Forecasting High-Frequency Volatility Shocks

1. Front Matter

   Pages I-XXIX

   PDF

2. Introduction

   • Holger Kömm

   Pages 1-9

3. General Framework

   • Holger Kömm

   Pages 11-28

4. Integrated Volatility

   • Holger Kömm

   Pages 29-46

5. Zero-inflated Data Generation Processes

   • Holger Kömm

   Pages 47-64

6. Volatility Shock Causing Incidents

   • Holger Kömm

   Pages 65-86

7. Algorithmic Text Forecasting

   • Holger Kömm

   Pages 87-97

8. Benchmarking

   • Holger Kömm

   Pages 99-121

9. Monitoring

   • Holger Kömm

   Pages 123-146

10. Conclusion

    • Holger Kömm

    Pages 147-153

11. Back Matter

    Pages 155-171

    PDF

This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system, using first, a sequence of competing estimators to compute the unobservable volatility; second, a new two-state Markov switching mixture model for autoregressive and zero-inflated time-series to identify structural breaks in a latent data generation process and third, a selection of competing pattern recognition algorithms to classify the potential information embedded in unexpected, but public observable text data in shock and nonshock information. The monitor is trained, tested, and evaluated on a two year survey on the prime standard assets listed in the indices DAX, MDAX, SDAX and TecDAX.

• Integrated Volatility
• Zero-inflated Data Generation Processes
• Algorithmic Text Forecasting
• Monitoring
• Time Series and Textual Data

• Wirtschaftswissenschaftliche Fakultät, Kath. Universität Eichstätt-Ingolstadt, Ingolstadt, Germany

  Holger Kömm

Dr. Holger Kömm is research associate at the chair of statistics and quantitative methods in the economics & business department of the Catholic University Eichstätt-Ingolstadt. 

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