QFC Quantitative Finance Algorithmic Trading & Data Science 3

Question 1

What does the Hurst exponent measure in financial time series analysis?

Accepted Answer

The degree of mean reversion or trend persistence in a time series

Answer

A Hurst exponent H > 0.5 indicates trending (persistent) behavior, H < 0.5 indicates mean reversion, and H = 0.5 indicates a random walk.

Question 2

Which regularization technique in linear regression is most appropriate when many predictors are expected to have zero effect on the target variable?

Accepted Answer

Lasso (L1) regularization

Answer

Lasso (L1) regularization produces sparse solutions by driving irrelevant coefficients exactly to zero, making it ideal for feature selection.

Question 3

In market microstructure, what does the 'adverse selection' component of the bid-ask spread represent?

Accepted Answer

The cost of trading with better-informed counterparties

Answer

Adverse selection cost reflects the risk that the market maker's counterparty possesses private information, leading to losses on subsequent price moves.

Question 4

What is the 'volatility smile' in options markets and what does it imply about the Black-Scholes model?

Accepted Answer

Implied volatility varies with strike price, implying the lognormal return assumption is violated

Answer

The volatility smile shows that implied volatilities differ across strikes, indicating that real return distributions have fat tails and/or skewness not captured by the lognormal model.

Question 5

Which gradient boosting hyperparameter most directly controls the bias-variance trade-off in financial prediction models?

Accepted Answer

Maximum tree depth

Answer

Maximum tree depth controls model complexity directly — shallow trees (low depth) give high bias/low variance, while deep trees give low bias/high variance.

(QFC) Quantitative Finance Certification Practice Test

(QFC) Quantitative Finance Certification Practice Test

QFC Quantitative Finance Algorithmic Trading & Data Science 3