Working Papers




Black's Leverage Effect is not Due to Leverage
One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility, first documented by Black (1976) who attributed it to the effects of financial leverage. As a company's stock price declines, it becomes more highly leveraged given a fixed level of debt outstanding, and this increase in leverage induces a higher equity-return volatility. In a sample of all-equity-financed companies from January 1972 to December 2008, we find that the leverage effect is just as strong if not stronger, implying that the inverse relationship between price and volatility is not driven by financial leverage.
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Can Hedge Funds Time Market Liquidity?
This paper examines how hedge funds manage their market risk by responding to
changes in aggregate liquidity conditions. Using a large sample of equity-oriented hedge funds during the period of 1994-2008, we find strong evidence that hedge-fund managers possess the ability to time market liquidity at both the style category level
and the individual fund level. They increase (decrease) their portfolios’ market exposure when equity-market liquidity is high (low). This liquidity timing ability is asymmetric, and depends on market liquidity conditions: hedge funds reduce their portfolios’ market exposure significantly when market liquidity is extremely low, but they do not increase their market exposure when market liquidity is unusually high. Finally, we find that investing in top liquidity timing funds can generate economically significant profits. Our results persist after controlling for alternative benchmark models, various data biases, and return-timing and volatility-timing abilities.
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Fear, Greed, and Financial Crises:
A Cognitive Neurosciences Perspective
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. download pdf (327kb)
Security in the age of Systemic Risk: Strategies, Tactics and Options for Dealing with Femtorisks and Beyond The world today is increasingly confronted with systemic threats and challenges, in which femtorisks—small-scale dangers that are inherent to system structures and function and which pose asymmetrically catastrophic risks—can build in consequence, spreading uncontrollably like epidemics in both natural and social systems in such diverse areas as ecology, epidemiology, finance, the Internet, terrorism, and international relations. They have been successfully modeled in ecology in the context of complex adaptive systems: systems made up of individual agents, whose interactions have macroscopic consequences that feed back to influence individual behavior. While acknowledging challenges, this paper argues for the value of applying to societal systems the approaches that natural scientists have developed in quantifying and modeling biological interactions and ecosystems. download pdf (84kb)
Do Labyrinthine Legal Limits on Leverage Lessen the Likelihood of Losses? with Thomas J. Brennan
A common theme in the regulation of financial institutions and transactions is leverage constraints. Although such constraints are implemented in various ways—from minimum net capital rules to margin requirements to credit limits—the basic motivation is the same: to limit the potential losses of certain counterparties. However, the emergence of dynamic trading strategies, derivative securities, and other financial innovations poses new challenges to these constraints. We propose a simple analytical framework for specifying leverage constraints that addresses this challenge by explicitly linking the likelihood of financial loss to the behavior of the financial entity under supervision and prevailing market conditions. An immediate implication of this framework is that not all leverage is created equal, and any fixed numerical limit can lead to dramatically different loss probabilities over time and across assets and investment styles. This framework can also be used to investigate the macroprudential policy implications of microprudential regulations through the general-equilibrium impact of leverage constraints on market parameters such as volatility and tail probabilities. download pdf (1.2Mb)
A Survey of Systemic Risk Analytics with Dimitrios Bisias, Mark Flood, and Stavros Valavanis
We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and management. We motivate these measures from the supervisory, research, and data perspectives in the main text, and present concise definitions of each risk measure—including required inputs, expected outputs, and data requirements—in an extensive appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed open-sourse Matlab code for most of the analytics surveyed, which can be accessed through the Office of Financial Research (OFR) at Available for download here
Privacy-Preserving Methods for Sharing Financial Risk Exposures with Emmanuel A. Abbe and Amir E. Khandani
Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that protect the privacy of all parties involved and without the need for a trusted third party. Our approach employs secure multi-party computation techniques from cryptography in which multiple parties are able to compute joint functions without revealing their individual inputs. In our framework, individual financial institutions evaluate a protocol on their proprietary data which cannot be inverted, leading to secure computations of real-valued statistics such as concentration indexes, pairwise correlations, and other single- and multi-point statistics. The proposed protocols are computationally tractable on realistic sample sizes. Potential financial applications include: the construction of privacy-preserving real-time indexes of bank capital and leverage ratios; the monitoring of delegated portfolio investments; financial audits, and the publication of new indexes of proprietary trading strategies.
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Agent-Based Models of Financial Markets: A Comparison with Experimental Markets, with Nicholas Chan, Blake LeBaron, and Tomaso Poggio. We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among artificially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six different experimental designs, we investigate a number of features of our agent-based model: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the different types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several findings of human-based experimental markets, however, we also find intriguing differences between agent-based and human-based experiments.
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Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors, with Monica Billio, Mila Getmansky, and Loriana Pelizzon. A significant contributing factor to the Financial Crisis of 2007–2009 was the apparent interconnectedness among hedge funds, banks, brokers, and insurance companies, which amplified shocks into systemic events. In this paper, we propose five measures of systemic risk based on statistical relations among the market returns of these four types of financial institutions. Using correlations, cross-autocorrelations, principal components analysis, regime-switching models, and Granger causality tests, we find that all four sectors have become highly interrelated and less liquid over the past decade, increasing the level of systemic risk in the finance and insurance industries. These measures can also identify and quantify financial crisis periods. Our results suggest that while hedge funds can provide early indications of market dislocation, their contributions to systemic risk may not be as significant as those of banks, insurance companies, and brokers who take on risks more appropriate for hedge funds. download pdf (1.0Mb)
Is It Real, or Is It Randomized?: A Financial Turing Test, with Jasmina Hasanhodzic and Emanuele Viola. We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online videogame where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random". A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot     download pdf (167Kb)