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most important factor in reducing portfolio variance is the use of a flexible model for time varying volatility, rather … good on the present set of measures as the stochastic volatility models, with or without dynamic correlation. …
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conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain … variance, in the order of integration, in the short memory characteristics and in the volatility of volatility. …
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When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on … stochastic volatility. Estimation of the model delivers measures of daily variation outperforming their non …
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This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used...
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methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …
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