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Advances in Portfolio Risk Control: Risk! Parity?
Winfried G. Hallerbach (Robeco) | May 1, 2013
Spurred by the increased interest in applying “risk control” techniques in an asset allocation context, we offer a practitioner’s review of techniques that have been newly proposed or revived from academic history. We discuss minimum variance, “1/N” or equal-weighting, maximum diversification, volatility weighting and volatility targeting – and especially “risk parity”, a concept that has become a real buzz word. We provide a taxonomy of risk control techniques. We discuss their main characteristics and their pluses and minuses and we compare them against each other and against the maximum Sharpe Ratio criterion. We illustrate their implications by means of an empirical example. We also highlight some important papers from the vast and still growing literature in this field. All in all, this note serves as a practical and critical guide to risk control strategies. It may help you to demystify risk control techniques, to appreciate both the “forest” and the “trees”, and to judge these techniques on their potential merits in practical investment applications.

Tail Hedging Strategies
Issam Strubn (The Cambridge Strategy) | May 7, 2013
This article introduces an algorithm for tail risk hedging and compares it to other existing methods. This algorithm adjusts the exposure level based on a measure of tail risk obtained by applying Extreme Value Theory (NYSE:EVT) to estimate Conditional Value at Risk (CVaR). This method is applied to the S&P 500 and MSCI Emerging Markets equity indexes between 2000 and 2012 and its performance is compared to cash and options based tail hedging strategies. The cash based methods are shown to significantly increase risk adjusted returns and reduce drawdowns, while the options based strategy suffers a decrease in performance from 2003 on due to the increase in cost of puts with respect to calls after that date. The tail hedging technique presented in the article is fully investable as its turnover is limited; additionally it can replace long/short equity hedge funds for investors who do not have access to alternative investments.

Macro-Based Parametric Asset Allocation
Richard Franz (WU Vienna University) | May 3, 2013
This paper presents a novel approach to asset allocation which builds up on macroeconomic factors. Without doubt the financial return of asset classes are interlinked with the economy. However, it is not that clear how to bring the finance and economy world together within a portfolio’s asset allocation. I propose a direct modeling of the weights with global macroeconomic risk factors. These risk factors are not asset class specific but potentially related to the return of all asset classes. In this paper I focus on three asset classes: stocks, bonds and the risk free asset. The approach is robust, links macroeconomic factors to financial returns intuitively and outperforms a standard 60/40 portfolio almost twice in terms of the Sharpe Ratio - in sample and out of sample. This outperformance even remains to a large extent when considering transaction or leverage costs.

Stochastic Portfolio Theory Optimization and the Origin of Alternative Asset Allocation Strategies
Gianluca Oderda (Ersel Asset Management) | .May 7, 2013
What is the theoretical reason why a particular alternative allocation strategy, or a combination thereof, should offer a superior return vs. risk tradeoff? Can we derive an optimal alternative allocation strategy from first principles, both from an absolute return perspective, to identify the most appropriate long-term strategic benchmark, and from a relative return perspective, to identify the alternative allocation strategy with the highest expected information ratio relative to a market-cap weighted index? We attempt to answer these questions, by building on the stochastic portfolio theory framework of Fernholz, to study the evolution of portfolio wealth, both in absolute terms and relative to a market index. We prove that the portfolio maximizing the expected value of logarithmic portfolio wealth at a fixed level of volatility differs from the traditional mean-variance portfolio solution by the linear combination of three further terms: an equally weighted portfolio, a risk parity portfolio, and a high cash flow rate of return portfolio. Most importantly, we prove that, given any market capitalization weighted index, the portfolio maximizing relative logarithmic growth with respect to this index deviates from the market benchmark by the linear combination of four subportfolios: an equally weighted portfolio, a risk parity portfolio, a high cash flow rate of return portfolio, and a global minimum variance portfolio. Consistent with previous empirical research, we prove that an investor can profit from diversification effects when spreading his investment across different alternative asset allocation methods, and we show the four alternative asset allocation building blocks which constitute the optimal portfolio.

Strategic Allocation to Commodity Factor Premiums
D. Blitz & W. De Groot (Robeco) | May 16, 2013
Investors may wonder whether the traditional arguments for investing in commodities still apply, as the return, diversification and inflation-hedging potential of commodities appear to have declined. In this study, we take a fresh look at the strategic allocation to commodities, considering not only the commodity market portfolio, but also various other factor premiums documented to exist in the commodities market. We find that a commodity factor portfolio consisting of the momentum, carry and low-volatility factor premiums exhibits a significantly better risk-adjusted performance than a conventional commodity portfolio. We also find that only such a commodity multi-factor portfolio adds value in the strategic asset allocation. As the traditional commodity market portfolio appears to deserve little or no role at all in the strategic asset allocation, we argue that investors should not postpone the consideration of alternative commodity factor premiums to a later stage of the investment process.

Source: Research Review: Risk Management And Asset Allocation