Publications

You can also find a complete list of my publications on my Google Scholar profile.

Peer-Reviewed

The Limits of Local Laws in Global Supply Chains: Cutting Ties or “Edutrading” Procurement Partners? Permalink

Published in Journal of Development Economics, Vol. 182 (June), 2026

We study the procurement patterns of non-listed firms and examine how these often-overlooked, yet pivotal players in global supply chains adjust their sourcing when they anticipate accountability for externalities beyond their organizational boundaries. Using granular customs data and a surprise information release about the German Supply Chain Due Diligence Act, product-level regressions reveal that importing firms are 3.5 percentage points less likely to source a product from countries where the relevant production sector exhibits elevated ESG-related risks, suggesting that firms tend to cut ties with higher-risk suppliers. The effects are concentrated among firms with well-diversified supplier networks for a product and higher profitability, suggesting they have the necessary flexibility to respond quickly to anticipated regulatory pressure. Our findings suggest that mandates requiring firms to incorporate broad sustainability considerations into their operational decisions may have limits, particularly for non-listed firms.

Recommended citation: Hendrik Keilbach, Michael Koetter, Melina Ludolph, and Fabian Woebbeking (2026). "The Limits of Local Laws in Global Supply Chains: Cutting Ties or “Edutrading” Procurement Partners?" Journal of Development Economics, 182 (June), 103804. DOI: 10.1016/j.jdeveco.2026.103804.

“Let Me Get Back to You” — A Machine Learning Approach to Measuring NonAnswers Permalink

Published in Management Science, Vol. 69 (10), 2023

Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.

Recommended citation: Andreas Barth, Sasan Mansouri, and Fabian Woebbeking (2023). "Let Me Get Back to You" — A Machine Learning Approach to Measuring NonAnswers. Management Science, 69(10).

Correlation Scenarios and Correlation Stress Testing Permalink

Published in Journal of Economic Behavior and Organization, Vol. 205 (January), 2023

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 Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or Highest Density Regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.

Recommended citation: Natalie Packham and Fabian Woebbeking (2023). "Correlation Scenarios and Correlation Stress Testing." Journal of Economic Behavior and Organization, 205 (January).

Cryptocurrency Volatility Markets Permalink

Published in Digital Finance, Vol. 3 (3), 2021

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 granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet share common shocks.

Recommended citation: Fabian Woebbeking (2021). "Cryptocurrency Volatility Markets." Digital Finance, 3(3).

A Factor-model Approach for Correlation Scenarios and Correlation Stress Testing Permalink

Published in Journal of Banking and Finance, Vol. 101 (April), 2019

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 of correlations. We derive a number of analytical results related to a portfolio of homogeneous assets. Using the concept of Mahalanobis distance, we show how to identify adverse scenarios of correlation risk. In addition, we demonstrate how correlation and volatility stress tests can be combined. As an example, we apply the factor-model approach to the “London Whale” portfolio and determine the value-at-risk impact from correlation changes. Since our findings are particularly relevant for large portfolios, where even small correlation changes can have a large impact, a further application would be to stress test portfolios of central counterparties, which are of systemically relevant size.

Recommended citation: Natalie Packham and Fabian Woebbeking (2019). "A Factor-model Approach for Correlation Scenarios and Correlation Stress Testing." Journal of Banking and Finance, 101 (April). DOI: 10.1016/j.jbankfin.2019.01.020.

Tail-risk Protection Trading Strategies Permalink

Published in Quantitative Finance, Vol. 17 (5), 2017

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. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). 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 behaviour 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.

Recommended citation: Natalie Packham, Jochen Papenbrock, Peter Schwendner, and Fabian Woebbeking (2017). "Tail-risk Protection Trading Strategies." Quantitative Finance, 17(5). DOI: 10.1080/14697688.2016.1249512.

Working Papers

The Price of Beauty: Biodiversity Effects on Residential Housing Markets Permalink

Published in IWH Discussion Papers, No. 21, 2025

We study how and why local biodiversity affects residential property values. Leveraging remotely sensed greenness indicators and a novel dataset of granular property listings, we examine how changes in vegetation load on real estate prices. Hikes in greenness are associated with higher listing prices, fewer properties listed, and reduced liquidity in housing markets. These results suggest that price hikes in housing markets are driven by supply-side constraints instead of a “greenium” that buyers might be willing to pay due to innate preferences. Exogenous zoning shocks to foster biodiversity corroborate the presence of supply side constraints as price drivers in residential housing markets. Our findings emphasize the need to calibrate biodiversity and (social) housing policy objectives more explicitly.

Recommended citation: Michael Koetter, Birte Winter, and Fabian Woebbeking (2025). "The Price of Beauty: Biodiversity Effects on Residential Housing Markets." IWH Discussion Papers, No. 21. DOI: 10.18717/dpb9v0-zk76.

Information Flow and Market Efficiency - The Economic Impact of Precise Language Permalink

Published in IWH Discussion Papers, No. 13, 2025

This paper examines the impact of complex yet precise language, particularly financial jargon, on information dissemination and ultimately market efficiency. As a natural laboratory, we analyze the information exchanged during earnings conference calls, where we instrument jargon with the Plain Writing Act of 2010. Our findings suggest that the Act’s promotion of plain language usage results in a reduction in complex financial jargon for US firms. However, in contrast to the presumed benefits of accessible language, this reduction in jargon is associated with a decrease in market efficiency, implying that the Act may inadvertently hinder information flow. This finding is particularly important at the juncture where human-generated information is received by machines, which are known to be vunerable to ambiguous inputs.

Recommended citation: Andreas Barth, Sasan Mansouri, and Fabian Woebbeking (2025). "Information Flow and Market Efficiency - The Economic Impact of Precise Language." IWH Discussion Papers, No. 13. DOI: 10.18717/dpds-2j29.

Contractionary Macroprudential Policy, Collateral Valuation, and Risk-shifting in EU Banking Permalink

Published in IWH Discussion Papers, No. 4, 2025

We study real estate lending responses to tighter macroprudential policy (MPP) in the form of lower required loan-to-value (LTV) ratios. Contract details of 2.4 million mortgage loans originated between 2008 and 2020 reveal significantly fewer new loan issuances in response to contractionary MPP, commensurate with an average reduction in aggregate lending of 21 percent. Loan-level analyses reveal, however, that banks comply with lower LTVs by systematically more benevolent valuations of residential real estate pledged as collateral instead of reducing loan size. Exploiting earthquakes as plausible exogenous shocks to property values corroborates these risk-shifting patterns by banks in the form of inflated property valuations after LTV shocks.

Recommended citation: Michael Koetter, Felix Noth, and Fabian Woebbeking (2025). "Contractionary Macroprudential Policy, Collateral Valuation, and Risk-shifting in EU Banking." IWH Discussion Papers, No. 4. DOI: 10.18717/dp8vbg-3v09.