Forecasting High-Frequency Volatility Shocks

Holger Kömm

ISBN 10: 3658125950 / ISBN 13: 9783658125950

Published by Springer Fachmedien Wiesbaden, Springer Fachmedien Wiesbaden Feb 2016, 2016

About this Item

Neuware -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.Springer Gabler in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg 204 pp. Englisch.

Seller Inventory # 9783658125950

Bibliographic Details

• Title: Forecasting High-Frequency Volatility Shocks
• Publisher: Springer Fachmedien Wiesbaden, Springer Fachmedien Wiesbaden Feb 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3658125950
• ISBN 13: 9783658125950
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

About the Author

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.