Quant interview questions

496 questions · page 6 of 13 · Clear filters

• With orthonormal regressors, the lasso minimising…Regression and econometrics · Applied
• With highly collinear regressors, ridge regression often predicts better out of…Regression and econometrics · Applied
• An instrument Z has Cov(Z,Y) = 0.6 and Cov(Z,X) = 0.4.Regression and econometrics · Advanced
• Your first-stage F-statistic is 4. What does that tell you about the IV estimate?Regression and econometrics · Advanced
• You model trade counts per venue where exposure times differ. What belongs in the model?Regression and econometrics · Advanced
• Two models are fitted on 100 observations.Regression and econometrics · Advanced
• You average 1,000 observations from an AR(1) process with autocorrelation 0.5.Regression and econometrics · Advanced
• A design matrix has squared singular values 9, 4 and 1.Regression and econometrics · Advanced
• Which of these is closest to being a stationary series?Time series · Foundation
• For white noise, sample autocorrelations have standard error about 1/√T.Time series · Foundation
• When is the AR(1) process xₜ = φxₜ₋₁+εₜ covariance stationary?Time series · Foundation
• A HAR model forecasts tomorrow’s realised volatility as 0.1+0.4 RV_d+0.3 RV_w+0.2 RVₘ.Time series · Foundation
• Your labels are each asset’s return over the next 10 trading days.Time series · Foundation
• You regress one stock’s price on another’s and get R² = 0.95 with a t-statistic of 40.Time series · Applied · Free solution
• A spread follows Δsₜ = −0.05 sₜ₋₁+εₜ on daily data.Time series · Applied · Free solution
• An AR(1) has φ = 0.8 and innovation variance 1. What is its stationary variance?Time series · Applied
• Daily equity returns show almost no autocorrelation, but their squares show a lot.Time series · Applied
• An augmented Dickey–Fuller test fails to reject its null. What have you learned?Time series · Applied
• For xₜ = εₜ+0.5εₜ₋₁, what is the lag-one autocorrelation?Time series · Applied
• For xₜ = 2+0.6xₜ₋₁+0.3xₜ₋₂+εₜ, what is the unconditional mean?Time series · Applied
• Your prior on a state is 10 with variance 4, and you observe 13 with…Time series · Applied
• What is the leverage effect in equity returns?Time series · Applied
• Using EWMA with λ = 0.94, yesterday’s volatility estimate was 1% and today’s return is 2%.Time series · Applied
• You have ten years of daily data, start with a two-year training window, expand…Time series · Applied
• Daily returns have first-order autocorrelation 0.1 and none beyond.Time series · Applied
• With T = 100 observations, the sample autocorrelations at lags one and two are 0.2 and 0.1.Time series · Applied
• An AR(1) process has mean 2 and coefficient 0.8.Time series · Applied
• For the stationary AR(2) process xₜ = 0.5xₜ₋₁+0.3xₜ₋₂+εₜ, what is the lag-one…Time series · Applied
• What is the lag-one autocorrelation of the MA(1) process xₜ = εₜ+2εₜ₋₁?Time series · Applied
• A series’ ACF decays geometrically while its PACF is significant at lags one…Time series · Applied
• An AR(1) process has coefficient 0.8 and innovation variance 1.Time series · Applied
• A spread follows an Ornstein–Uhlenbeck process dX = θ(μ−X) dt+σ dW with θ = 5 per year.Time series · Applied
• A daily GARCH(1,1) has ω = 0.000002, α = 0.08 and β = 0.90.Time series · Applied
• A GARCH(1,1) has α+β = 0.98.Time series · Applied
• With the GARCH(1,1) above (ω = 0.000002, α = 0.08, β = 0.90), yesterday’s…Time series · Applied
• For a random-walk level observed with noise, what does the Kalman filter’s…Time series · Applied
• Returns are modelled with a Student-t distribution with 5 degrees of freedom.Time series · Applied
• Daily returns have volatility 1% and first-order autocorrelation −0.1.Time series · Applied
• The autocorrelation of absolute daily returns stays positive and decays only…Time series · Applied
• Out of sample, your return forecast has mean squared error 0.98 while the…Time series · Applied