Here we will discuss more of Stress Modeling in the risk management domain. And how PCA is one such application to determine the optimal stress components.
The relevance of Stress Modeling framework to an ETRM is multifold.. Its not just a capability that enables the MO to enhance its risk management capability by supplementing VaR especially to capture non linear risk but in general provides a framework to stress specific weak points in the trading book regardless of the non linearity of the positions. We can then impose specific conditions as given for this stress window with the rules specified to be stressed Additionally one of the very important uses of the Stress framework is to optimize the market risk exposure via streamlining risk into a encapsulated form from the 100 of factors in the market place that can potentially cause volatility. How do we do that? Actually when we are dealing with capturing risk in the energy markets where there are 100 of variables effecting prices the ability for us to model all prices as input to modeling risk becomes extremely difficult. So the very first steps are to manage the dimensionality of the problem and streamline the risk factors. Dimensionality reduction helps by reducing the number of features while retaining key information. Techniques like principal component analysis (PCA), singular value decomposition (SVD) and linear discriminant analysis (LDA) project data onto a lower-dimensional space, while preserving important details.