Fabian Woebbeking

Fabian Woebbeking
Goethe-Universität Frankfurt am Main · Department of Finance

Dr.

About

12
Publications
2,975
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
20
Citations
Additional affiliations
September 2014 - present
Frankfurt School of Finance & Management
Position
  • Lecturer
Description
  • See: http://woebbeking.info/
January 2014 - November 2017
Goethe-Universität Frankfurt am Main
Position
  • PhD Student
Education
October 2014 - December 2017
March 2013 - August 2014

Publications

Publications (12)
Article
Full-text available
Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. Th...
Article
In 2012, JPMorgan accumulated a USD 6.2 billion loss on a credit derivatives portfolio, the so-called “London Whale”, partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for scenario-based stress testing...
Preprint
Full-text available
We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayes...
Article
Full-text available
By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from gran...
Preprint
Full-text available
In 2012, JPMorgan accumulated a USD 6.2 billion loss on a credit derivatives portfolio, the so-called "London Whale", partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for scenario-based stress-testing...
Article
Starting from well-known empirical stylised facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalised innovations. The...

Network

Cited By

Projects

Project (1)
Project
Starting from well-known empirical stylised facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalised innovations. These generalised innovations may for example follow a Student t, a generalized hyperbolic (GH), an alpha-stable or a Generalised Pareto (GPD) distribution. Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behavior in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2702275