Past Events

The predictive power of the variance risk premium (VRP) for realized volatility depends critically on aligning the maturity of the option-implied variance measure with the forecast horizon. We exploit the CBOE's progressive expansion of the SPY option expiration calendar — from weekly Friday expirations through Wednesday and Monday additions to daily expirations in 2022 — as a natural experiment. Using SPY options from 2013 to 2025, we construct two VRP measures: one restricted to Friday-expiring weekly options (replicating the pre-expansion constraint) and one using the best-available expiration. Within a nested HAR framework evaluated across one-, five-, and ten-day horizons, we find that VRP inclusion reduces out-of-sample mean squared prediction error by 12–18%, with additional significant gains from maturity alignment concentrated at short horizons and in the post-2022 daily-expiry regime. Statistical improvements are corroborated by realized utility gains. Our findings establish a direct link between exchange design decisions and forecast quality.

This research explores how mobility big data can be used to understand urban travel behavior and evaluate policy implications. The data sources include public transport smart cards, public bike systems, and mobile phone data. In particular, the transport card–based analysis demonstrates the benefits of Seoul’s integrated fare system with free transfers. The presentation will also briefly introduce data from autonomous delivery robots deployed in Seoul as a potential emerging urban data source.

Optimism—the expectation that future events will be better than they actually are—is a widely spread cognitive bias. How can this bias survive? Previous studies have mostly claimed that it survives because it benefits the optimist. However, I show that optimism may have evolved not to benefit the optimist but her family or community, who share her genes. I present an agent-based simulation model in which optimism serves as a proactive/pro-exploratory bias, leading the optimist to explore more opportunities than an unbiased individual. This may harm the optimist herself, but it benefits her relatives who can learn from, imitate, or benefit from her explorations in other ways. In this way, kin selection can explain the origin and persistence of optimism.

Germany was the first country worldwide to introduce a structured reimbursement pathway for digital therapeutics (DiGA), effectively allowing physicians to prescribe smartphone apps covered by statutory health insurance. In this seminar, Simon Reif presents recent research evaluating whether this pioneering regulation successfully incentivized innovation in the digital health market.  Using high-frequency data from the Apple App Store and synthetic control methods, the study analyzes the market entry of health apps following the policy's introduction. The findings reveal a significant increase in the number of German-language digital therapeutics; however, this quantity did not translate into a broader diversity of medical conditions treated. Furthermore, the surge was largely driven by apps that monetize patient data for advertising rather than high-quality applications backed by scientific evidence. The presentation will discuss the implications of these results for designing policy that balances market incentives with patient privacy and care quality.

Teaser: Simon will be holding a separate, short workshop on the reproducible research tools used at his institute. Join us to learn how to implement these transparent workflows in your own analysis. 

Do people think enough before acting? We study this in a first laboratory experiment where participants can either use a known but tedious method or search for a more efficient strategy whose existence they are made aware of, but whose nature is not disclosed. We exogenously induce early search through a "Freeze" intervention, where participants must spend 90 seconds studying solved example sequences from which the efficient strategy can be inferred. In the "No Freeze" condition, participants see the same examples but are free to move on to the task immediately. We find that Freeze significantly increases discovery rates, however not uniformly across specifications, and leads to more efficient task performance conditional on discovery. At the same time, given our calibration of time horizon and task difficulty, Freeze does not improve overall performance, as the gains from discovery are insufficient to offset the time spent searching. Conditional on discovery, outcomes are similar between the two conditions, while unsuccessful search reduces the overall output in "Freeze" due to lost time. These results highlight a trade-off between exploration and exploitation: forcing individuals to think before acting can backfire when the probability of discovering a better strategy is low or when too little time remains to exploit the discovery. The talk concludes with follow-up ideas on why individuals fail to search and how to better target efficient exploration.

Motherhood is associated with persistent penalties in both labour market outcomes and health. However, the role of family policies in mitigating these effects, particularly with respect to maternal mental health, remains unclear. This paper examines the impact of extending paternity leave on maternal mental health. Using administrative health records covering all publicly funded births in Catalonia (Spain) between 2006 and 2024, we implement a local difference-in-differences design around the April 1st, 2019 reform, which introduced partially mandatory paternity leave for fathers. The analysis focuses on first time mothers giving birth within a three-month window around the reform cutoff and compares them to a control cohort from 2016. Maternal mental health is measured using diagnoses and prescription records for stress-related, mood-related, and broader mental health conditions drawn from primary care and pharmacy data. We find an increase in stress-related diagnoses and in the use of antidepressants and anxiolytics from the second year after childbirth among mothers exposed to the reform. These effects are concentrated among older mothers (above the median age of 31) and among those who do not have a second child within four years after birth. We find suggestive evidence that these patterns may be related to increased healthcare use and to stress-related mechanisms, including delayed fertility, changes in labour market attachment, and relationship instability. Ongoing work will extend the analysis to additional mental health outcomes and to subsequent paternity leave reforms

In 2025, a survey of 8,000 students and employees at ten Czech universities analysed commuting behaviour using a logit model of mode choice. Public transport dominated (47%), followed by car (32%) and walking or cycling (21%), with higher car use among employees and higher-income respondents. Distance, age (non-linear effect), and children significantly increased the likelihood of car use, particularly beyond 10 km. Differences across universities reflected urban size and topography, with higher public transport shares in large agglomerations. Key barriers to sustainable mobility were habits, distance, and public transport quality, while faster connections, lower costs, improved cycling infrastructure, and limited parking were identified as main drivers of modal shift.

What happens when firms merge, and how do economists determine whether competition is harmed? This lecture offers an accessible introduction to merger analysis, combining theory with a real-world case study. It explores how economists define markets, measure competitive pressure, and assess the likely impact of mergers on consumers. The session also highlights how data-driven tools and empirical methods are increasingly central to competition policy. 

We consider the problem of identifying the optimal subset of past observations to be used for estimating the covariance matrix of asset returns when computing the global minimum variance portfolio. While the usual approach simply employs either all the available past returns or a rolling window of fixed size, we argue that such solutions are inefficient, and we instead propose to dynamically adjust the estimation window. To this end, we employ the PELT changepoints detection algorithm to identify outdated observations. We then set up a cross-validation procedure to determine the minimum number of upto-date observations that individual assets must have to be considered when estimating the covariance matrix. Assets that do not reach the selected threshold are excluded from the optimization and assigned equal weights. In an empirical application, our solution achieves a higher Sharpe ratio compared to both an expanding and rolling window estimation of the global minimum variance portfolio, and it also compares well with other benchmarks.

When financial markets become turbulent, how do investors price the fear of uncertainty itself? Does that fear spread across different corners of the global economy? This research investigates the Variance Risk Premium (VRP), defined as the gap between what investors expect volatility to be and what it actually turns out to be, across five distinct asset classes: equities, fixed income, foreign exchange, cryptocurrencies, and commodities. While VRP has been studied extensively in stock markets, its behaviour in bond markets, currency markets, commodity markets, and the rapidly growing cryptocurrency space remains poorly understood — particularly in a unified, comparative framework.
Using intraday and high-frequency data spanning 2018–2025 — a period capturing COVID-19, the 2022 rate hiking cycle, and multiple crypto boom-bust episodes — the research employs advanced econometric models including GARCH, HAR, VAR, VECM, and Dynamic Conditional Correlation to examine how VRP behaves, what drives it, and whether volatility risk in one market spills over into others.

This lecture examines pharmaceutical regulation and the incentives for innovation from an international perspective, highlighting the public good nature of health care innovation and its cross-border diffusion. It summarizes empirical evidence on how push and pull incentives shape R&D investment, innovation, and global access. It emphasizes the role of strategic interdependencies and spillovers, including free riding in R&D financing, learning-by-doing effects, drug shortages, reference pricing, and parallel trade. We then provide new evidence on the international spillovers of pull incentives on innovation showing that international cooperation and innovative institutions are necessary to better align national regulations with the global objective of sustaining pharmaceutical innovation.

Before the lecture, there will be small refreshments at 4:00 pm. During this time, you can discuss with each other and meet the speaker.

Our cleantech startup strategy research examines how cleantech start-ups cope with resource constraints while pursuing both economic and environmental objectives. We focus on how firms can achieve balanced growth without mission drift. Building on bricolage theory, we conceptualize firms’ responses as configurations of hybrid harvesting strategies, including partnerships, networking, local resource utilisation, customer empowerment, and inclusive employment. Using fuzzy-set Qualitative Comparative Analysis (fsQCA) on data from a questionnaire survey and qualitative interviews with cleantech start-ups, we explore how different strategy configurations perform under varying resource constraints. The research is conducted within the project “Resilience and impact of clean-tech start-ups in Bavaria and the Czech Republic” in collaboration with the Technical University of Munich. Preliminary findings suggest multiple pathways to balanced growth, but also configurations associated with mission drift, indicating that outcomes depend on both strategy combinations and the context in which they are applied.

This talk explores how implementation science and behavioral nudges can drive the successful design, adoption, and impact of digital health interventions for chronic disease management. Using the pediatric obesity pandemic as a focal point, we examine a novel eHealth solution launched within a major US safety-net hospital system for families with children ages 6–12 diagnosed with overweight or obesity. Key theoretical frameworks and evidence-based practices will be introduced alongside practical methodological considerations. The case study invites the audience to critically engage with real-world challenges—highlighting how behavioral insights, technology, and context-sensitive methods intersect to shape research outcomes, patient care experiences, and broader societal wellbeing. Participants will gain actionable strategies for integrating behavioral economics and scientific rigor into digital health, with lessons applicable to business, marketing, and health innovation.

Therese Nilsson is a Professor of Economics affiliated with the Department of Economics at Lund University and the Research Institute of Industrial Economics (IFN). She also serves as an Academic Programme Advisor for the Trade Policy Training Institute in Africa (TRAPCA). Her research primarily lies at the intersection of health and education economics, with additional contributions to cultural and institutional economics.

Eugen is an Associate Professor of Practice in Behavioral & Decision Sciences at the University of Pennsylvania and a Visiting Professor at Stanford University. His research lies at the intersection of behavioral economics, sociology, and political psychology, with a focus on social norms, polarization, and their effects on social preferences and cooperation. From 2022–2023, he served on the White House Behavioral Science Team, working on large-scale RCTs related to the opioid epidemic.

Does the legal protection of free speech change how managers use stock prices to guide corporate investment? We study this question using the staggered enactment of anti-SLAPP laws across U.S. states from 1992 to 2022, a setting that lowers the litigation cost of speaking out and empowers journalists, NGOs, employees, and short sellers to bring firm-specific information into public view. In a stacked difference-in-differences design, we find that stronger free-speech protections reduce the sensitivity of corporate investment to Tobin's Q by roughly 34%. We interpret this through revelatory price efficiency, the component of price informativeness that managers actually learn from: anti-SLAPP laws expand public information about matters managers already know, crowding out private information acquisition about the growth opportunities they most need prices to reveal. Consistent with this composition-shift mechanism, the effect is stronger for firms with higher pre-treatment informed trading and in states with broader statutory protections, and it survives controls for forecasting price efficiency. Channel tests show that anti-SLAPP enactment raises fundamental investor information acquisition, while analyst forecasts pin down the crowded-out information as growth-related: long-horizon forecast accuracy deteriorates even as short-horizon accuracy is unchanged. Cross-sectionally, the decline is most pronounced for firms with high discretionary accruals, low disclosure frequency, and high CEO ability, characteristics tied to greater pre-existing reliance on managerial learning from prices. Our findings reveal an unintended cost of transparency reform: laws that strengthen public discourse and corporate accountability can simultaneously weaken the informational feedback from stock prices to real investment decisions.

We investigate whether audit clients’ climate-related risks disclosure influence audit planning and fees. Using the Task Force on Climate-related Financial Disclosures (TCFD) framework in the UK, we distinguish between clients who mandatorily comply with TCFD (mandatory clients) and those who do not. We find that mandatory TCFD adoption is associated with higher audit fees, but fewer key audit matters disclosed by auditors, suggesting that enhanced climate-related disclosures reduce perceived audit risk through greater transparency, yet do not reduce audit pricing. Cross-sectional analyses reveal important heterogeneity in auditors’ responses to mandatory TCFD adoption. The increase in audit fees is concentrated among clients of small audit firms, while clients of large audit firms experience fee reductions. In addition, auditors apply higher materiality levels to clients with high climate impact, consistent with reduced audit effort. Further tests indicate that mandatory TCFD adoption does not translate into improvement in actual environmental performance. Given the growing interest in climate-related risks in practice and research, our study sheds light on how auditors respond to changes in climate-related disclosure requirements.