/PrincipalComponentAnalysis_in_LMM_HJM_and_Hedging

In this repository we cover the application of Principal Component Analysis to two areas of fixed income: to induce correlation in multi factor models such as LMM and HJM, and to hedge interest rate risk

Primary LanguageJupyter Notebook

PrincipalComponentAnalysis_in_LMM_HJM_and_Hedging

In this repository we cover the application of Principal Component Analysis to two areas of fixed income: to create correlation in multi factor models such as LMM and HJM, and to hedge interest rate risk.

In two previous notebooks on the Libor Market Model and HJM, the correlation across forward rates of different tenors was generated using Principal Components. We did not explain how the principal component factors were calculated. In the first notebook, Note 01, we want to do just that.

PCA is also an excellent statistical technique used to explain the historical variation of yields: the level, the slope and the curvature, and as a result, it is very useful for hedging purposes. Note 02 explains how to use PC to hedge interest rate risk.