Some economic events are so major and unsettling that they “change everything.” Such is the case with the financial crisis that started in the summer of 2007 and is still a drag on the world economy. Yet enough time has now elapsed for economists to consider questions that run deeper than the usual focus on the immediate causes and consequences of the crisis. How have these stunning events changed our thinking about the role of the financial system in the economy, about the costs and benefits of financial innovation, about the efficiency of financial markets, and about the role the government should play in regulating finance? In Rethinking the Financial Crisis, some of the nation’s most renowned economists share their assessments of particular aspects of the crisis and reconsider the way we think about the financial system and its role in the economy.

The portfolio optimization problem has attracted researchers from many disciplines to resolve the issue of poor out-of-sample performance due to estimation errors in the expected returns. A practical method for portfolio construction is to use assets’ ordering information, expressed in the form of preferences over the stocks, instead of the exact expected returns. Due to the fact that the ranking itself is often described with uncertainty, we introduce a generic robust ranking model and apply it to portfolio optimization. In this problem, there are n objects whose ranking is in a discrete uncertainty set. We want to find a weight vector that maximizes some generic objective function for the worst realization of the ranking. This robust ranking problem is a mixed integer minimax problem and is very difficult to solve in general. To solve this robust ranking problem, we apply the constraint generation method, where constraints are efficiently generated by solving a network flow problem. For empirical tests, we use post-earnings-announcement drifts to obtain ranking uncertainty sets for the stocks in the DJIA index. We demonstrate that our robust portfolios produce smaller risk compared to their non-robust counterparts.

This is a pre-conference essay prepared for 'What Post-Crisis Changes Does the Economics Discipline Need?', a conference organized by Diane Coyle and Enlightenment Economics, the Bank of England, and the U.K. Government Economic Service on 7 February 2012. In this essay, I trace the origins of 'theory envy' to Paul Samuelson and the mathematization of economics over the past half century, and consider its implications for how economics should be taught. Although this research program has produced many genuine breakthroughs in economics, any virtue can become a vice when taken to an extreme, and the recent financial crisis has given us an opportunity to reinvent our field. One innovation is to teach economics not from an axiomatic and technique-oriented perspective, but by posing challenges that can only be addressed through economic logic. Instead of starting microeconomics with the consumer’s problem of maximizing utility subject to a budget constraint, begin by challenging students to predict the impact of a gasoline tax on the price of gasoline, or asking them to explain why diamonds are so much more expensive than water, despite the fact that the latter is critical for survival unlike the former. Instead of starting macroeconomics with national income accounts, begin with the question of how to measure and manage the wealth of nations, or why inflation can be so disruptive to economic growth. Without the proper institutional, political, and historical context in which to interpret economic models, constrained optimization methods and fixed-point existence proofs have much less meaning and are more likely to give rise to theory envy. However, when students understand the “why” of their course of study, even the most complex mathematical tools can be mastered and are almost always applied more meaningfully.

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-source Matlab code for most of the analytics surveyed, available for download above.

Biomedical innovation has become riskier, more expensive and more difficult to finance with traditional sources such as private and public equity. Here we propose a financial structure in which a large number of biomedical programs at various stages of development are funded by a single entity to substantially reduce the portfolio's risk. The portfolio entity can finance its activities by issuing debt, a critical advantage because a much large pool of capital is available for investment in debt versus equity. By employing financial engineering techniques such as securitization, it can raise even greater amounts of more-patient capital. In a simulation using historical data for new molecular entities in oncology from 1990 to 2011, we find that megafunds of $5-15 billion may yield average investment returns of 8.9-11.4% for equity holders and 5-8% for 'research-backed obligation' holders, which are lower than typical venture-capital hurdle rates by attractive to pension funds, insurance companies and other large institutional investors. Open-source software available for download in link above.

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.

BACKGROUND: The National Institutes of Health (NIH) is among the world’s largest investors in biomedical research, with a mandate to: "…lengthen life, and reduce the burdens of illness and disability." Its funding decisions have been criticized as insufficiently focused on disease burden. We hypothesize that modern portfolio theory can create a closer link between basic research and outcome, and offer insight into basic-science related improvements in public health. We propose portfolio theory as a systematic framework for making biomedical funding allocation decisions–one that is directly tied to the risk/reward trade-off of burden-of-disease outcomes.

METHODS AND FINDINGS: Using data from 1965 to 2007, we provide estimates of the NIH "efficient frontier", the set of funding allocations across 7 groups of disease-oriented NIH institutes that yield the greatest expected return on investment for a given level of risk, where return on investment is measured by subsequent impact on U.S. years of life lost (YLL). The results suggest that NIH may be actively managing its research risk, given that the volatility of its current allocation is 17% less than that of an equal-allocation portfolio with similar expected returns. The estimated efficient frontier suggests that further improvements in expected return (89% to 119% vs. current) or reduction in risk (22% to 35% vs. current) are available holding risk or expected return, respectively, constant, and that 28% to 89% greater decrease in average years-of-life-lost per unit risk may be achievable. However, these results also reflect the imprecision of YLL as a measure of disease burden, the noisy statistical link between basic research and YLL, and other known limitations of portfolio theory itself.

CONCLUSIONS: Our analysis is intended to serve as a proof-of-concept and starting point for applying quantitative methods to allocating biomedical research funding that are objective, systematic, transparent, repeatable, and expressly designed to reduce the burden of disease. By approaching funding decisions in a more analytical fashion, it may be possible to improve their ultimate outcomes while reducing unintended consequences.

The financial system has reached a level of complexity that only “power users” – highly trained experts with domain-specific knowledge – are able to manage. But because technological advances have come so quickly and are often adopted so broadly, there are not enough power users to go around. The interconnectedness of financial markets and institutions has created a new form of financial accident: a systemic event that extends beyond the borders of any single organisation.

In the Adaptive Markets Hypothesis (AMH) intelligent but fallible investors learn from and adapt to changing economic environments. This implies that markets are not always efficient, but are usually competitive and adaptive, varying in their degree of efficiency as the environment and investor population change over time. The AMH has several implications including the possibility of negative risk premia, alpha converging to beta, and the importance of macro factors and risk budgeting in asset-allocation policies.

BACKGROUND
Most economic theories are based on the premise that individuals maximize their own self-interest and correctly incorporate the structure of their environment into all decisions, thanks to human intelligence. The influence of this paradigm goes far beyond academia---it underlies current macroeconomic and monetary policies, and is also an integral part of existing financial regulations. However, there is mounting empirical and experimental evidence, including the recent financial crisis, suggesting that humans do not always behave rationally, but often make seemingly random and suboptimal decisions.
METHODS AND FINDINGS
Here we propose to reconcile these contradictory perspectives by developing a simple binary-choice model that takes evolutionary consequences of decisions into account as well as the role of intelligence, which we define as any ability of an individual to increase its genetic success. If no intelligence is present, our model produces results consistent with prior literature and shows that risks that are independent across individuals in a generation generally lead to risk-neutral behaviors, but that risks that are correlated across a generation can lead to behaviors such as risk aversion, loss aversion, probability matching, and randomization. When intelligence is present the nature of risk also matters, and we show that even when risks are independent, either risk-neutral behavior or probability matching will occur depending upon the cost of intelligence in terms of reproductive success. In the case of correlated risks, we derive an implicit formula that shows how intelligence can emerge via selection, why it may be bounded, and how such bounds typically imply the coexistence of multiple levels and types of intelligence as a reflection of varying environmental conditions.
CONCLUSIONS
Rational economic behavior in which individuals maximize their own self interest is only one of many possible types of behavior that arise from natural selection. The key to understanding which types of behavior are more likely to survive is how behavior affects reproductive success in a given population’s environment. From this perspective, intelligence is naturally defined as behavior that increases the probability of reproductive success, and bounds on rationality are determined by physiological and environmental constraints.

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.

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.

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.

Implicit in most asset-allocation policies is the statistical assumption of “stationarity,” which means that the means, variances, and covariances of asset returns are assumed to be constant over time. This assumption is a reasonable approximation during normal market conditions but fails dramatically during periods of market turmoil and dislocation. In such periods, market volatility is highly dynamic, correlations can jump to 100% in a matter of days, and risk premia can become negative for months at a time. FTSE and AlphaSimplex Group have developed a family of rule-driven (passive), transparent, and high-capacity indices whose volatilities are rescaled as often as daily with the goal of maintaining more stable risk levels. By stabilizing the risk of each asset class over time, the FTSE StableRisk Indices have the potential to capture the long-term risk premia of asset classes and simple strategies with less severe maximum drawdowns than those of traditional indices, which have no risk controls.

As financial markets grow in size and complexity, risk management protocols must also evolve to address more challenging demands. One of the most difficult of these challenges is managing event risk, the risk posed by unanticipated news that causes major market moves over short time intervals. Often cited but rarely managed, event risk has been relegated to the domain of qualitative judgment and discretion because of its heterogeneity and velocity. In this chapter, we describe one initiative aimed at solving this problem. The Thomson Reuters NewsScope Event Indices Project is an integrated framework for incorporating real-time news from the Thomson Reuters NewsScope subscription service into systematic investment and risk management protocols. The framework consists of a set of real-time event indices—each one taking on numerical values between 0 and 100—designed to capture the occurrence of unusual events of a particular kind. Each index is constructed by applying disciplined pattern recognition algorithms to real-time news feeds, and validated using econometric methods applied to historical data.