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We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...
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. The methodology is hybrid because it combines a formaltesting procedure with volatility curve pattern recognition based …
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We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete … series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades … intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling …
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We study optimality properties in finite samples for time-varying volatility models driven by the score of the …-driven volatility models have optimality properties when they matter most. Score-driven models perform best when the data is fat …
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that is embedded in the time-varying parameter path. We illustrate our findings in a volatility analysis for monthly …
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We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...
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