A Statistical FX Factor Model

Factor models attempt to explain asset returns. You can approach this in two ways: define the factors you think are relevant, or use statistical learning to build the relevant factors from the data. My previous post took the first approach. This post will use principal component analysis (PCA) to let the data tell us which factors are most relevant in an FX factor model. Enjoy these types of posts? Then sign up for my newsletter. My last post on factor models (A Fundamental FX Factor Model) was about defining the factors we think are relevant to FX returns. These included: The DXY return (Making Sense of the DXY) Macro ETF returns to represent stocks, bonds, gold, and oil Momentum/reversion factors The final results were okay, and we found four factors were significant: 1-month momentum, 6-month momentum, DXY, and the EM factor. This time, we will start with the same dataset, but use statistical methods to learn the factors directly rather than presuming what might explain FX returns.…