Financial Econometrics *

This is an introduction to a five-volume collection of papers on financial econometrics to be published by Edward Elgar Publishers in 2007. Financial econometrics is one of the fastest growing branches of economics today, both in academia and in industry. The increasing sophistication of financial models requires equally sophisticated methods for their empirical implementation, and in recent years financial econometricians have stepped up to the challenge. The toolkit of financial econometrics has grown in size and depth, including techniques such as nonparametric estimation, functional central limit theory, nonlinear time-series models, artificial neural networks, and Markov Chain Monte Carlo methods. In these five volumes, the most influential papers of financial econometrics have been collected, spanning four decades and five distinct subfields: statistical models of asset returns (Volume I), static asset-pricing models (Volume II), dynamic assetpricing models (Volume III), continuous-time methods and market microstructure (Volume IV), and statistical methods and non-standard finance (Volume V). Within each volume, different strands of the literature are weaved together to form a rich and coherent historical perspective on empirical and methodological breakthroughs in financial markets, while covering the major themes of financial econometrics. I thank John Cox and John Heaton for helpful discussions. Harris & Harris Group Professor, Sloan School of Management, MIT, Cambridge, MA 02142–1347. (617) 253-0920 (voice), (617) 891–9783 (fax), [email protected] (email). General Introduction As a discipline, financial econometrics is still in its infancy, and from some economists’ perspective, not a separate discipline at all. However, this is changing rapidly, as the publication of these volumes illustrates. The growing sophistication of financial models requires equally sophisticated methods for their empirical implementation, within academia and in industry, and in recent years financial econometricians have stepped up to the challenge. Indeed, the demand for financial econometricians by investment banks and other financial institutions—not to mention economics departments, business schools, and financial engineering programs throughout the world—has never been greater. Moreover, the toolkit of financial econometrics has also grown in size and sophistication, including techniques such as nonparametric estimation, functional central limit theory, nonlinear time-series models, artificial neural networks, and Markov Chain Monte Carlo methods. What can explain the remarkable growth and activity of this seemingly small subset of econometrics, which is itself a rather esoteric subset of economics? The answer lies in the confluence of three parallel developments in the last half century. The first is the fact that the financial system has become more complex over time, not less. This is an obvious consequence of general economic growth and development in which the number of market participants, the variety of financial transactions, and the sums involved have also grown. As the financial system becomes more complex, the benefits of more highly developed financial technology become greater and greater and, ultimately, indispensable. The second factor is, of course, the set of breakthroughs in the quantitative modeling of financial markets, e.g., financial technology. Pioneered over the past three decades by the giants of financial economics—Fischer Black, John Cox, Eugene Fama, John Lintner, Harry Markowitz, Robert Merton, Franco Modigliani, Merton Miller, Stephen Ross, Paul Samuelson, Myron Scholes, William Sharpe, and others—their contributions laid the remarkably durable foundations on which all of modern quantitative financial analysis is built. Financial econometrics is only one of several intellectual progeny that they have sired. The third factor is a contemporaneous set of breakthroughs in computer technology, including hardware, software, and data collection and organization. Without these computational innovations, much of the financial technology developed over the past fifty years