TY  - GEN
AB  - 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 K©œmm 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. Contents ℓ́Ø Integrated Volatility ℓ́Ø Zero-inflated Data Generation Processes ℓ́Ø Algorithmic Text Forecasting Target Groups ℓ́Ø Teachers and students of economic science with a focus on financial econometrics<ℓ́Ø Executives and consultants in the field of business informatics and advanced statistics About the Author Dr. Holger K©œmm is research associate at the chair of statistics and quantitative methods in the economics & business department of the Catholic University Eichst©Þtt-Ingolstadt.
AU  - Kömm, Holger,
CN  - HG6024.A3
ID  - 806506
KW  - Financial institutions.
KW  - Economic forecasting.
KW  - Stock price forecasting.
KW  - Risk management.
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-658-12596-7
N2  - 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 K©œmm 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. Contents ℓ́Ø Integrated Volatility ℓ́Ø Zero-inflated Data Generation Processes ℓ́Ø Algorithmic Text Forecasting Target Groups ℓ́Ø Teachers and students of economic science with a focus on financial econometrics<ℓ́Ø Executives and consultants in the field of business informatics and advanced statistics About the Author Dr. Holger K©œmm is research associate at the chair of statistics and quantitative methods in the economics & business department of the Catholic University Eichst©Þtt-Ingolstadt.
SN  - 9783658125967
SN  - 3658125969
SN  - 3658125950
SN  - 9783658125950
T1  - Forecasting high-frequency volatility shocks :an analytical real-time monitoring system /
TI  - Forecasting high-frequency volatility shocks :an analytical real-time monitoring system /
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-658-12596-7
ER  -