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dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …
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We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat tails in both types of data by appropriate distributional assumptions. The covariance...
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-Leibler divergence in empirically relevant settings. We illustrate the theory with an application to time-varying volatility models. We …
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. The methodology is hybrid because it combines a formaltesting procedure with volatility curve pattern recognition based …
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