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Difficulty: Medium
Category: time_series
Practice quant interview questions from top firms including Jane Street, Citadel, Two Sigma, DE Shaw, and other leading quantitative finance companies.
Topics: gbm, geometric_brownian_motion, mle, drift, volatility, time_series
Geometric Brownian Motion (GBM) is a standard model for asset price dynamics in quantitative finance. Calibrating its parameters—drift (μ) and volatility (σ) from historical price data is a crucial first step for tasks like derivatives pricing and risk management. This problem uses Maximum Likelihood Estimation (MLE) on the log-returns of a price series to find these parameters. Task Implement the function gbm_mle(prices: list, dt: float) to calculate the Maximum Likelihood Estimates (MLE) for
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