This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Compute variance of trading profits states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Question
Consider a stock price that starts at 0 and evolves as a simple symmetric random walk, moving +1 or -1 each discrete time step. At each time T you:
Buy one share if the price change from T−1 to T is +1.
Short (sell) one share if the price change is −1.
Assuming you can accumulate multiple positions purchased at different prices, derive the variance of the cumulative profit of your holdings at time T.
Follow-up: What is the expected profit and why? How would the answer extend if the random walk is replaced by continuous-time Brownian motion?
Quick Answer: This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Compute variance of trading profits states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
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Compute variance of trading profits
Symmetric Random Walk Trading Strategy: Profit Variance and Expectation
Setup
Let S_t be a simple symmetric random walk with S_0 = 0 and increments X_t = S_t − S_{t−1} taking values ±1 with equal probability, independent across t.
Time is discrete: t = 1, 2, ..., T.
Trading Rule
At each time t (after observing the move from t−1 to t):
If X_t = +1, buy 1 share.
If X_t = −1, short 1 share.
You accumulate all positions and mark them to market at the terminal time T.
Tasks
Derive a closed form for the cumulative profit at time T and compute its variance.
What is the expected profit? Explain.
How do these results extend if S_t is replaced by continuous-time standard Brownian motion B_t?
Clarifying Questions to Ask Guidance
Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
Show enough derivation for the interviewer to follow the reasoning.
Explain how you would validate the result with simulation or sensitivity checks.
What a Strong Answer Covers Guidance
A correct setup with definitions, formulas, and boundary conditions.
A step-by-step derivation or estimation plan.
Interpretation of the result, including uncertainty and practical limitations.
Checks for assumptions, edge cases, and numerical stability.
Follow-up Questions Guidance
How would the result change if the assumptions were relaxed?
Can you verify the answer with a simulation?
What is the most likely source of estimation error?