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 capture the long memory as measured from the real data. Since estimating parameters in a stochastic volatility model is a challenging task, we introduce a robust, knowledge-driven methodology based on the Generalized Methods of Moments. In addition to  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 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 challenging task, 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.
R
capture
captures
A
the