Marcel Müller

Postdoctoral Researcher

Karlsruhe Institute of Technology

Welcome!

I am a researcher and lecturer in finance at the Karlsruhe Institute of Technology. My research combines econometric and machine-learning methods to study how bonds, funds, and ETFs are priced and how risks propagate across financial markets. My teaching covers investments, asset pricing, and financial markets. Outside academia, I have worked in consulting, banking, and for small and medium-sized enterprises, gaining hands-on experience in financial analysis, reporting, and data-driven decision-making.

Interests

Asset Pricing
Asset Management
Decentralized Finance
Machine Learning

Education

PhD in Finance, 2020
Karlsruhe Institute of Technology
MSc in Industrial Engineering and Management, 2013
Karlsruhe Institute of Technology
BSc in Industrial Engineering and Management, 2010
Karlsruhe Institute of Technology

News

[14/01/26] The paper Same Same but Different: The Risk Profile of Corporate Bond ETFs scheduled for presentation at the Frontiers of Factor Investing Conference 2026 at Lancaster University.

[30/08/25] Updated version of the paper Same Same but Different: The Risk Profile of Corporate Bond ETFs is online.

[31/03/25] New paper Same Same but Different: The Risk Profile of Corporate Bond ETFs is online.

Working Papers

Same Same But Different: The Risk Profile of Corporate Bond ETFs

with Johannes Dinger, Aleksandra Rzeźnik, and Marliese Uhrig-Homburg

We examine how corporate bond ETFs differ from the bond portfolios they hold. ETFs exhibit lower liquidity risk but higher intermediary risk, especially for high-yield funds, less liquid portfolios, and those served by weaker Authorized Participants. Using a structural decomposition, we show that ETFs are more exposed to intermediation supply shocks, whereas the underlying bonds are more exposed to demand shocks. A stylized model rationalizes these differences through partial segmentation between ETF and bond markets. Overall, corporate bond ETFs transform the risk profile of underlying bonds, creating a trade-off between liquidity and intermediary risk.

Climate Change, Energy Prices, and the Returns of Proof-of-Work vs. Proof-of-Stake Crypto Assets

with Fabian Eska

From December 2021, Proof-of-Work (PoW) crypto assets earn a systematic risk premium of 20% p.a. over Proof-of-Stake (PoS) crypto assets. This finding aligns with asset pricing theory, suggesting that energy-intensive assets, such as PoW assets, should be systematically riskier than their less energy-intensive PoS counterparts due to the cyclicality of energy prices. We show that contemporaneously, the systematic part of the returns from a portfolio that is long PoW and short PoS covaries negatively with innovations in climate change concerns and with innovations in the oil price. A one standard deviation increase in climate change concerns is associated with 25% of a standard deviation decrease in systematic PoW minus PoS returns. For an oil price shock, the corresponding number is 11%. Prior to 2021, PoS assets were systematically riskier than PoW assets. We show that this can be attributed to the cyclicality of the opportunity cost associated with PoS, which dominates the energy-related risk premium of PoW in this period of the sample.

Climate Change, Energy Prices, and the Returns of Proof-of-Work vs. Proof-of-Stake Crypto Assets

Expected Bond Liquidity

with Michael Reichenbacher, Philipp Schuster, and Marliese Uhrig-Homburg

(Revise & Resubmit)

We challenge the common assumption that current illiquidity is the best proxy for future market conditions by developing a forward-looking framework that forecasts the full distribution of U.S. corporate bond illiquidity using gradient-boosted trees. From these forecasts, we construct expected illiquidity (EI) and expected tail illiquidity (ETI), capturing downside liquidity risk. Relative to current illiquidity, EI reduces forecast errors by 21% and, in yield-spread regressions, exhibits a coefficient up to four times larger. ETI further strengthens this link. In cross-sectional asset-pricing tests, a simultaneous one-standard-deviation increase in both measures commands a 1.53% annual risk premium and 0.57% alpha.

Expected Bond Liquidity

Asset Pricing with Slanted News

with Marliese Uhrig-Homburg

We argue that media slant constitutes a source of ambiguity and show that the uncertainty stemming from slanted news is priced in the cross section of US stocks. Our identification of slanted news stocks is based on a combination of a news proxy using Wikipedia page view data and mutual fund managers' aggregated portfolio positions. We find that slanted news stocks earn a premium of roughly 1% in announcement months over their unslanted peers, which peaks on the announcement day itself. Our results further show that the premium is compensating for the exposure to a slanted news mimicking factor.

Asset Pricing with Slanted News

with Tobias Rosenberger and Marliese Uhrig-Homburg

We develop a model in which mutual fund investors chase CAPM alpha. Managers can generate CAPM alpha either by discovering mispricing, True Alpha, or by loading on risk factors that are beyond the scope of the CAPM, Fake Alpha,. Investors cannot distinguish between the two different types of alpha and thus confuse Fake Alpha with True Alpha. We show that this confusion ex-post causes negative CAPM alpha in equilibrium states. Empirical results support our theoretical predictions. The average CAPM alpha is significantly negative, and retail funds with large loads of Fake Alpha provide investors significantly lower CAPM alpha than their peers.

Fake Alpha

Drivers of Sovereign Recovery Risk

with Marliese Uhrig-Homburg

What determines the recovery of sovereign bond holders in the face of a credit event? This paper studies empirical determinants for sovereign recovery risk. Guided by theoretically backed hypotheses we use a sample of 102 past restructurings and empirically test the relation between haircut sizes and their economic drivers. We find a significant linkage of the haircut size to a debtor’s ability to repay as well as his willingness. Distinguishing between excusable and strategic defaulters in a new way enables us to empirically show that punishment is of markedly increased effectiveness amongst the strategic cohort. Based on these results we develop a forecasting-model for predicting haircuts conditional on the restructurings taking place within the year ahead and assess the performance of the model by applying it to a sample of the 45 restructurings observed from 1991 to present.

Drivers of Sovereign Recovery Risk

Contact

marcel.mueller@kit.edu
Blücherstraße 17, 76185 Karlsruhe, Germany
Enter building from the north and take the stairs to office 234 on Floor 2