DOI Kapitel:
C. Förderung des wissenschaftlichen Nachwuchses
DOI Kapitel:II. Das WIN-Kolleg
DOI Kapitel:Sechster Forschungsschwerpunkt „Messen und Verstehen der Welt durch die Wissenschaft“
DOI Kapitel:3. Analyzing, Measuring and Forecasting Financial Risks by means of High-Frequency Data
DOI Seite / Zitierlink:https://doi.org/10.11588/diglit.55654#0249
3. Financial Risks (WIN-Programm)
Sechster Forschungsschwerpunkt
„Messen und Verstehen der Welt durch die Wissenschaft"
3. Anaiyzing, Measuring and Forecasting Financial Risks by means of
High-Frequency Data
Kollegiatin: Dr. Roxana Halbleib1
Mitarbeiter: Timo Dimitriadis1
1 Department of Economics, Universität Konstanz
The project aims at improving the predictability of financial risks by exploiting the
richness of the Information content of high-frequency data. The practical goal is
to minimize the possible losses that financial institutions may encounter during
turbulent financial times and by which each economy, as a whole, but also each
individual, as a tax-payer, is directly affected.
The first objective is to understand what causes extreme losses during fi-
nancial turmoil, such as the previous financial crisis. More precisely, the project
analyzes how the theoretical assumptions of the existing financial risk measures
restrict their empirical performance on real data. This research will lead to model
improvements and refinements that mirror the complexity of financial data, but,
simultaneously, remain feasible and easy to apply.
The second objective is to identify and analyze the high-frequency trading
specific Information that is most valuable in forecasting extreme financial risks.
The main focus is to analyze how this Information can be incorporated in accurate-
ly measuring the occurrence probabilities and sizes of extreme events on financial
markets. Of particular interest is the analysis within a multivariate context, given
that Investors face simultaneously many sources of risk. The research on these two
objectives will result in new risk measures that are able to predict extreme events
on financial markets. Consequently, the third objective is to assess the accuracy and
robustness of these new measures when applied to real data, especially to data typi-
cal to financial crises. This assessment is mainly quantitative. Of further interest it
would be to undergo a non-quantitative analysis on the performance of these mea-
sures from the perspective of the Investor, who implements them in practice.
251
Sechster Forschungsschwerpunkt
„Messen und Verstehen der Welt durch die Wissenschaft"
3. Anaiyzing, Measuring and Forecasting Financial Risks by means of
High-Frequency Data
Kollegiatin: Dr. Roxana Halbleib1
Mitarbeiter: Timo Dimitriadis1
1 Department of Economics, Universität Konstanz
The project aims at improving the predictability of financial risks by exploiting the
richness of the Information content of high-frequency data. The practical goal is
to minimize the possible losses that financial institutions may encounter during
turbulent financial times and by which each economy, as a whole, but also each
individual, as a tax-payer, is directly affected.
The first objective is to understand what causes extreme losses during fi-
nancial turmoil, such as the previous financial crisis. More precisely, the project
analyzes how the theoretical assumptions of the existing financial risk measures
restrict their empirical performance on real data. This research will lead to model
improvements and refinements that mirror the complexity of financial data, but,
simultaneously, remain feasible and easy to apply.
The second objective is to identify and analyze the high-frequency trading
specific Information that is most valuable in forecasting extreme financial risks.
The main focus is to analyze how this Information can be incorporated in accurate-
ly measuring the occurrence probabilities and sizes of extreme events on financial
markets. Of particular interest is the analysis within a multivariate context, given
that Investors face simultaneously many sources of risk. The research on these two
objectives will result in new risk measures that are able to predict extreme events
on financial markets. Consequently, the third objective is to assess the accuracy and
robustness of these new measures when applied to real data, especially to data typi-
cal to financial crises. This assessment is mainly quantitative. Of further interest it
would be to undergo a non-quantitative analysis on the performance of these mea-
sures from the perspective of the Investor, who implements them in practice.
251