Why Mean-Variance Optimization Does Not Require Normal Returns

# Why does the Markowitz mean-variance model require the assumption of normality?


# Why does the Markowitz mean-variance model require the assumption of normality?












Given $N$ assets, the Markowitz mean-variance model requires expected returns, expected variances and a $N \times N$ covariance matrix. The joint distribution is fully defined by these measures.

However I often read that assets are required to be normally distributed for consideration in the mean-variance model. While I understand that a normal joint distribution is fully defined by the statistics described above, I can't really see why normality is required.

Can't we simply assume that the distribution is fully described by $\mu$, $\sigma^2$ and $\Sigma$, and not necessarily imply normality? That is, an obvious drawback is not considering higher moments which influence assets, such as skewness and kurtosis, but why is normality an assumption?

## Answer by Mark Joshi (score 17, accepted)

https://quant.stackexchange.com/a/33856

it doesn't require normality. What it requires is that the investor's decisions are determined by mean and variance.

A normal distribution is determined by mean and variance, so if you assume joint normality then there is no point in the investor being interested in anything else.

(we try to discuss assumptions thoroughly in our book, Introduction to Mathematical Portfolio Theory.)

## Answer by David Addison (score 2)

https://quant.stackexchange.com/a/38481

Portfolio optimization techniques, such as those defined under Modern Portfolio Theory (MPT), are mildly predicated on the assumption of joint normality. Even though there will be a set of portfolio weights which minimizes variance regardless of the underlying distributions, correlation is only a complete measure of association if the joint multivariate distribution is normal; i.e., covariance is only an exhaustive measure of co-movement if the joint distributions are themselves normal. We can see this is true because the joint distribution of X and Y is defined by joint normality:

${\frac {1}{2\pi \sigma _{X}\sigma _{Y}{\sqrt {1-\rho ^{2}}}}}\iint _{X\,Y}\exp \left[-{\frac {1}{2(1-\rho ^{2})}}\left({\frac {X^{2}}{\sigma _{X}^{2}}}+{\frac {Y^{2}}{\sigma _{Y}^{2}}}-{\frac {2\rho XY}{\sigma _{X}\sigma _{Y}}}\right)\right]\,\mathrm {d} X\,\mathrm {d} Y$

Which through a proof can be show to produce:

$\sigma _{X+Y}={\sqrt {\sigma _{X}^{2}+\sigma _{Y}^{2}+2\rho \sigma _{X}\sigma _{Y}}},$

If now, we define $\omega_i \sigma^2_i=\sigma_X$, and $\omega_j \sigma^2_j=\sigma_Y$, then we get back the equation which is used as the basis of mean variance optimization of a two asset portfolio:

$\mathbb{E}[\sigma _{p}^{2}]=\omega_{i}^{2}\sigma _{i}^{2}+\omega_{j}^{2}\sigma _{j}^{2}+2\omega_{i}\omega_{j}\sigma _{i}\sigma _{j}\rho _{ij}$

So while the portfolio covariance matrix can always be computed, to the extent that underlying assets have returns which are not normal the optimization is likely to result in spuriously optimal weights.

## Answer by user59275 (score 1)

https://quant.stackexchange.com/a/68057

If the joint distribution of all the assets has a multivariate normal distribution then the distribution of any portfolio constructed out of a linear combination of those assets also has a normal distribution. Therefore, the risk can be measured by its variance (or equivalently by its standard distribution). Suppose that two assets have the same mean return and that the first has a smaller variance than the second then it can be shown that for any reasonable definition of risk, the first asset will have lower risk than the second provided both assets have normal distributions. However, when the distributions are not Gaussian, the same statement is no longer true. [source: Modeling in the Spirit of Markowitz Portfolio Theory in a Non Gaussian World Rajeeva L Karandikar, Director, Chennai Mathematical Institute, India and Tapen Sinha, AXA Chair Professor of Risk Management, ITAM, Mexico]