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Hard · risk_management · Quant Researcher interview question · kalman-filter, dynamic-beta, state-space, risk-management, time-series
The Kalman filter provides an optimal linear estimator for a system's state, making it a core tool for dynamic parameter estimation in quantitative finance. Modeling an asset's market beta as a time-varying random walk allows for adaptive hedging and risk decomposition without fixed rolling windows. This state-space approach is a workhorse for multi-factor risk models at quantitative trading firms. Task Implement the function solution(asset_returns: list, market_returns: list, q_var: float, r_v