Time Series Modeling in Quant Interviews: Linear Regression vs GARCH
Interviewers want to know: can you recognize when classic linear models fail? Do you understand the nuances of autocorrelation, volatility clustering, and non-stationarity? This article will walk you through the evolution of time series modeling in quant interviews, from basic linear regression to advanced GARCH models, equipping you to answer common time series modeling quant interview questions with confidence.
Time series analysis is at the heart of quantitative finance. Prices and returns evolve sequentially, and understanding their structure—trends, mean-reversion, volatility regimes—can make or break a trading or risk management strategy. As a candidate, you’ll face interview questions that probe not just your knowledge of models, but also your intuition about their assumptions, limitations, and real-world behavior.
This guide covers the essential time series modeling quant interview questions, starting from ordinary least squares (OLS) regression and moving toward sophisticated models like GARCH that capture the quirks of financial data.
