__Title__: MITIGATING ESTIMATION RISK IN ASSET ALLOCATION: DIAGONAL MODELS VERSUS 1/N DIVERSIFICATION
__Authors__: CHRIS STIVERS, LICHENG SUN
__Publication__**:** THE FINANCIAL REVIEW, 2016 (version here)

**What are the research questions?**

In spite of several efforts by researchers to overcome the estimation-risk problem (the use of estimate inputs based on sample information as if they were representative of the true population) which produces the so-called “wacky weights”, DeMiguel, Garlappi and Uppal (2009) present striking evidence that favors a simple 1/N naıve portfolio strategy.

The authors challenge the results by DeMiguel et al. (2009) by studying the following research question:

- Are asset allocation models that use “diagonal” elements of the inverse covariance matrix superior to those using the “full” matrix, in addressing the “wacky weights” problem?
- Do “diagonal” models outperform the naive 1/N strategy?

**What are the Academic Insights?**

By using five different empirical datasets of disaggregate portfolio returns, simulated data, out of sample testing and the introduction of transaction costs, the authors find that:

- YES- Diagonal strategies naturally avoid short-sale positions and substantially mitigate the so-called “wacky weights” problem
- YES-Simple diagonal strategies based solely on estimates of total volatility, generally outperform the 1/N strategy. The differences in the performance metrics are statistically significant in most case

**Why does it matter?**

This study adds to prior literature focused on comparing “full-matrix” strategies to 1/N. In fact, it studies “diagonal only” solutions that consider limited information such as volatility or idiosyncratic volatility. It shows that they avoid short sale positions and limit the “wacky weights” issue. Additionally, they generally outperform 1/N. Asset allocation and its research development is not dead yet!

## The Most Important Chart from the Paper: