Akuna Capital Statistics 2027: OLS Assumptions Explained
The Akuna Capital ols assumptions answer: linearity, exogeneity (errors uncorrelated with regressors), homoskedasticity, no perfect multicollinearity, and normal errors for inference. Commonly reported by candidates, this tests whether your statistics knowledge is operational — especially what breaks when each assumption fails.
What This Question Assesses
This tests depth of statistical understanding. The interviewer wants the Gauss-Markov assumptions stated precisely plus what breaks when each fails — because in trading, regressions on financial data violate these assumptions constantly, and knowing the consequences is what matters.
How to Answer: Akuna Capital OLS Assumptions
- Step 1 — Linearity: the model is linear in parameters — the relationship being estimated has the assumed form.
- Step 2 — Exogeneity: errors have zero conditional mean (uncorrelated with regressors) — violation means biased coefficients, the most dangerous failure.
- Step 3 — Homoskedasticity and no autocorrelation: constant error variance and uncorrelated errors — violations distort standard errors and inference.
- Step 4 — No perfect multicollinearity (regressors not exactly linearly related) plus normality of errors for exact finite-sample inference.
Example: "The core assumptions are linearity, exogeneity — errors uncorrelated with the regressors — homoskedasticity, no perfect multicollinearity, and normal errors for inference. In practice I worry most about exogeneity, since its violation biases the coefficients themselves."
Common Mistakes on Akuna Capital OLS Assumptions
- Listing assumptions without consequences — 'what breaks if it fails' is the real test.
- Confusing homoskedasticity with normality — one concerns error variance, the other the error distribution.
- Claiming OLS requires normally distributed regressors — it does not; the assumption concerns the errors.
Statistics questions separate candidates who took a class from those who understood it. Learn each assumption plus its failure mode — that pairing is what gets asked.
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FAQ
What is the Gauss-Markov theorem?
Under the core assumptions, OLS is the best linear unbiased estimator (BLUE) — minimum variance among linear unbiased estimators.
What breaks OLS most in finance?
Endogeneity and autocorrelation — financial time series routinely violate independence assumptions.
How do you fix heteroskedasticity?
Robust standard errors correct inference; weighted least squares can improve efficiency. Know both.
Why does multicollinearity matter?
It inflates coefficient variances — estimates become unstable even though predictions may stay fine.
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