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Reading List RSS feed for reading list en-us Wed, 20 Jun 2018 12:00:48 MST Wed, 20 Jun 2018 12:00:48 MST In Defense of Optimization: The Fallacy of 1/N This paper by Kritzman, Page and Turkington debunks the myth that portfolio optimization is never useful and that 1/N portfolio always trump it. Another Look at Portfolio Optimization under Tracking-Error Constraints This paper by Phillipe Bertrand looks at portfolio optimization with tracking error constraints. He provides some geometric interpretations of this problem. I am always interested in new ways to visualize portfolio optimization. Skulls. Financial Turbulence, and Risk Management This article from Mark Kritzman and Yuanzhen Li describes their research on measuring turbulence in the market. They describe a process where by generating the covariance matrix from turbulent time periods then the optimized portfolio is more robust. A VaR Black-Litterman Model for the Construction of Absolute Return Fund-of-Funds This paper by Miguel Lejeune provides an interesting study in portfolio optimization. He describes a pretty rich objective function and constraints including integer, linear and quadratic constraints and then provides some information on the open source optimization software that they used to solve the problem. Much of the analysis is devoted to performance of their optimization algorithms. The Augmented Black-Litterman Model: A Ranking-Free Approach to Factor-Based Portfolio Construction and Beyond This paper by Wing Cheung introduces a great idea for using factor models with Black-Litterman. Since we're already doing a least squares type of fit to the views, why not put the factor loadings and views on factors in there too. I haven't quite digested the paper in it's entirety, but it seems to be some fresh new thinking on this topic. Conditional Distribution in Portfolio Theory Qian and Gorman consider posterior variance in a Black-Litterman and Bayes context. Their paper uses the alternative reference model rather than the Black-Litterman model itself, and doesn't consider the expressions for the posterior variance from the Black-Litterman model itself. Comparison and robustification of Bayes and Black-Litterman models This paper describes the robust optimization using Bayes and Black-Litterman models. The Black-Litterman Model for Active Portfolio Management This paper looks at the Black-Litterman model in the context of an active investor. By their definition this is an investor measuring their risk with tracking error versus a benchmark. The investor is also using this benchmark as their prior, or equilibrium, portfolio. The active investor is most likely to be optimizing their posterior portfolio to minimize tracking error, but this utility function is different from that naively used to compute the equilibrium returns. If the investor uses these two different utility functions, they will generate spurious trades in their portfolio. They propose the solution of a consistent utility function across the entire model. They illustrate the concept with a very simple two asset example. Approximating the Confidence Intervals for Sharpe Style Weights This paper by Angelo Lobosco and Dan DiBartolomeo describes a technique to add confidence intervals to weights derived using Sharpe's return based style analysis Assessing Views This 2003 paper by Fusai and Meucci appeared in Risk Magazine. It introduces their idea of measuing the consistency of the views and the prior through examining the Mahalanobis distance of the posterior from the prior. Fully Flexible Views: Theory and Practice Attilio Meucci continues to extend his state of the art work on bayesian mixing. This latest paper illustrates the ability to use non-normal views parameters with a model, where the models are not required to be linear. He shows how to efficiently apply this technique to scenario analysis, stress testing and ranking based allocation. This work uses simulation to generate the distributions which makes this model exceedingly flexible. TEV Sensitivity to Views in the Black-Litterman Model This paper by Maria Braga and Francecso Natale proposes the use of TEV as a measure of the posterior returns from the prior. Most portfolio managers who manage to a benchmark will be familiar with using Tracking Error vs the benchmark. This paper extends that use to the equilibrium portfolio which is the prior in the Black-Litterman model. Alternatives and Liquidity: Will Spending and Capital Calls Eat Your Modern Portfolio This paper by Laurence Siegel in the Journal of Portfolio Management describes issues relating to alternative and illiquid investments and their impact on a portfolio asset allocation in the event of a bear market. He works through a series of scenarios and shows how even modest payouts and capital calls on illiquid investments can significantly skew a portfolio's asset allocation. A Step-by-Step Guide to the Black-Litterman Model This is the latest version of Tom Idzorek's paper on the Black-Litterman model, dates Apr 26, 2005. It provides background on the model and then describes his extension to specify the confidence in a view using a simple percent. Commercial Real Estate: The Role of Global Listed Real Estate Equities in a Strategic Asset Allocation This paper by Tom Idzorek, Michael Barad, and Steve Meier from Ibbotson/Morningstar provides a balanced discussion of the role of REITs in a portfolio. They go on to look at how to estimate a global market capitalization so they can be included in the Black-Litterman model.