# What I Learned Today

No frills, just learn

## Optimizing: the Minimum Variance (or Maximum Return) Portfolio example

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The aim is the optimization either by maximizing total return (or minimizing risk), in keeping with the constraints of normalization for a given risk (or return).

Thus Lagrange method is perfectly suited for the job.
For minimizing risk, respect to a desired total return , one must consider the function of total risk

and minimize the lagrangian with constrain over the desired return and sum to one of weights.

Thus, provided that the constraints have non-zero derivatives,

by the fact that  is a non--degenerate constant vector (otherwise fixing the Total Risk is given), the Lagrangian is given by

with  the vector of lagrange multipliers.

Thus the minimization (maximization) is given by finding the extremant of the Lagrangian,

That is solved by considering the following linear system, that considering a symmetric covariance matrix  is given by

where  and  in the definition of the matrix are linear vectors of equal dimension.

Finally considering the Hessian for  operator to find the convexity.

Analogously could be done for a maximum return portfolio or relaxing the attribute for the desired best compromise.

## Investment Portfolio Basics

Considering  the vector of assets i weight in the portfolio (with constrain ) and  the vector of expect returns on the corresponding assets.

Considering  the volatility of the given asset, corresponding to the standard deviation of the normal distribution of returns (and thus  the risk), and  the correlation between assets.

The Total Return is given by:

And the Total Risk is

with  the covariance matrix with elements