Zeitliche Skalierung von Value-at-Risk und Volatilität in Aktien- und Optionsportfolios

Abstract

This thesis is about calculating and scaling the two risk measures value-at-risk (var) and volatility, which are used in financial risk management applications to quantify the risk in an asset or option portfolio. Different approaches to modelling financial time series, such as GARCH processes, iid random variables or the self-similarity assumption are considered and taken into account for generating a 1-day value-at-risk estimate. Starting with the square-root-of-time rule for converting a 1-day var estimate into a T-day (T >1) var estimate, different scaling laws - based on the different approaches of modeling financial risk factors - are presented, computed and compared.