Explain derivatives, YTM, and core statistics
Company: Squarepoint
Role: Data Scientist
Category: Statistics & Math
Difficulty: Medium
Interview Round: Technical Screen
You are interviewing for a quantitative/data role and are asked a mix of markets + statistics fundamentals.
## Part A — Markets & instruments
1. **Derivatives:** Explain the difference between a **forward**, **futures**, and an **option**.
- For each, describe: (i) who has the *right* vs *obligation*, (ii) how payoff works at expiration, (iii) typical trading venue (OTC vs exchange), (iv) margin/collateral, and (v) counterparty risk.
2. **Compared to stocks:** How are forwards/futures/options different from **stocks**?
3. **Stock basics:** What are the basic characteristics of a stock? Mention at least:
- what ownership/claim it represents,
- what the ticker/symbol is used for,
- common rights/cashflows (e.g., voting/dividends) and limited liability.
4. **Common market terms:** Define **YTM (Yield to Maturity)** and give a concrete numeric example of how it is interpreted (you do not need to solve a full bond-pricing root-finding problem unless asked).
## Part B — Probability & statistics
1. **Fair coin toss:** Assume a fair coin with \(P(H)=P(T)=0.5\).
- (a) For \(n\) tosses, what is the probability of getting exactly \(k\) heads?
- (b) What are the expected value and variance of the number of heads \(X\) in \(n\) tosses?
2. **Markov chain application (coin toss):** Let \(T\) be the number of tosses needed to see **two consecutive heads (HH)** for the first time.
- Compute \(\mathbb{E}[T]\) using a Markov chain / state-based recursion.
3. **Variance & covariance algebra:** Derive \(\mathrm{Var}(A+B)\) in terms of \(\mathrm{Var}(A)\), \(\mathrm{Var}(B)\), and \(\mathrm{Cov}(A,B)\).
- Explain what covariance means.
- What simplification occurs if \(A\) and \(B\) are independent?
4. **Regression interpretation:**
- What does **variance** represent in general, and how does it show up in linear regression (e.g., noise term, uncertainty, standard errors)?
- Define \(R^2\) and explain what it means in a regression context, including at least one limitation/pitfall.
Quick Answer: This question evaluates knowledge of financial instruments (forwards, futures, options), bond metrics like yield-to-maturity, and foundational probability and statistics topics including binomial distributions, Markov-chain stopping times, variance/covariance algebra, and regression interpretation.