Agents' heterogeneity has been recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons ; we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scales version effectively captures the long memory as measured from the real data. Since estimating parameters in a stochastic volatility model is a challengingtask , we introduce a robust , knowledge-driven methodology based on the Generalized Methods of Moments . In addition to the volatility clustering, the estimated model also captures other relevant stylized facts, emerging as a minimal but realistic and complete framework for modeling financial time series.
Agents' heterogeneity is recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons , we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scale version effectively captures the long memory as measured from the real data. Since estimating parameters in a stochastic volatility model is challenging , we introduce a robust methodology based on the Generalized Method of Moments supported by a heuristic selection of the orthogonal conditions . In addition to the volatility clustering, the estimated model also captures other relevant stylized facts, emerging as a minimal but realistic and complete framework for modelling financial time series.
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has been
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two-scales
two-scale
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a challengingtask
challenging
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Methods of Moments
Method of Moments supported by a heuristic selection of the orthogonal conditions
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modeling
modelling