Due to turbulence in the financial market throughout history, stress testing has become a growing part of the risk analysis performed by clearing houses. Events connected to previous crises have increased the demand for prudent risk exposure, and in this thesis we investigate regulators view on how CCPs should construct risk scenarios to meet best practice for stress testing their members’ composite portfolios. A method based on multivariate t-distributions and copula-transformations applied to historical time series data, is proposed for constructing an independent scenario generator which should be used as a compliment to other, more knowledge-based methods. The method was implemented in Matlab to test the theory in practice, and experiments were setup for pure stock portfolios as well as for derivative based portfolios. Backtests were then carried out to validate the underlying theory on historical data spanning 25 years in total. Results show that the method proposed in this thesis indeed has the potential to be a useful approach for creating stress scenarios. Its ability to render specific levels of plausibility seems to show a sufficient level of consistency with real life data, and further research is thereby justified.