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Aktuelles Forschung

Forschungskolloquium des Instituts für Wirtschaftswissenschaften

Ab dem Wintersemester 2021/2022 wird das Forschungskolloquium von Prof. Roxana Halbleib und Prof. Germain Gaudin organisiert.

Ab dem Wintersemester 2021/2022 wird das Forschungskolloquium des Instituts für Wirtschaftswissenschaften von Prof. Roxana Halbleib - gemeinsam mit Prof. Germain Gaudin und der Absolventenvereinigung Freiburger Wirtschaftswissenschaftler (FWW) - organisiert.

 

Prof. Halbleib Mitherausgeberin von AStA

Seit 2021 ist Professorin Roxana Halbleib Mitherausgeberin des Journals Advances in Statistical Analysis (AStA). 

 

Invited Session auf der CFE 2021

Prof. Halbleib organisiert die Invited Session "Latest developments in financial econometrics" auf der 15. Internationalen Konferenz Computational and Financial Econometrics (CFE) vom 18. bis zum 20. Dezember 2021 in London.

 

Paper von Prof. Halbleib vom JBES angenommen

Das Paper Realized Quantiles von Roxana Halbleib und Timo Dimitriadis wurde vom Journal of Business & Economic Statistics (JBES) zur Publikation angenommen.

Abstract 

In this paper, the authors propose a simple approach to estimate quantiles of daily financial returns directly from high-frequency data. We denote the resulting estimator as realized quantile (RQ) and use it to forecast tail risk measures, such as Value at Risk (VaR) and Expected Shortfall (ES). The RQ estimator is built on the assumption that financial logarithm prices are subordinated self-similar processes in intrinsic time. The intrinsic time dimension stochastically transforms the clock time in order to capture the real “heartbeat” of financial markets in accordance with their trading activity and/or riskiness. The self-similarity assumption allows to compute daily quantiles by simply scaling up their intraday counterparts, while the subordination technique can easily accommodate numerous empirical features of financial returns, such as volatility persistence and fat-tailedness. Our method, which is built on a flexible assumption, is simple to implement and exploits the rich information content of high-frequency data from another time perspective than the classical clock time. In a comprehensive empirical exercise, we show that our forecasts of VaR and ES are more accurate than the ones from a large set of up-to-date comparative models, for both, stocks and foreign exchange rates.