Selected work

Representative Papers

Arbitrage and rents in European long-term transmission rights

Jul 30, 2026

This study addresses the persistent underpricing of Long-Term Transmission Rights (LTTRs) in Europe relative to forward market prices, suggesting constrained arbitrage and the presence of implicit rents. Combining option pricing theory with panel regression models, the paper empirically examines how LTTR holders lock in arbitrage profits by shorting forward contracts in import markets and going long in export markets, using EEX futures data from 2018 to 2025. The analysis reveals, for the first time, that the LTTR mechanism systematically generates rents, resulting in an implicit transfer from electricity consumers to rights holders. Furthermore, it documents significant shifts in German power forward prices following LTTR auctions, highlighting inefficiencies in the current regulatory design.

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Competitive effects of transmission constraints in the German electricity market

Jul 01, 2026

This study investigates how cross-border transmission constraints exacerbate the exercise of market power in Germany’s wholesale electricity market. Leveraging data from gas- and coal-fired generation units between 2022 and 2024, the authors employ a two-stage residual inclusion (2SRI) instrumental variable approach, using regional net position limits as a proxy for transmission congestion severity. Strategic generator behavior—such as capacity withholding and price suppression—is identified by deviations between actual dispatch and competitive benchmarks. The analysis provides the first empirical evidence that, under binding transmission constraints, each 1 GW reduction in available import or export margin significantly increases the likelihood of suspected capacity withholding and price suppression by 15% and 16%, respectively. These findings offer quantitative support for enhancing cross-border grid interconnectivity to improve market efficiency and fairness.

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De-risking renewable energy investments: Assessing contract design and project finance using operational wind park data

May 22, 2026

This study addresses the critical role of revenue stability in renewable energy project finance and examines how different contract-for-difference (CfD) designs can reconcile income certainty with market price signals. Using hourly generation data from 63 onshore wind farms in Germany over 2013–2024, the authors integrate a project finance model with three CfD structures—two-way, one-way, and financial—to simulate cash flows and assess their impacts on revenue volatility, debt capacity, and levelized cost of electricity. The findings indicate that financial CfDs substantially reduce revenue risk and enhance debt financing potential while preserving responsiveness to wholesale electricity prices, achieving hedging effectiveness comparable to traditional two-way CfDs. The results demonstrate that well-designed public contracts can effectively substitute for absent long-term hedging markets without compromising market efficiency, offering empirical support for renewable energy policy design.

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Identifying attributions of causality in political text

Dec 02, 2025

Political science has long lacked systematic identification and analysis of causal explanations in political texts; existing methods are fragmented, context-specific, and entail high annotation costs. Method: We propose the first structured causal relation detection framework tailored to political texts, integrating a lightweight causal language model with low-resource NLP techniques to enable high-precision, automatic extraction of cause-effect pairs from minimal annotated data. Contribution/Results: The framework achieves strong generalizability, near-human coding accuracy (F1 > 0.85), and cross-text robustness, validated across diverse political corpora—including news articles, policy commentaries, and legislative records. We release the first large-scale, structured dataset of political causal claims, substantially reducing manual coding effort. This work establishes a reproducible, scalable, and automated infrastructure for large-scale quantitative studies of causal attribution patterns in political discourse.

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An empirical estimate of the electricity supply curve from market outcomes

Nov 28, 2025

This paper addresses the challenge of empirically estimating supply curves and price elasticity in electricity markets. We propose a nonparametric, data-driven approach grounded in observed market outcomes, circumventing biases from conventional engineering-based assumptions. Using high-frequency electricity price and generation data from the German market (2019–2024), we reconstruct counterfactual supply curves directly from observations. Methodologically, we innovatively integrate time-series clustering of price–quantity pairs with clustering of fundamental drivers to robustly identify stable supply regimes and their structural shifts. Our results reveal that during the energy crisis, fuel price shocks drove rapid, nonlinear regime transitions—highlighting the inherent nonlinearity in supply response dynamics. The empirically derived supply curves enable causal inference and policy counterfactual analysis, offering a novel paradigm for assessing market power and improving price forecasting.

0 citationsRead paper

Recent publications

Latest Papers

Arbitrage and rents in European long-term transmission rights

Jul 30, 2026

This study addresses the persistent underpricing of Long-Term Transmission Rights (LTTRs) in Europe relative to forward market prices, suggesting constrained arbitrage and the presence of implicit rents. Combining option pricing theory with panel regression models, the paper empirically examines how LTTR holders lock in arbitrage profits by shorting forward contracts in import markets and going long in export markets, using EEX futures data from 2018 to 2025. The analysis reveals, for the first time, that the LTTR mechanism systematically generates rents, resulting in an implicit transfer from electricity consumers to rights holders. Furthermore, it documents significant shifts in German power forward prices following LTTR auctions, highlighting inefficiencies in the current regulatory design.

0 citationsRead paper

Competitive effects of transmission constraints in the German electricity market

Jul 01, 2026

This study investigates how cross-border transmission constraints exacerbate the exercise of market power in Germany’s wholesale electricity market. Leveraging data from gas- and coal-fired generation units between 2022 and 2024, the authors employ a two-stage residual inclusion (2SRI) instrumental variable approach, using regional net position limits as a proxy for transmission congestion severity. Strategic generator behavior—such as capacity withholding and price suppression—is identified by deviations between actual dispatch and competitive benchmarks. The analysis provides the first empirical evidence that, under binding transmission constraints, each 1 GW reduction in available import or export margin significantly increases the likelihood of suspected capacity withholding and price suppression by 15% and 16%, respectively. These findings offer quantitative support for enhancing cross-border grid interconnectivity to improve market efficiency and fairness.

0 citationsRead paper

De-risking renewable energy investments: Assessing contract design and project finance using operational wind park data

May 22, 2026

This study addresses the critical role of revenue stability in renewable energy project finance and examines how different contract-for-difference (CfD) designs can reconcile income certainty with market price signals. Using hourly generation data from 63 onshore wind farms in Germany over 2013–2024, the authors integrate a project finance model with three CfD structures—two-way, one-way, and financial—to simulate cash flows and assess their impacts on revenue volatility, debt capacity, and levelized cost of electricity. The findings indicate that financial CfDs substantially reduce revenue risk and enhance debt financing potential while preserving responsiveness to wholesale electricity prices, achieving hedging effectiveness comparable to traditional two-way CfDs. The results demonstrate that well-designed public contracts can effectively substitute for absent long-term hedging markets without compromising market efficiency, offering empirical support for renewable energy policy design.

0 citationsRead paper

Identifying attributions of causality in political text

Dec 02, 2025

Political science has long lacked systematic identification and analysis of causal explanations in political texts; existing methods are fragmented, context-specific, and entail high annotation costs. Method: We propose the first structured causal relation detection framework tailored to political texts, integrating a lightweight causal language model with low-resource NLP techniques to enable high-precision, automatic extraction of cause-effect pairs from minimal annotated data. Contribution/Results: The framework achieves strong generalizability, near-human coding accuracy (F1 > 0.85), and cross-text robustness, validated across diverse political corpora—including news articles, policy commentaries, and legislative records. We release the first large-scale, structured dataset of political causal claims, substantially reducing manual coding effort. This work establishes a reproducible, scalable, and automated infrastructure for large-scale quantitative studies of causal attribution patterns in political discourse.

0 citationsRead paper

An empirical estimate of the electricity supply curve from market outcomes

Nov 28, 2025

This paper addresses the challenge of empirically estimating supply curves and price elasticity in electricity markets. We propose a nonparametric, data-driven approach grounded in observed market outcomes, circumventing biases from conventional engineering-based assumptions. Using high-frequency electricity price and generation data from the German market (2019–2024), we reconstruct counterfactual supply curves directly from observations. Methodologically, we innovatively integrate time-series clustering of price–quantity pairs with clustering of fundamental drivers to robustly identify stable supply regimes and their structural shifts. Our results reveal that during the energy crisis, fuel price shocks drove rapid, nonlinear regime transitions—highlighting the inherent nonlinearity in supply response dynamics. The empirically derived supply curves enable causal inference and policy counterfactual analysis, offering a novel paradigm for assessing market power and improving price forecasting.

0 citationsRead paper