(Stochastic) Optimization

Optimization appears in a variety of financial applications (portfolio allocation, trading strategies, identifying arbitrage strategies etc) and in various aspects in the real economy (what is the optimum volume for maximizing profitability, what is the optimum allocation of resources etc). An optimization problem formulation consists of three parts:

  1. a set of decision variables;
  2. an objective function;
  3. an a set of constraints.

If all elements are kept static then the equation is mostly not hard to find. However, it becomes harder when elements change over time or are uncertain.


We have in depth understanding of building complex optimization frameworks and to explain and communicate these frameworks to our client.  The explanation how the results are derived is often more important than the results itself. Garbage in is garbage out. Furthermore, the analysis to come to the outcome mostly provides new knowledge to our clients.

Examples of our (stochastic) optimization assignments are:

  • risk optimization for the introduction of new diagnostic assays under uncertainty
  • factory portfolio optimization whereby the market dynamics are uncertain

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