Quantitative Research Interview Questions

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Consider a geometric Brownian motion martingale with stochastic volatility. The logarithm of the latent volatility follows a fractional Brownian motion, with an unknown “volatility of volatility” and Hurst exponent H. (a) Write the stochastic differential equations for the system as described. (b) Assuming the model represents a stock price and given a historical price sampled every second, describe your approach for fitting the model. How would you estimate the latent volatility? Be specific. (c) The current volatility model is non-stationary. Suggest and justify an adjustment to make it stationary. What are the revised equations?

Quantitative Researcher

Interviewed at Agitprop

Aug 4, 2024

Consider a geometric Brownian motion martingale with stochastic volatility. The logarithm of the latent volatility follows a fractional Brownian motion, with an unknown “volatility of volatility” and Hurst exponent H. (a) Write the stochastic differential equations for the system as described. (b) Assuming the model represents a stock price and given a historical price sampled every second, describe your approach for fitting the model. How would you estimate the latent volatility? Be specific. (c) The current volatility model is non-stationary. Suggest and justify an adjustment to make it stationary. What are the revised equations?

Consider the following code: 1 double f ( double x ) { 2 if ( x == 0) 3 return 1.0; 4 return f (0.5 * x ) + f (0.3 * x ) ; 5 } Analyze the time and memory complexities of this function. Provide asymptotically tight bounds.

Quantitative Researcher

Interviewed at Agitprop

Aug 4, 2024

Consider the following code: 1 double f ( double x ) { 2 if ( x == 0) 3 return 1.0; 4 return f (0.5 * x ) + f (0.3 * x ) ; 5 } Analyze the time and memory complexities of this function. Provide asymptotically tight bounds.

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