Fund Management and Systemic Risk - Lessons from the Global Financial Crisis

Fund managers play an important role in increasing efficiency and stability in financial markets. But research also indicates that fund management in certain circumstances may contribute to the buildup of systemic risk and severity of financial crises. The global financial crisis provided a number of new experiences on the contribution of fund managers to systemic risk. In this article, we focus on these lessons from the crisis. We distinguish between three sources of systemic risk in the financial system that may arise from fund management: insufficient credit risk transfer to fund managers; runs on funds that cause sudden reductions in funding to banks and other financial entities; and contagion through business ties between fund managers and their sponsors. Our discussion relates to the current intense debate on the role the so-called shadow banking system played in the global financial crisis. Several regulatory initiatives have been launched or suggested to reduce the systemic risk arising from non-bank financial entities, and we briefly discuss the likely impact of these on the sources of systemic risk outlined in the article

Lessons from the Subprime Meltdown

This paper uses Hyman P. Minsky's approach to analyze the current international financial crisis, which was initiated by problems in the American real estate market. In a 1987 manuscript, Minsky had already recognized the importance of the trend toward securitization of home mortgages. This paper identifies the causes and consequences of the financial innovations that created the real estate boom and bust. It examines the role played by each of the key playersincluding brokers, appraisers, borrowers, securitizers, insurers, and regulatorsin creating the crisis. Finally, it proposes short-run solutions to the current crisis, as well as longer-run policy to prevent it (a debt deflation) from happening again

GMO's Predictions: A Useful Guide for Investors?

How successful are stock market predictions? We explore one set of easily accessible predictions by a respected firm, GMO. Specifically, we evaluate how effective GMO’s predicted stock returns have been in guiding investors from June 2000 through March 2014. We find that the predictions have been useful, although based on past history investing solely in the top one or two performing indexes would have been an inferior strategy for maximizing return to investing equal amounts in the three indexes with the top predicted returns

A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms

We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that our framework nests as special cases the shrinkage approaches of Jagannathan and Ma (Jagannathan, R., T. Ma. 2003. Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance 58 1651-1684) and Ledoit and Wolf (Ledoit, O., M. Wolf. 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finance 10 603-621, and Ledoit, O., M. Wolf. 2004. A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 365-411) and the 1/N portfolio studied in DeMiguel et al. (DeMiguel, V., L. Garlappi, R. Uppal. 2009. Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Rev. Financial Stud. 22 1915-1953). We also use our framework to propose several new portfolio strategies. For the proposed portfolios, we provide a moment-shrinkage interpretation and a Bayesian interpretation where the investor has a prior belief on portfolio weights rather than on moments of asset returns. Finally, we compare empirically the out-of-sample performance of the new portfolios we propose to 10 strategies in the literature across five data sets. We find that the norm-constrained portfolios often have a higher Sharpe ratio than the portfolio strategies in Jagannathan and Ma (2003), Ledoit and Wolf (2003, 2004), the 1/N portfolio, and other strategies in the literature, such as factor portfolios.portfolio choice, covariance matrix estimation, estimation error, shrinkage estimator, norm constraints

An overview of the evolution of human reliability analysis in the context of probabilistic risk assessment.

Since the Reactor Safety Study in the early 1970's, human reliability analysis (HRA) has been evolving towards a better ability to account for the factors and conditions that can lead humans to take unsafe actions and thereby provide better estimates of the likelihood of human error for probabilistic risk assessments (PRAs). The purpose of this paper is to provide an overview of recent reviews of operational events and advances in the behavioral sciences that have impacted the evolution of HRA methods and contributed to improvements. The paper discusses the importance of human errors in complex human-technical systems, examines why humans contribute to accidents and unsafe conditions, and discusses how lessons learned over the years have changed the perspective and approach for modeling human behavior in PRAs of complicated domains such as nuclear power plants. It is argued that it has become increasingly more important to understand and model the more cognitive aspects of human performance and to address the broader range of factors that have been shown to influence human performance in complex domains. The paper concludes by addressing the current ability of HRA to adequately predict human failure events and their likelihood