by Lieven Vandenberghe, Stephen Boyd
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Suggested foundations for portfolio construction and optimisation and numerical methods. Specialist prerequisites vary by resource.
Understand the covariance structure of portfolio risk.
Identify the objective, constraints and conditions for an optimum.
Read the linear algebra used to define a numerical problem.
Construct allocations and examine sensitivity to estimated inputs.
Turn mathematical weights into feasible portfolio decisions.
Understand identifiability and fitted model parameters.
Choose a quant role, build the mathematical core, and turn your first research question into an auditable Python project.
Connect probability, linear algebra and stochastic calculus to the quantities a quant model must estimate or price.