Top Quant Interview Questions from IMC Trading with Answers
In this article, we’ll tackle two classic quant interview questions, providing detailed explanations, mathematical insights, and practical approaches for each. Whether you’re preparing for an interview or deepening your understanding of time series modeling, this comprehensive guide is for you.
You have a time series with strong dependence between observations. A simple linear regression gives good in-sample performance, but its errors (residuals) appear to be correlated over time. What would you look at to determine whether the model is adequately capturing the temporal structure?
In time series modeling, the assumption of independent errors is often violated, especially when using simple linear regression on data with autocorrelated structure. If residuals are autocorrelated, the model may not have fully captured the underlying temporal dependencies, leading to unreliable inference and suboptimal predictions.
