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Forecasting Seasonal Long-memory Stochastic Volatility

A.C. Gonzaga

Abstract


In this paper, we propose an alternative approach to forecasting seasonal long-memory stochastic volatility based on the generalized long-memory stochastic volatility (GLMSV) model. We investigate the predictive ability of the proposed method vis-à-vis another forecasting approach using real data – Microsoft stock intraday volatility.

Keywords


Stochastic Volatility, Long-memory, k-GARMA process

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