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GARCH Volatility Persistence

Hard · Statistics & Regression · Quant Trader interview question · GARCH, volatility, time-series, statistics, persistence

You are analyzing the volatility of a stock using a GARCH(1,1) model. The model is defined as: $ \sigma_t^2 = \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2 $ where: $ \sigma_t^2 $ is the conditional variance at time t. $ \omega $ is a constant. $ \epsilon_{t-1} $ is the error term at time t-1. $ \sigma_{t-1}^2 $ is the conditional variance at time t-1. If the estimated parameters result in $ \alpha + \beta $ being very close to 1, what does this imply about the volatility