Measuring and modeling risk using high-frequency data

Arbeitspapier

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management.The recent availability of high-frequency data allows for refined methods in this field.In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns.In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures af systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorpration and the DJIA index.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2008,045

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods
• Subject: Realized volatility ; realized betas ; volatility modeling ; Finanzmarkt ; Volatilität ; Messung ; Beta-Faktor ; Zeitreihenanalyse ; Ökonometrisches Modell ; Schätzung ; Börsenumsatz ; Mikrostrukturanalyse ; USA

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Härdle, Wolfgang Karl ; Hautsch, Nikolaus ; Pigorsch, Uta
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/25285
• Last update: 10.03.2025, 11:41 AM CET