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Statistical analysis and quantitative modeling problems
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Difficulty: Medium
Category: statistical_analysis
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Topics: garch, volatility_forecasting, time_series
The GARCH(1,1) model is a cornerstone of financial time series analysis for modeling volatility clustering. It provides a framework for forecasting future conditional variance by assuming it reverts to a long-run average. These forecasts are critical inputs for risk management, derivatives pricing, and algorithmic trading strategies. Task Implement the function garch_variance_forecast(omega: float, alpha: float, beta: float, sigma2_0: float, h: int) to compute an h-step-ahead variance forecast
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