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Risk Estimation on High Frequency Financial Data : Empirical Analysis of the DAX 30

By: Jacob, Florian.
Material type: TextTextSeries: eBooks on Demand.BestMasters: Publisher: Wiesbaden : Springer Fachmedien Wiesbaden, 2015Description: 1 online resource (78 p.).ISBN: 9783658093891.Subject(s): Electronic trading of securities | Investments -- Mathematical models | Stock exchanges -- GermanyGenre/Form: Electronic books.Additional physical formats: Print version:: Risk Estimation on High Frequency Financial Data : Empirical Analysis of the DAX 30DDC classification: 510 Online resources: Click here to view this ebook.
Contents:
Acknowledgements; Contents; List of Figures; List of Tables; 1. Introduction; 2. Theory of Time Series Modeling and Risk Estimation; 2.1. Financial Econometrics; 2.1.1. GARCH models; 2.1.2. FIGARCH; 2.2. Model Choice and Validation; 2.2.1. Detecting the Autocorrelation; 2.2.2. Testing for Long-Range Dependency; 2.3. The innovation process; 2.3.1. Lévy Processes; 2.3.2. Subordination; 2.3.3. Time-changed Stochastic Process; 2.3.4. Time-changed Brownian Motion; 2.3.5. The α-stable Process; 2.3.6. Tempered Stable Distribution; 2.3.7. CTS Subordinator
2.3.8. Univariate Normal Tempered Stable Distribution2.3.9. Standard Univariate NTS; 2.3.10. Multivariate Standard Normal Tempered Stable Distribution; 2.4. Goodness of Fit; 2.4.1. Kolmogorov-Smirnov-Test; 2.4.2. Anderson-Darling Test; 2.5. Risk Management; 2.5.1. Value-at-Risk; 2.5.2. Coherent Risk Measures; 2.5.3. Average Value-at-Risk; 2.5.4. Computation; 2.5.5. Backtesting; 3. Data and Methodology; 3.1. Data Selection; 3.2. Data Transformation; 3.3. Autocorrelation and Dependence; 3.4. Empirical Estimation and GoF Tests; 3.4.1. Index Returns; 3.4.2. Stock Returns; 3.4.3. ARMA-GARCH
3.5. Comparison of Risk Measures for ARMA-GARCH Models3.5.1. Risk Prediction; 3.5.2. Backtestings; 4. Conclusion; A. DAX 30 Stocks; Bibliography
Summary: By studying the ability of the Normal Tempered Stable (NTS) model to fit thestatistical 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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Electronic Book UT Tyler Online
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Acknowledgements; Contents; List of Figures; List of Tables; 1. Introduction; 2. Theory of Time Series Modeling and Risk Estimation; 2.1. Financial Econometrics; 2.1.1. GARCH models; 2.1.2. FIGARCH; 2.2. Model Choice and Validation; 2.2.1. Detecting the Autocorrelation; 2.2.2. Testing for Long-Range Dependency; 2.3. The innovation process; 2.3.1. Lévy Processes; 2.3.2. Subordination; 2.3.3. Time-changed Stochastic Process; 2.3.4. Time-changed Brownian Motion; 2.3.5. The α-stable Process; 2.3.6. Tempered Stable Distribution; 2.3.7. CTS Subordinator

2.3.8. Univariate Normal Tempered Stable Distribution2.3.9. Standard Univariate NTS; 2.3.10. Multivariate Standard Normal Tempered Stable Distribution; 2.4. Goodness of Fit; 2.4.1. Kolmogorov-Smirnov-Test; 2.4.2. Anderson-Darling Test; 2.5. Risk Management; 2.5.1. Value-at-Risk; 2.5.2. Coherent Risk Measures; 2.5.3. Average Value-at-Risk; 2.5.4. Computation; 2.5.5. Backtesting; 3. Data and Methodology; 3.1. Data Selection; 3.2. Data Transformation; 3.3. Autocorrelation and Dependence; 3.4. Empirical Estimation and GoF Tests; 3.4.1. Index Returns; 3.4.2. Stock Returns; 3.4.3. ARMA-GARCH

3.5. Comparison of Risk Measures for ARMA-GARCH Models3.5.1. Risk Prediction; 3.5.2. Backtestings; 4. Conclusion; A. DAX 30 Stocks; Bibliography

By studying the ability of the Normal Tempered Stable (NTS) model to fit thestatistical 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.

Description based upon print version of record.

Author notes provided by Syndetics

Florian Jacob obtained his Master's Degree in Business Engineering from the Karlsruhe Institute of Technology focusing on the application of tempered stable distributions on financial data and financial engineering.

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