Review Papers and Book Chapters

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Fear, Greed, and Financial Crises: A Cognitive Neurosciences Perspective, 2011
Historical accounts of financial crises suggest that fear and greed are the common denominators of these disruptive events: periods of unchecked greed eventually lead to excessive leverage and unsustainable asset-price levels, and the inevitable collapse results in unbridled fear, which must subside before any recovery is possible. The cognitive neurosciences may provide some new insights into this boom/bust pattern through a deeper understanding of the dynamics of emotion and human behavior. In this chapter, I describe some recent research from the neurosciences literature on fear and reward learning, mirror neurons, theory of mind, and the link between emotion and rational behavior. By exploring the neuroscientific basis of cognition and behavior, we may be able to identify more fundamental drivers of financial crises, and improve our models and methods for dealing with them.
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Reading About the Financial Crisis: A 21-Book Review, 2011
The recent financial crisis has generated many distinct perspectives from various quarters. In this article, I review a diverse set of 21 books on the crisis, 11 written by academics, and 10 written by journalists and one former Treasury Secretary. No single narrative emerges from this broad and often contradictory collection of interpretations, but the sheer variety of conclusions is informative, and underscores the desperate need for the economics profession to establish a single set of facts from which more accurate inferences and narratives can be constructed.
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Stock Market Trading Volume, with Jiang Wang, Handbook of Financial Econometrics, Volume 2, 2010, North-Holland. If price and quantity are the fundamental building blocks of any theory of market interactions, the importance of trading volume in understanding the behavior of financial markets is clear. However, while many economic models of financial markets have been developed to explain the behavior of prices—predictability, variability, and information content—far less attention has been devoted to explaining the behavior of trading volume. In this article, we hope to expand our understanding of trading volume by developing well-articulated economic models of asset prices and volume and empirically estimating them using recently available daily volume data for individual securities from the University of Chicago's Center for Research in Securities Prices. Our theoretical contributions include: (1) an economic definition of volume that is most consistent with theoretical models of trading activity; (2) the derivation of volume implications of basic portfolio theory; and (3) the development of an intertemporal equilibrium model of asset market in which the trading process is determined endogenously by liquidity needs and risk sharing motives. Our empirical contributions include: (1) the construction of a volume/returns database extract of the CRSP volume data; (2) comprehensive exploratory data analysis of both the time-series and cross-sectional properties of trading volume; (3) estimation and inference for price/volume relations implied by asset-pricing models; and (4) a new approach for empirically identifying factors to be included in a linear-factor model of asset returns using volume data. Not available
Efficient Markets Hypothesis, in L. Blume and S. Durlauf, eds., The New Palgrave: A Dictionary of Economics, Second Edition, 2007. New York: Palgrave McMillan. The Efficient Markets Hypothesis (EMH) refers to the notion that market prices fully reflects all available information. Developed independently by Paul A. Samuelson and Eugene F. Fama in the 1960's, this idea has been applied extensively to theoretical models and empirical studies of financial securities prices, generating considerable controversy as well as fundamental insights into the price-discovery process. The most enduring critique comes from psychologists and behavioral economists who argue that the EMH is based on counterfactual assumptions regarding human behavior, i.e., rationality. Recent advances in evolutionary psychology and the cognitive neurosciences may be able to reconcile the EMH with behavioral anomalies. download pdf (103Kb)
Systemic Risk and Hedge Funds, with Nicholas Chan, Mila Getmansky, and Shane M. Haas, in M. Carey and R. Stulz, eds., The Risks of Financial Institutions and the Financial Sector, 2007. Chicago, IL: University of Chicago Press. In this article, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise. download pdf (706Kb)
Do Hedge Funds Increase Systemic Risks?, with Nicholas Chan, Mila Getmansky, and Shane M. Haas Federal Reserve Bank of Atlanta Economic Review 2006:Q4, 49–80. In this article, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise.

This is a redacted version of our paper "Systemic Risk and Hedge Funds".
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Marketable Alternatives, Commonfund Quarterly Fall 2002. An institutional perspective of risk management for alternative investments. download pdf (101Kb)
Personal Indexes, Journal of Indexes Q3(2001), 26–35. Artificial intelligence has transformed financial technology in many ways and in this review article, three of the most promising applications are discussed: neural networks, data mining, and pattern recognition. Just as indexes are meant to facilitate the summary and extraction of information in an efficient manner, sophisticated automated algorithms can now perform similar functions but at higher and more powerful levels. In some cases, artificial intelligence can save us from natural stupidity. download pdf (205Kb)
Fat Tails, Long Memory, and the Stock Market Since the 1960's, Economic Notes 26(1997), 219–252. The practice of risk management starts with an understanding of the statistical behavior of financial asset prices over time. Models such as the random walk hypothesis, the martingale model, and geometric Brownian motion are fundamental to any analysis of financial risks and rewards, particularly for longer investment horizons. Recent empirical evidence has cast doubt on some of these models, and this article provides an overview of such evidence. I begin with a review of the random walk hypothesis and related models, including a discussion of why such models perform so poorly, and then turn to some current research on alternative models such as long-term memory models and stable distributions. Not available
A Non-Random Walk Down Wall Street, in D. Jerison, I. Singer, and D. Stroock, eds., 1996, The Proceedings of the 1994 Wiener Centennial Symposium. Providence, RI: American Mathematical Society. While financial economics is still in its infancy when compared to the mathematical and natural sciences, it has enjoyed a spectacular period of growth over the past three decades, thanks in part to the mathematical machinery that Wiener, Ito, and others pioneered. In this review article, I shall present a survey of some recent research in this exciting area—more specifically, in empirical finance and financial econometrics—including a discussion of the random walk hypothesis, long-term memory in stock market prices, performance evaluation, and the statistical estimation of diffusion processes. It is my hope that such a survey will serve both as a tribute to the amazing reach of Nobert Wiener's research, and as an enticement to those in the "hard" sciences to take on some of the challenges of modern finance. Not available
Securities Transaction Taxes: What Would Be Their Effects on Financial Markets and Institutions?, with John Heaton, in Securities Transaction Taxes: False Hopes and Unintended Consequences, edited by Suzanne Hammond, 1995. Chicago, IL: Catalyst Institute. A securities transactions tax is likely to have far-reaching and profound implications for the financial systems and institutions. We evaluate the effect that a transactions tax will have on the financial system's role in transferring resources over time and in allocating risk efficiently across individuals and sectors. In particular, we examine the impact of a transactions tax on individual investors due to the reduction in the rate of return on savings, the reduction in trading, and the likely reduction in the value of stocks. We also consider the possible effects of a transactions tax on market liquidity. By reducing the informational role of prices and reducing market liquidity, a transactions tax may result in higher market volatility. We provide a simple numerical example that illustrates the enormous impact such a tax will have on the derivatives markets, where participants rely heavily on dramatic trading strategies to control risk. This sector is the financial system, along with its jobs, revenues, and risk-management capabilities are likely to move offshore in response to the tax.  Not available
Data-Snooping Biases in Financial Analysis, in H. Russell Fogler, ed.: Blending Quantitative and Traditional Equity Analysis, 1994. Charlottesville, VA: Association for Investment Management and Research. Data-snooping--finding seemingly significant but in fact spurious patterns in the data—is a serious problem in financial analysis. Although it afflicts all non-experimental sciences, data-snooping is particularly problematic for financial analysis because of the large number of empirical studies performed on the same datasets. Given enough time, enough attempts, and enough imagination, almost any pattern can be teased out of any dataset. In some cases, these spurious patterns are statistically small, almost unnoticeable in isolation. But because small effects in financial calculations can often lead to very large differences in investment performance, data-snooping biases can be surprisingly substantial. In this review article, I provide several examples of data-snooping biases, explain why it is impossible to eliminate them completely, and propose several ways to guard against the most extreme forms of data-snooping in financial analysis. Not available
Neural Networks and Other Nonparametric Techniques in Economics and Finance, in H. Russell Fogler, ed.: Blending Quantitative and Traditional Equity Analysis, 1994. Charlottesville, VA: Association for Investment Management and Research. Although they are only one of the many types of statistical tools for modeling nonlinear relationships, neural networks seem to be surrounded by a great deal of mystique and, sometimes, misunderstanding. Because they have their roots in neurophysiology and the cognitive sciences, neural networks are often assumed to have brain-like qualities: learning capacity, problem-solving abilities, and ultimately, cognition and self-awareness. Alternatively, neural networks are often viewed as "black boxes" that can yield accurate predictions with little modeling effort. In this review paper, I hope to remove some of the mystique and misunderstandings about neural networks by providing some simple examples of what they are, what they can and cannot do, and where neural nets might be profitably applied in financial contexts. Not available
Nontrading Effect, New Palgrave Dictionary of Money and Finance, 1992, with Craig MacKinlay. London: Stockton Press. An explanation of the nontrading effect. The New Palgrave Dictionary of Money and Finance is a 3-volume set which provides an unparalleled guide to modern money, banking and finance with over 1,000 substantial essays. It provides the most comprehensive analysis available of contemporary theory and the fast-evolving global monetary and financial framework. Not available
Empirical Issues in the Pricing of Options and Other Derivative Securities, Cuadernos Economicos de ICE 50(1992), 129–155. The pricing of options, certificates, and other derivatives or assetsfinancial assets whose payments depend on the prices of other assetsis one of the great successes of modern financial economics. Although the pricing of derivatives is computationally intensive, there is little done in terms of the traditional empirical analysis since by the very nature of the determination of prices and arbitrage there is no error term to minimize. There are, however, many issues of statistical inference that affect the pricing of options and other derivatives. This paper analyzes two of the most common issues neglected in the literature: reduced form empirical instruments for the determination of prices and how to use Monte Carlo simulations to calculate option prices depend on a path. Not available