Akuna Capital Quantitative Analyst Interview: OLS Regression Assumptions
If you’re preparing for a quantitative analyst interview at Akuna Capital or any top-tier trading firm, you’ll almost certainly be tested on your understanding of regression analysis and, specifically, the assumptions underlying Ordinary Least Squares (OLS). OLS is a foundational technique in statistics and quantitative finance, central to everything from risk modeling to strategy backtesting. This article thoroughly explores the OLS assumptions, why they matter, and how they relate to both theory and practice—vital knowledge for any aspiring quant.
Ordinary Least Squares (OLS) is the most common method for estimating the parameters of a linear regression model. In its simplest form, OLS seeks to fit a line through a set of data points such that the sum of the squared differences between the observed values and those predicted by the model is minimized.
The classic simple linear regression model can be written as:
