Volatility-responsive asset allocation

**Bob Collie, et al. (Russell Investments) | Aug 2011**

The use of fixed weights in strategic asset allocation policy does not result in a stable risk/return pattern over time, but rather leads to greater risk at times of high market volatility and to lower risk in unusually stable markets. For investors who are sensitive to volatility, a more consistent outcome can be achieved – both in terms of the volatility of returns and in terms of how volatile that volatility itself is – by adopting a dynamic, or volatility-responsive, approach… The principle that underpins volatility-responsive asset allocation is to reduce exposure to risky assets when volatility is high, and to increase that exposure when volatility is low. This might result in a portfolio that averages, say, 50% exposure to the equity market, but which has more than that at times of market stability and less during volatile markets.

Benchmarking Low-Volatility Strategies

**Pim Van Vliet and David Blitz (Robeco Asset Mgt.) | Feb 2011**

An increasing number of investors are adopting low-volatility investment strategies designed to benefit from the empirical result that low-risk stocks offer high risk-adjusted returns… In this paper we discuss the benchmarking of low-volatility investment strategies, which are designed to benefit from the empirical result that low-risk stocks tend to earn high risk-adjusted returns. Although the minimum-variance portfolio of Markowitz is the ultimate low-volatility portfolio, we argue that it is not a suitable benchmark, as it can only be determined with hindsight. This problem is overcome by investable minimum-variance strategies, but because various approaches are equally effective at minimizing volatility it is ambiguous to elevate the status of any one particular approach to benchmark. As an example we discuss the recently introduced MSCI Minimum Volatility indices and conclude that these essentially resemble active low-volatility investment strategies themselves, rather than a natural benchmark for such strategies. In order to avoid these issues, we recommend to simply benchmark low-volatility managers against the capitalization-weighted market portfolio, using risk-adjusted performance metrics such as Sharpe ratio or Jensen’s alpha.

Is the Relation Between Volatility and Expected Stock Returns Positive, Flat or Negative?

**Pim Van Vliet and David Blitz (Robeco Asset Mgt.) | Jul 2011**

In theory the relation between volatility and expected stock returns should be positive, but the empirical evidence suggests that the relation is flat or even negative in reality. We have reconciled the conflicting empirical results by showing how methodological choices can lead to different, or even opposite conclusions. In our 1963-2009 U.S. sample we find that the empirical relation between historical volatility and expected returns is negative, with an average quintile return spread of -3.7%.The relation becomes 2% less negative when small caps are excluded, but 3% more negative when compounding effects are taken into account. We also show that studies which have reported a strong positive relation between volatility and expected return consider strategies which are not feasible in practice due to look-ahead biases. Our results provide an empirical basis for low-volatility and minimum-variance investment approaches.

What is the Shape of the Risk-Return Relation?

**Alberto Rossi and Allan G. Timmermann (University of Calif.) | Mar 2010**

The notion of a systematic trade-off between risk and expected returns is central to modern finance. Yet, despite more than two decades of empirical research, there is little consensus on the basic properties of the relation between the equity premium and conditional stock market volatility… Using a flexible econometric approach that avoids imposing restrictive modeling assumptions, we find evidence of a non-monotonic relation between conditional volatility and expected stock market returns: At low-to-medium levels of conditional volatility there is a positive trade-off between risk and expected returns, but this relationship gets inverted at high levels of volatility as observed during the recent financial crisis… These findings make it easier to understand why so many studies differ in their results regarding the sign and magnitude of the empirical volatility-return relationship and why results from linear models appear not to be robust to the sample period used in the analysis. In particular, studies that are conducted over periods without bouts of high (conditional) volatility are more likely to find a positive tradeoff between volatility and returns, while studies that include such episodes are more likely to find a negative or insignificant trade-off.

The Common Component of Idiosyncratic Volatility

**Jefferson Duarte, et al. (University of Washington) | Aug 2011**

We advance that a systematic risk factor, missing from Fama and French (1993), explains why high idiosyncratic volatility (IV) stocks yield lower risk-adjusted returns than low IV stocks. A single principal component, associated with business cycle variables, explains 32% of IV variation… Taken together, our empirical results provide support for the hypothesis that a systematic risk factor, which is not captured by the Fama-French factors, explains the puzzling relationship between *seemingly* idiosyncratic volatility and expected returns, first identified by Ang et al. (2006).