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Risk Estimation on High Frequency Financial Data
Risk Estimation on High Frequency Financial Data
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83,57 €
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By studying the ability of the Normal Tempered Stable (NTS) model to fit the statistical features of intraday data at a 5 min sampling frequency, Florian Jacobs extends the research on high frequency data as well as the appliance of tempered stable models. He examines the DAX30 returns using ARMA-GARCH NTS, ARMA-GARCH MNTS (Multivariate Normal Tempered Stable) and ARMA-FIGARCH (Fractionally Integrated GARCH) NTS. The models will be benchmarked through their goodness of fit and their VaR and AVa…
  • Leidėjas:
  • Metai: 2015
  • Puslapiai: 70
  • ISBN-10: 3658093889
  • ISBN-13: 9783658093884
  • Formatas: 14.8 x 21 x 0.5 cm, minkšti viršeliai
  • Kalba: Anglų

Risk Estimation on High Frequency Financial Data (el. knyga) (skaityta knyga) | knygos.lt

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By studying the ability of the Normal Tempered Stable (NTS) model to fit the statistical features of intraday data at a 5 min sampling frequency, Florian Jacobs extends the research on high frequency data as well as the appliance of tempered stable models. He examines the DAX30 returns using ARMA-GARCH NTS, ARMA-GARCH MNTS (Multivariate Normal Tempered Stable) and ARMA-FIGARCH (Fractionally Integrated GARCH) NTS. The models will be benchmarked through their goodness of fit and their VaR and AVaR, as well as in an historical Backtesting.

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  • Autorius: Florian Jacob
  • Leidėjas:
  • Metai: 2015
  • Puslapiai: 70
  • ISBN-10: 3658093889
  • ISBN-13: 9783658093884
  • Formatas: 14.8 x 21 x 0.5 cm, minkšti viršeliai
  • Kalba: Anglų

By studying the ability of the Normal Tempered Stable (NTS) model to fit the statistical features of intraday data at a 5 min sampling frequency, Florian Jacobs extends the research on high frequency data as well as the appliance of tempered stable models. He examines the DAX30 returns using ARMA-GARCH NTS, ARMA-GARCH MNTS (Multivariate Normal Tempered Stable) and ARMA-FIGARCH (Fractionally Integrated GARCH) NTS. The models will be benchmarked through their goodness of fit and their VaR and AVaR, as well as in an historical Backtesting.

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