.5 Asset Price Bubbles . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

6 Cointegration 161
6.1 The Present Value Model and Cointegration . . . . . . . . . . . . 162
6.2 Vector Error Correction Models . . . . . . . . . . . . . . . . . . . 167
6.3 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
6.4 Cointegration Testing . . . . . . . . . . . . . . . . . . . . . . . . . 180
6.5 Parameter Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
6.6 Cointegration and the Gordon Model . . . . . . . . . . . . . . . 187
6.7 Cointegration and the Yield Curve . . . . . . . . . . . . . . . . . 192
6.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

7 Forecasting 205
7.1 Types of Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
7.2 Forecasting Univariate Time Series Models . . . . . . . . . . . . 208
7.3 Forecasting Multivariate Time Series Models . . . . . . . . . . . 212
7.4 Combining Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . 216
7.5 Forecast Evaluation Statistics . . . . . . . . . . . . . . . . . . . . 220
7.6 Evaluating the Density of Forecast Errors . . . . . . . . . . . . . 224
7.7 Regression Model Forecasts . . . . . . . . . . . . . . . . . . . . . 229
7.8 Predicting the Equity Premium . . . . . . . . . . . . . . . . . . . 230
7.9 Stochastic Simulation of Value at Risk . . . . . . . . . . . . . . . 237
7.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

II Methods 247

8 Instrumental Variables 249
8.1 The Exogeneity Assumption . . . . . . . . . . . . . . . . . . . . . 250
8.2 Estimating the Risk-Return Tradeoff . . . . . . . . . . . . . . . . 251
8.3 The General Instrumental Variables Estimator . . . . . . . . . . 255
8.4 Testing for Endogeneity . . . . . . . . . . . . . . . . . . . . . . . 259
8.5 Weak Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
8.6 Consumption CAPM . . . . . . . . . . . . . . . . . . . . . . . . . 266
8.7 Endogeneity and Corporate Finance . . . . . . . . . . . . . . . . 269
8.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

9 Generalised Method of Moments 277
9.1 Single Parameter Models . . . . . . . . . . . . . . . . . . . . . . . 278
9.2 Multiple Parameter Models . . . . . . . . . . . . . . . . . . . . . 281
9.3 Over-Identified Models . . . . . . . . . . . . . . . . . . . . . . . . 288
9.4 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
9.5 Properties of the GMM Estimator . . . . . . . . . . . . . . . . . . 299
9.6 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
9.7 Consumption CAPM Revisited . . . . . . . . . . . . . . . . . . . 309
9.8 The CKLS Model of Interest Rates . . . . . . . . . . . . . . . . . 312
9.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

10 Maximum Likelihood 323
10.1 Distributions in Finance . . . . . . . . . . . . . . . . . . . . . . . 324
10.2 Estimation by Maximum Likelihood . . . . . . . . . . . . . . . . 331
10.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334
10.4 Numerical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 342
10.5 Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
10.6 Quasi Maximum Likelihood Estimation . . . . . . . . . . . . . . 347
10.7 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
10.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

11 Panel Data Models 365
11.1 Types of Panel Data . . . . . . . . . . . . . . . . . . . . . . . . . . 366
11.2 Reasons for Using Panel Data . . . . . . . . . . . . . . . . . . . . 368
11.3 Two Introductory Panel Models . . . . . . . . . . . . . . . . . . . 372
11.4 Fixed and Random Effects Panel Models . . . . . . . . . . . . . . 377
11.5 Dynamic Panel Models . . . . . . . . . . . . . . . . . . . . . . . . 386
11.6 Nonstationary Panel Models . . . . . . . . . . . . . . . . . . . . . 394
11.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400

12 Latent Factor Models 409
12.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410
12.2 Principal Components . . . . . . . . . . . . . . . . . . . . . . . . 412
12.3 A Latent Factor CAPM . . . . . . . . . . . . . . . . . . . . . . . . 420
12.4 Dynamic Factor Models: the Kalman Filter . . . . . . . . . . . . 423
12.5 A Parametric Approach to Factors . . . . . . . . . . . . . . . . . 431
12.6 Stochastic Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . 434
12.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437

III Topics 445

13 Univariate GARCH Models 447
13.1 Volatility Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 448
13.2 The GARCH Model . . . . . . . . . . . . . . . . . . . . . . . . . . 450
13.3 Asymmetric Volatility Effects . . . . . . . . . . . . . . . . . . . . 458
13.4 Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460
CONTENTS xi
13.5 The Risk-Return Tradeoff . . . . . . . . . . . . . . . . . . . . . . 465
13.6 Heatwaves and Meteor Showers . . . . . . . . . . . . . . . . . . 467
13.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470

14 Multivariate GARCH Models 477
14.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478
14.2 Early Covariance Estimators . . . . . . . . . . . . . . . . . . . . . 480
14.3 The BEKK Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 482
14.4 The DCC Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487
14.5 Optimal Hedge Ratios . . . . . . . . . . . . . . . . . . . . . . . . 493
14.6 Capital Ratios and Financial Crises . . . . . . . . . . . . . . . . . 495
14.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

15 Realised Variance and Covariance 505
15.1 High Frequency Data . . . . . . . . . . . . . . . . . . . . . . . . . 506
15.2 Realised Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . 509
15.3 Integrated Variance . . . . . . . . . . . . . . . . . . . . . . . . . . 512
15.4 Microstructure Noise . . . . . . . . . . . . . . . . . . . . . . . . . 515
15.5 Bipower Variation and Jumps . . . . . . . . . . . . . . . . . . . . 518
15.6 Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 522
15.7 The Realised GARCH Model . . . . . . . . . . . . . . . . . . . . 525
15.8 Realised Covariance . . . . . . . . . . . . . . . . . . . . . . . . . 527
15.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532

16 Microstructure Models 537
16.1 Characteristics of High Frequency Data . . . . . . . . . . . . . . 537
16.2 Limit Order Book . . . . . . . . . . . . . . . . . . . . . . . . . . . 538
16.3 Bid Ask Bounce . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540
16.4 Information Content of Trades . . . . . . . . . . . . . . . . . . . . 543
16.5 Modelling Price Movements in Trades . . . . . . . . . . . . . . . 545
16.6 Modelling Durations . . . . . . . . . . . . . . . . . . . . . . . . . 551
16.7 Modelling Volatility in Transactions Time . . . . . . . . . . . . . 555
16.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558

17 Options 565
17.1 Option Pricing Basics . . . . . . . . . . . . . . . . . . . . . . . . . 566
17.2 The Black-Scholes Option Price Model . . . . . . . . . . . . . . . 569
17.3 A First Look at Options Data . . . . . . . . . . . . . . . . . . . . 573
17.4 Estimating the Black-Scholes Model . . . . . . . . . . . . . . . . 574
17.5 Testing the Black-Scholes Model . . . . . . . . . . . . . . . . . . 581
17.6 Option Pricing and GARCH Volatility . . . . . . . . . . . . . . . 583
17.7 The Melick-Thomas Option Price Model . . . . . . . . . . . . . . 585
17.8 Nonlinear Option Pricing . . . . . . . . . . . . . . . . . . . . . . 587
17.9 Using Options to Estimate GARCH Models . . . . . . . . . . . . 588
17.10Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591

18 Extreme Values and Copulas 599
18.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600
18.2 Evidence of Heavy Tails . . . . . . . . . . . . . . . . . . . . . . . 602
18.3 Extreme Value Theory . . . . . . . . . . . . . . . . . . . . . . . . 605
18.4 Modelling Dependence using Copulas . . . . . . . . . . . . . . . 611
18.5 Properties of Copulas . . . . . . . . . . . . . . . . . . . . . . . . . 614
18.6 Estimating Copula Models . . . . . . . . . . . . . . . . . . . . . . 621
18.7 MGARCH Model Using Copulas . . . . . . . . . . . . . . . . . . 624
18.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626

19 Concluding Remarks 633

A Mathematical Preliminaries 635
A.1 Summation Notation . . . . . . . . . . . . . . . . . . . . . . . . . 635
A.2 Expectations Operator . . . . . . . . . . . . . . . . . . . . . . . . 638
A.3 Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639
A.4 Taylor Series Expansions . . . . . . . . . . . . . . . . . . . . . . . 641
A.5 Matrix Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646
A.6 Transposition of a Matrix . . . . . . . . . . . . . . . . . . . . . . . 650
A.7 Symmetric Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . 651

B Properties of Estimators 661
B.1 Finite Sample Properties . . . . . . . . . . . . . . . . . . . . . . . 661
B.2 Asymptotic Properties . . . . . . . . . . . . . . . . . . . . . . . . 663
C Linear Regression Model in Matrix Notation 669
D Numerical Optimisation 673
E Simulating Copulas 677
Author index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698
Subject index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702


Author index
A¨ıt-Sahalia, Y., 341, 517, 518
Adams, R., 269, 270, 382
Akaike, H., 113
Aldrich, J., 323
Almeida, H., 269, 270, 382
Amenc, N., 477
Andersen, T.G, 517
Anderson, D.R., 218
Anderson, R.C., 269
Anderson, T.W., 390
Andreou, E., 237
Arellano, M., 390
Augustin, N.H., 218
Bae, K.H., 599
Bai, J., 419
Baillie, R.T., 493, 494
Baker, R., 262
Bali, T.G., 255
Baltagi, B.H, 400
Banerjee, A., 203
Barndorff-Nielsen, O.E., 508, 515,
518, 520, 521, 529, 538
Bartlett, M.S., 175, 349
Bates, J., 218, 235
Becker, R., 224
Bekaert, G., 67
Bera, A.K., 79, 229
Bergstrom, A.R., 161
Black, F., 569
Blundell, R., 390
Bollerslev, T., 448, 450, 457, 517,
525, 589
Bond, S., 390
Bonhomme, S., 372
Bouchard, J-P., 544
Bound, J., 262
Bover, O., 390
Brennan, M.J., 315
Brockwell, P.J., 98, 106
Brownlees, C., 495, 498
Buckland, S.T., 218
Burnham, K.P., 218
Bykhovskaya, A., 150
Campbell, J.Y., 131, 154, 237
Carhart, M.M., 65
Chan, K.C., 313
Chao, J.C., 179
Cheng, X., 179
Chiang, M.-H., 400
Choi, I., 399
Christoffersen, P.F., 464
Chu, C.-J., 397
Clemen, R.T., 219
Clements, A.E., 224, 469
Clements, M.P., 219
Cochrane, J.H., 48, 237
Colacito, R., 224
Corbae, D., 199
Corsi, F., 522
Cox, J.C., 315, 329
Cram´er, H., 345
Davidson, R., 260
Davis, R.A., 98, 106
Diba, B.T., 154
Dickey, D.A., 140, 141, 146
Diebold, F.X., 24, 126, 132, 223, 229,
431, 441, 517
Dolado, J.J., 203
Doolan, M.B., 224
Dungey, M., 362, 467
Durbin, J., 129, 298
Eisler, Z., 544
Elliott, G., 149, 217, 219, 224
Engle, R.F., 78, 161, 173, 174, 224,
448–450, 467, 471, 487,
489, 495, 498, 542, 552,
555, 556
Epps, T.W., 528
Evans, G.W., 154
Fakhrutdinova, L., 467
Fama, E.F., 48, 64, 107, 403
AUTHOR INDEX 699
Fan, Y., 612
Ferreira, D., 269, 270, 382
Ferson, W.E., 268, 269
Fisher, R.A., 398
Flannery, M.J., 388, 406
Flemming, J., 224
French, K.R., 64, 107
Fuller, W.A., 140, 141
Galbraith, J.W., 203
Garratt, A., 219
Ghosh, A., 229
Ghysels, E., 255
Gibbons, M., 403
Gibson, M., 589
Glosten, L.R., 458
Goltz, F., 477
Goodhart, C.A.E., 467
Gordon, M.J., 187, 190, 201
Goyal, A., 231, 244
Granger, C.W.J., 115, 161, 174, 203,
218, 235
Grossman, H.I., 154
Gunther, T.A., 229
Haldane, A.G., 442
Hall, A.D., 544
Hall, A.R., 300
Hall, S.G., 442
Hamilton, J.D., 98
Hankins, K.W., 388, 406
Hannan, E.J., 113
Hansen, B.E., 174
Hansen, L.P., 298, 300, 302, 303, 311
Hansen, P.R., 462, 508, 518, 525,
529, 538
Harris, D., 98, 122, 292, 350, 427,
548, 587, 662
Harvey, A.C., 427, 493
Harvey, C.R., 268, 269
Hasbrouck, J., 543, 559
Hausman, J.A., 385, 545
Hautsch, N., 544
Heaton, J., 298
Hendry, D.F., 203, 219
Hodrick, R.J., 54
Hoerova, M., 67
Hoyem, K., 67
Hsiao, C., 390
Hu, W-Y., 67
Huang, R., 544
Huang, Z., 525
Hurn, A.S., 98, 122, 224, 292, 350,
427, 469, 548, 587, 662
Hwang, J., 173
Im, K.S., 397
Ingersoll, J.E., 315, 329
Ito, T., 467
Jacobs, J.P.A.M., 362
Jacod, J., 518
Jaeger, D., 262
Jaganathan, R., 458
Jarque, C.M., 79
Jensen, M.C., 80
Johansen, S., 174, 177, 184
Judson, R.A., 388
Kanniainen, J., 588, 589
Kao, C., 400
Kapetanious, G., 219
Karolyi, G.A., 313, 599
Kasparis, I., 237
Kelly, B., 489
Kim, M.J., 107
Kirby, C., 224
Kiviet, J., 388
Kockelkoren, J., 544
Koop, G., 219
Kwiatkowski, D.P., 151
L¨ utkepohl, H., 113, 122
Labhard, V., 219
Labys, P., 517
Lee, C.C., 150
Lee, J.H., 237
Levin, A., 397
Li, C., 24, 431, 441
Li, D.X., 600
Lim, K-G., 199
Lin, B., 588, 589
700 BIBLIOGRAPHY
Lin, C.F., 397
Lin, W.-L., 467
Liu, L., 517
Lo, A.W., 154, 545
Lo, D.K., 544
Longstaff, F.A., 313
Loretan, M., 173
Lunde, A., 462, 508, 518, 525, 529,
538
MacBeth, J.D., 403
MacKenzie, D., 600
MacKinlay, A.C., 154, 545
MacKinnon, J.G., 144, 181, 260
Maddala, G.S., 399
Malmendier, U., 67
Mann, H.B., 95, 109
Manresa, E., 372
Mariano, R.S., 223
Martin, V.L., 98, 122, 292, 350, 427,
548, 587, 662
Melick, W.R., 586
Merton, R.C., 251, 473, 536
Millar, R.B., 345
Mincer, J., 462
Moon, H.R., 400
Myers, R.J., 493, 494
Mykland, P.A., 517, 518
Nagel, S., 67
Nelson, C.R., 107, 431
Nelson, D.B., 459
Newbold, P., 203
Newey, W.K., 175, 349
Ng, S., 147, 419
Nickell, S., 387, 388
Ostdiek, B., 224
Ouliaris, S., 181, 199
Owen, A.L., 388
P´erignon, C., 45
Patton, A.J., 224, 471, 517, 525, 612
Peng, L., 255
Perron, P., 147, 150
Pesaran, M.H., 397
Phillips, P.C.B., 140, 150–152, 156,
173, 174, 179–181, 203,
237, 264, 372, 400
Poterba, J.M., 108
Pratt, J.W., 323
Prescott, E.C., 54
Price, S., 219
Quaedvlieg, R., 525
Quinn, B.G., 113
Rajan, R.G., 393, 406
Ramanathan, R., 218
Reeb, D.M., 269
Reinhart, C.M., 362
Rogoff, K.S., 362
Roley, V.V., 467
Roll, R., 541, 558
Ross, S.A., 315, 329, 403
Rothenberg, T.J., 149
Roulet, J., 54
Runkle, D, 458
Russell, J.R., 542, 552
Said, S.E., 146
Saikkonen, P., 173
Samuelson, P., 48
Sanders, A.B., 313
Santa-Clara, P., 255
Schmidt, P., 151
Scholes, M., 569
Schwartz, E.S., 315
Schwarz, G., 113
Shanken, J., 403
Sharpe,W.F., 80
Shek, H.H., 525
Shephard, N., 508, 515, 518, 520,
521, 529, 538
Sheppard, K., 224, 517
Shi, S., 156
Shi, Z., 372
Shiller, R.J., 67, 131
Shin, Y., 151, 397
Siegel, A.F., 431
Silvennoinen, A., 488, 493
Sims, C.A., 109
Singleton, K.J., 311
Smith, D.R., 45
Smith, J., 219
Solnik, B., 54
Spears, T., 600
Staiger, D., 262, 264
Stambaugh, R.F., 236
Startz, B., 107
Stigler, S.M., 323
Stock, J.H., 149, 173, 219, 262, 264,
274
Stulz, R.M., 599
Su, L., 372
Summers, L.H., 108
Sun, Y., 173
Tang, L., 477
Taylor, S.J., 435
Ter¨asvirta, T., 488, 493
Thiele, S., 493
Thomas, C.P., 586
Timmerman, A., 217, 224
Toda, H.Y., 180
Treynor, J.L., 80
Trivedi, P.K., 612, 678
Tsay, R.S., 229
Tse, Y.K., 488
Tsui, A.K.C., 488
Vahey, S.P., 219
Vaidyanathan, V., 477
Valkanov, R., 255
Van der Vaart, A.W., 345
Van der Waart, J.W., 301
Vasicek, O., 341
Volkov, V.V., 469
Wald, A., 95, 109
Wallis, K.F., 219
Watson, M.W., 173, 219
Welch, I., 231, 244
Wellner, J.A., 301
West, K.D., 175, 349
White, H., 78, 349
Winkler, R.L., 219
Wooldridge, J.M., 457
Wright, J.H., 274
Wu, S., 399
Wu, Y., 152
Yamamoto, Y., 180
Yang, H., 588, 589
Yaron, A., 298
Yilmaz, K., 126, 132
Yogo, M., 237, 264, 274
Yoo, B.S., 173
Yu, J., 152, 156
Zako¨ıan, J.M., 458
Zarnowitz, V., 462
Zhang, L., 517, 518
Zhou, H., 589
Zimmer, D.M., 612, 678
Zingales, L., 393, 406

Subject index
F test of significance, 74
t test of significance, 73
Adjusted coefficient of determination
(R2), 72
Akaike information criterion (AIC),
113
Asset returns
volatility clustering, 448–450
Asymptotic efficiency
GMM, 301
maximum likelihood, 345
ordinary least squares, 69
Asymptotic normality
GMM, 303
maximum likelihood, 346
ordinary least squares, 70
Augmented Dickey-Fuller test,
145–147
GLS detrending, 149
lag length selection, 147
Autocorrelation function, 103
Autocovariance, 103
Autoregressive (AR) model
estimation, 99
specification, 98
Autoregressive conditional duration
model, 551–555
Autoregressive moving average
(ARMA) model
estimation, 102
specification, 101
BFGS algorithm, 675
BHHH algorithm, 674
Bid ask bounce, 540–542
Bipower variation, 518–522
Black-Scholes option pricing model
currency option, 573
equity option, 573
European call option, 569–572
European put option, 572
testing bias, 581
testing heteroskedasticity, 582
testing smiles and smirks, 582
Bollerslev-Wooldridge standard
errors, 457
Bond yields, 36
term structure, 24
yield curve, 24
yield to maturity, 23
Capital asset pricing model (CAPM),
58–63
consumption (C-CAPM), 309
Coefficient of determination (R2),
72
Cointegration, 163
fully modified estimation,
174–177
estimation, 173–180
Gordon model, 187–192
Johansen reduced rank regression
estimator, 177–180
modelling the yield curve,
192–198
present value model, 163–167
testing, 180–187
testing hypotheses on cointegrating
parameters, 186
Consistency
GMM, 301
instrumental variables, 259
maximum likelihood, 345
ordinary least squares, 68
Consumption capital asset pricing
model (C-CAPM), 266–
269
Copulas
t copula, 616
Clayton copula, 617
estimating copula models,
621–624
Frank copula, 618
SUBJECT INDEX 703
Gaussian copula, 614
Gumbel copula, 618
measuring tail dependence,
618–621
modelling dependence using
copulas, 611–614
properties of copulas, 614–621
Diagnostic tests on disturbances
ARCH, 78
autocorrelation, 76
heteroskedasticity, 77
normality, 79
Dickey-Fuller test, 140–145
Diebold-Mariano test, 223
Diebold-Yilmaz Spillover Index,
125–128
Dividends
discounted future stream of,
34
dividend yield, 34
Durations, 39
Dynamic factor models, 423–429
Efficient market hypothesis, 48–51,
134
return predictability, 48
variance ratio, 50
Endogeneity and corporate finance,
269–272
Equilibrium dynamics, 164
Equity prices, 30–32
effect of dividends, 10
effect of stock splits, 11
quoted prices, 8
Event analysis, 85–87
Extreme value distribution
distribution types, 606–607
Hill estimator, 608
maximum likelihood estimation,
609–610
VaR calculation, 611
Extreme value theory, 605–611
Financial assets
cash, 6
derivatives, 7
equities, 7
Eurodollar deposits, 6
fixed-income securities, 6
Treasury bills, 6
Forecasting
AR(1) model, 208–211
AR(2) model, 211
bivariate VAR(1) model, 213–
214
bivariate VECM(1) model,
214–216
combining forecasts, 216–219
density forecast evaluation,
224–229
ex ante forecasts, 206
ex post forecasts, 207
forecast evaluation, 220–224
predictive regressions, 229–
237
properties, 212
Fully modified estimation OLS,
174–177
Gibbons-Ross-Shanken test, 371,
403
Gordon model, 187–192
Granger causality, 115
Hannan information criterion
(HIC), 113
Hansen-Sargan J test, 306
High frequency data
characteristics, 537–538
cleaning, 507–508
limit order book, 538–540
transactions data, 506–509
Idiosyncratic risk, 59
Impulse response analysis, 116
Instrumental variables estimator
multiple endogenous regressors,
258–259
two-variable regression model,
254
Integrated process, 137
704 BIBLIOGRAPHY
Integrated variance, 512–515
Inter-temporal CAPM, 251–255
Invariance
maximum likelihood, 346
Jensen’s alpha, 80
Johansen reduced rank regression
estimator, 177–180
Johansen tests of cointegration,
182–185
Kalman filter
estimation, 428
factor extraction, 428–429
multivariate, 427
univariate, 424–426
Lag length selection
information criteria, 113–114
Lagrange multiplier (LM) test
test for ARCH, 470
Lagrange multiplier test, 351–352
Leptokurtosis, 38
Likelihood ratio test, 351
Limited dependent variables, 545–
551
linear probability model, 547
ordered probit, 549–551
probit, 546–549
Linear regression model, 58
disturbance term diagnostics,
76–80
explanatory variable diagnostics,
73–75
matrix notation, 669–670
Marginal expected shortfall, 495–
498
Mean absolute error (MAE), 221
Mean absolute percentage error
(MAPE), 221
Mean square error (MSE), 221
Measuring portfolio performance
Jensen’s alpha, 80
Sharpe ratio, 80
Treynor ratio, 80
Melick-Thomas option pricing
model, 585–587
Microstructure noise, 515–518
Minimum variance portfolio, 82–
85
Modelling the yield curve, 192–198
Moving average (MA) model
estimation, 100
specification, 100
Multi-factor CAPM
instrumental variables estimation,
255–258
Multifactor CAPM, 63–67
Multiple regression model, 63
Nelson-Siegel parametric factor
model, 431–434
News impact curve (NIC), 459
Newton-Raphson algorithm, 674
Nonlinear option pricing model,
587–588
Nonstationary process, 137
Optimal hedge ratio, 493–495
Options
Black-Scholes option pricing
model, 569–573
data, 573–574
GARCH volatility, 583–585
Greeks, 578–581
historical volatility, 575–576
implied volatility, 576–578
pricing basics, 566–569
Order of integration, 137
Order statistics, 602–603
Ordinary least squares estimator,
60–61
Panel data
Arellano-Bond estimator, 390
common effects model, 374–
377
fixed effects model, 378–380
Hausman test, 383–385
Nickell bias, 386
no common effects model,
372–374
SUBJECT INDEX 705
panel cointegration, 398–400
panel unit roots, 396–398
random effects model, 380–
382
system GMM estimator, 390
Partial autocorrelation function,
106
Percentiles, 45
Predicting the equity premium,
230–237
Principal component analysis,
412–420
estimation, 413–417
factor extraction, 417–418
model specification, 412–413
testing, 419–420
Probability integral transform
(PIT), 224
Properties of GMM estimators,
299–305
Properties of instrumental variable
estimators, 259
Properties of instrumental variables
estimators, 254
Properties of maximum likelihood
estimators, 344–347
Properties of ordinary least squares
estimators, 67–71
Quasi maximum likelihood estimator,
347
Random walk with drift model,
134–137
Realised covariance, 527–531
refresh time synchronisation,
529–531
Realised variance
computing, 509–511
forecasting, 522–525
Residual-based tests of cointegration,
180–182
Returns
effect of dividends, 15
excess returns, 16
continuous compounded returns,
13
dollar returns, 11
log returns, 13, 32
mean aversion and reversion,
107
simple returns, 12
Risk-return tradeoff, 251–255, 465–
467
Root mean square error (RMSE),
221
Safe capital ratio, 495–498
Schwarz information criterion
(SIC), 113
Sharpe ratio, 80
Signature plot, 511
Spurious regression problem, 203
Stationary process
introduced, 96
Stock market index
Deutscher Aktien Index (DAX),
19
Dow Jones Industrial Average
Index (DJIA), 19
Financial Times Stock Exchange
100 Index (FTSE),
19
Hang Seng Index (HSX), 19
Nikkei 225 Index (NKX), 19
price weighted, 19
Standard and Poors Composite
500 Index (S&P 500),
19
value weighted, 19
Strong exogeneity, 187
Summary statistics, 41–45
sample correlation, 44
sample covariance, 44
sample kurtosis, 43
sample mean, 41
sample skewness, 43
sample standard deviation, 42
sample variance, 41
Systematic risk, 59
706 BIBLIOGRAPHY
Term structure of interest rates, 24,
192
Testing for bubbles, 152–157
Testing for endogeneity, 259–261
Transactions data, 39
Treynor ratio, 80
Unit root tests
Augmented Dickey-Fuller
test, 145–147
Dickey-Fuller test, 140–145
GLS detrending, 149–150
KPSS test, 151
Phillips-Peron test, 150
Right-tailed tests, 152–157
Structural breaks, 147
Univariate GARCH model
Bollerslev-Wooldridge standard
errors, 457
estimation, 455–457
forecasting, 460–465
heatwaves and meteor showers,
467–470
normal distribution, 455
t distribution, 456
Value at risk, 45–47, 237–240
Variance decomposition, 122
Vector autoregressive models (VAR),
109–125
Diebold-Yilmaz spillover index,
125
estimation, 111
Granger causality, 115
impulse response analysis,
116
lag length selection, 113–114
specification, 110
transactions time, 543–544
variance decomposition, 122
Vector error correction model
(VECM), 167–173
Relationship with VARs, 171–
173
Volatility
defined, 42
Volatility models
EGARCH, 458
GARCH, 453–460
GARCH-M, 465–467
TARCH, 458
BEKK, 482–486
DCC, 487–493
DECO, 488
exponentially weighted moving
average, 452, 480
historical volatility, 451, 480
IGARCH, 454
in transactions time, 555–558
options data and GARCH,
588–591
realised GARCH, 525–527
stochastic volatility, 434–437
Wald test, 351
Weak exogeneity, 186
Weak instruments, 261–266
White standard errors, 348



Tuesday, June 30, 2020

Entering (and Trying to Exit) the Pandemic Recession

New paper at arXiv:2006.15183 
econ.EM econ.GN
Real-Time Real Economic Activity: Exiting the Great Recession and Entering the Pandemic Recession
AbstractWe study the real-time signals provided by the Aruoba-Diebold-Scotti Index of Business conditions (ADS) for tracking economic activity at high frequency. We start with exit from the Great Recession, comparing the evolution of real-time vintage beliefs to a "final" late-vintage chronology. We then consider entry into the Pandemic Recession, again tracking the evolution of real-time vintage beliefs. ADS swings widely as its underlying economic indicators swing widely, but the emerging ADS path as of this writing (late June) indicates a return to growth in May. The trajectory of the nascent recovery, however, is massively uncertain, particularly as COVID-19 spreads in the South and West, and could be reversed as quickly as it started.
Submitted 26 June, 2020; originally announced June 2020.

Monday, June 29, 2020

Causality and Generalized Impulse Response Functions

Neil Shephard just gave a fine talk, "Econometric analysis of potential outcomes time series:
instruments, shocks, linearity and the causal response function".  Recording here (soon). 
Slides here.  The key result is on slide 11:  If the conditions (Assns 1-3 on slides p. 5) for a potential outcome time series are satisfied, then the Koop-Pesaran-Potter (1996) "generalized impulse response function" (GIRF) has a direct causal interpretation. Neil pitched the paper as providing deeper understanding and firmer foundations for the GIRF, which it certainly does.

Wow! This is wonderful in general, and for me personally:  Throughout almost all my work with Kamil Yilmaz on measuring network connectedness (e.g., here), we work in a GIRF framework for the underlying vector autoregression (actually generalized variance decomposition, but it's the same thing). We liked the GIRF for a pragmatic reason -- its invariance to variable ordering, unlike Cholesky factor identification -- but we always wanted to understand it more deeply.

Thursday, June 25, 2020

Pandemic Economic Forecasting with Mixed-Frequency Data

Nice Schorfheide-Song (SS) Bayesian real-time pandemic economic forecasting with mixed-frequency data here.  They simulate exact posteriors.  ADS nowcasting is also based on exact mixed-frequency estimation (MLE).  So both SS and ADS get things right in principle (SS Bayesian forecasting, ADS frequentist nowcasting), and both are easily implemented in practice.  That is, both are intellectually pure, yet practically relevant.

Monday, June 22, 2020

COVID, Economic Activity, and Climate

Everyone talks about COVID helping reduce warming:
COVID up --> economic activity down --> CO2 down --> temperature down.

But there's a flip side:
COVID up --> activity down --> atmospheric sulphate aerosols down --> temperature UP!
(Sulphate aerosols reflect solar heat, so if they're down, temp is up.

See https://news.mongabay.com/2020/06/climate-conundrum-could-covid-19-be-linked-to-early-arctic-ice-melt/.

It would be interesting to assess the the competing effects of CO2 vs. sulphate aerosols, dynamically.  One might start with impulse-response analysis of an economic activity shock in a predictive model containing economic activity, CO2, sulphate aerosols, and temperature.

Wednesday, June 17, 2020

Time Series Modeling of COVID-19 Paths

Check out the refreshing new paper by Andrew Harvey and Paul Kattuman "Time series models based on growth curves with applications to forecasting coronavirus", pp. 126-156 here.

For non-causal forecasting, reduced-form approaches like those of Harvey-Kattuman are almost always the way to go, from traditional time series modeling to more recent extensions in machine learning. To paraphrase a long-ago No Hesitations post:  We generally don't need deep structural understanding to succeed at forecasting, which is wonderful, because we typically don't have deep structural understanding. (Admit it.) 

Forecasting COVID progression (cases, deaths, etc.) is a fine example. The leading structural ("SIR") model is a toy model, an intentionally stripped-down abstraction of a much more complex reality.  There's nothing wrong with that -- that's what all structural models are, and good structural models can yield invaluable insights. But good forecasting requires capturing the complex reality more fully, with its model uncertainty, measurement uncertainty, parameter uncertainty, innovation uncertainty, structural change uncertainty, etc. That's where reduced-form approaches shine.

On the other hand, because structural models can in principle illuminate the causal mechanisms that underlie reduced-form correlations, they may help with analysis of conterfactuals. That is, structural models may facilitate causal forecasting in addition to non-causal forecasting.

Of course it doesn't have to be an either/or choice.  One can attempt to blend the structural and reduced-form approaches, hoping to achieve the best of both worlds. To that end, see the also-refreshing new paper by Andrew Atkeson et al., "Estimating and forecasting disease scenarios for COVID-19 with an SIR model", here.  

Tuesday, June 16, 2020

SoFiE 2021 San Diego and 2022 Cambridge

Happy to help spread the word that the Society for Financial Econometrics annual conference 2021 will be in San Diego (UCSD) June 15-17, and 2022 will be in Cambridge England (University of Cambridge) June 27-29.  Finally some real in-person meetings, and each location is perfect.  Quite a big deal.  Zoom is hardly a substitute. See you there!

Monday, June 15, 2020

Did the U.S. Recession Start in February or March?

Some seem to think that the NBER declared a February recession start. It did not; rather, it declared a February cyclical peak.

So when did the recession start? It's a bit ambiguous, since a peak is an apex atop both the upward expansion path and the downward contraction path. Indeed the NBER's press release states that "The [February 2020] peak marks the end of the expansion that began in June 2009 and the beginning of a recession."

Nevertheless, when measuring expansion and contraction durations, the NBER convention is that peak months are taken as part of expansions ("the last month of good times"), and trough months are taken as part of contractions ("the last month of bad times"), as in the NBER table here.

Friday, June 12, 2020

Real Economics in Business Strategy Simulation

This spam somehow made it through my filters.  But it looks pretty cool, not really spam.  Maybe my filters are smarter than I thought.

https://scientificstrategy.com

I am certainly not expert in micro / IO / marketing, so maybe I'm far behind the curve, but at any rate the practitioner in me was intrigued by the tools described in the email below.  I have no idea whether they're any good, but it's certainly interesting to see real economics evidently getting in closer touch with the nitty-gritty of practical business decision making.

Hi Professor Diebold:

Model the dynamics of your market with Market Simulation:

Our 100+ example models include:

  • Cournot / Bertrand / Edgeworth / Giffen / Hotelling / Nash
  • Stackelberg Leader-Follower Price Competition
  • Wholesaler-Retailer Double Marginalization / M&A
  • eCommerce / Brick & Mortar
  • Good / Better / Best Product Pricing
  • Learning Curves / Search Costs / Bundling
  • Capacity Limitations / Switching Costs / Cannibalization
  • Conjoint Analysis / New Product Development

Our case studies include:

  • Android vs iOS 
  • Microbrews (6-parts)
  • Cola Wars (7-parts)
  • SUV Market (2-parts)
  • Competitive Strategy Game CSG (2-parts)
  • Porter’s Five Forces (5-parts)
    
Model, analyze, and solve your pricing / product / positioning / placement. Or send in your problem for us to solve.

Happy Simulations!

Ted.

-
Ted Hartnell | CTO
Phone: +1-415-800-4454
Address: 25 Pond Court, Milpitas, CA 95035

More on Conditional Predictive Accuracy Assessment

I recently blogged on the new Zhu-Timmermann paper.  I mentioned that they end on a constructive note for unconditional predictive accuracy comparisons, even if they raise issues for conditional comparisons.  I forgot (until now) about Li, Liao, and Quaedvlieg (2020), one of my favorite recent papers.  (We discussed it at length in my Ph.D. class in April.)  Their setup avoids the Zhu-Timmermann critique and provides an appealing route forward for conditional assessments.