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Hard · time_series · Quant Researcher interview question · garch, time-series, volatility, mle, nelder-mead, numpy
The GARCH(1,1) model is a cornerstone of financial time series analysis, designed to capture the volatility clustering commonly observed in asset returns. Calibrating its parameters via Maximum Likelihood Estimation (MLE) is a critical step for applications like volatility forecasting, Value-at-Risk (VaR) calculation, and derivatives pricing. This problem involves implementing the MLE procedure for a GARCH(1,1) process under a conditional Gaussian assumption. Task Implement the function solutio