Contributions published at Department of Banking and Finance (Pablo Koch Medina)

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat-tailed and heteroscedastic. The conditional return distribution is modeled by an elliptical multivariate generalized hyperbolic distribution, allowing for fast parameter estimation, via an expectation-maximization algorithm and a semi-closed form of the risk contributions. The efficient computation of non-Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish-Fisher-type approximations. Accounting for fat-tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis. Although risk parity portfolios are surprisingly robust to the misuse of the Gaussian distribution, a more realistic model for conditional returns and time-varying volatilies can improve risk-adjusted returns, reduces turnover during periods of market stress and enables the use of a holistic risk model for portfolio and risk management.

This paper examines how and why initially well-intentioned organizations can find themselves ethically adrift. Drawing on a qualitative analysis of the Volkswagen emissions scandal, we investigate what planted the seed of deception, why the company’s deceptive behavior changed from one stage to the next, and which factors catalyzed these shifts. Furthermore, we scrutinize the management’s response to the disclosure of their misconduct. We employ a cybernetic perspective, envisioning the dynamics of deception as a multi-stage, goal-directed process, in which shifts in behavior are driven by a need to resolve discrepancies between past and anticipated future states. Our analysis reveals two dominant dysfunctional feedback loops underlying the company’s ethical descent. We conclude by discussing the theoretical implications of this case study and derive propositions about the emergence of such dysfunctional feedback loops, as well as strategies that may help to de-escalate such situations by strengthening ethical feedback loops.

High-tech innovative firms often face difficulties in obtaining bank loans due to technological uncertainties and information asymmetries. This is because banks traditionally specialise in specific industry domains to mitigate risks. This column uses comprehensive patent data and syndicated bank loan data to show that banks also acquire technological expertise that extends beyond industry lines. This expertise is robustly related to lower loan spreads, and benefits both banks and future borrowers. Fostering collaboration between banks and innovative firms can boost productivity and can ultimately benefit both the economy and society.

Scope 3 greenhouse gas (GHG) emissions are frequently the most relevant element of a company's total emissions since they account for more than eighty percent. However, they are difficult to calculate since many stakeholders in the value chain are involved and emission data are usually not shared among them. Sustainable finance could provide a link to this discussion by providing data, digital data infrastructures and evaluation instruments. However, the existing research today is either limited to analyzing the levels of scope 3 emissions or to calculating them based on different measurement methods. How to implement scope 3 emissions reporting by solving the data sharing challenge remains mainly unexplored. This paper aims to close this gap by developing an approach, which chooses sustainable finance as a connecting element that (1) combines different calculation methods, (2) integrates cross-value chain data from different stakeholders and (3) combines primary and secondary data in a single model. The approach was developed in a prototype that uses real world data from collaboration with the UN-convened Net-Zero Asset Owner Alliance to evaluate its applicability. The findings of the prototype indicate that a digital data infrastructure can improve the calculation of scope 3 GHG emissions by improving data availability, accessibility and reliability and at the same time shows that the calculations are only as good as the data, which fuels this calculation. With this, the paper contributes to the theoretical and practical discussion about scope 3 GHG emission data.

This study analyzes price discrimination and household switching in the residential mortgage market. Using a unique proprietary micro dataset from Norway, we examine the factors that influence a bank’s choice to counter an offer from a competing bank and the difference between the loan rate paid by current clients when receiving a competing offer from another bank and the concurrent best rate offered to new customers by the current bank. The estimates show that a bank employs internal information to decide how to counter a competing offer and that current clients pay approximately 20 basis points more than new customers. We surmise that new regulations and digitalization enhance transparency and can reduce the rate differential. However, introducing new banking products and changes in the timing of rate differentiation - from immediate upfront to gradually over time - may be used to maintain a constant rate differential.

We interviewed 379 bank CEOs in 20 emerging markets to identify their banks’ main competitors. We show that banks are more likely to identify another bank as a main competitor in small-business lending when both banks are foreign owned or relationship oriented; when there exists a large spatial overlap in their branch networks and when the potential competitor has fewer hierarchical layers. We then construct a novel bilateral competition measure at the locality level and assess how well it explains geographic variation in firms’ credit constraints. We show that intense bilateral bank competition tightens local credit constraints, especially for small firms, as competition may impede the formation of lending relationships.

This study examines the stock price reactions of environmentally responsible stocks during the onset of the 2023 banking crisis, triggered by the collapse of Silicon Valley Bank (SVB). Our findings indicate that stocks poised to benefit from the shift to a low-carbon economy underperformed during the 2023 crisis. This suggests that investors anticipate a slowdown in climate tech development due to distress in the banking sector. Our results underscore the significance of considering not only the influence of the climate crisis on financial stability, but also the pivotal role that financial stability plays in ensuring a successful energy transition.

We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs.

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of high-frequency features such as time-varying volatility. Temporally aggregated models misspecify the evolution frequency of the volatility dynamics, resulting in poor volatility timing and worse portfolio performance than the mixed-frequency specification. These results highlight the importance of preserving the potential mixed-frequency nature of predictors and volatility in predictive regressions.

When firm-level information is not available, the greenness of financial portfolios, in terms of alignment to the EU Taxonomy, and their exposure to climate-related transition risk need to be estimated with a top-down approach. We improve the accuracy of available estimates by providing country-specific coefficients for both dimensions, based on homogeneous definitions of greenness and transition risk across countries. An application on confidential data from the European Central Bank shows that the exposure to transition risk of less regulated financial institutions has more than tripled from 2014 to 2023. Moreover, we show that the levels of Taxonomy alignment and transition risk exposure are largely heterogeneous across countries and sectors.

Purpose: In this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK. Design/methodology/approach: This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances. Findings: The authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags. Originality/value: To the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing.

We analyze how individual investors respond to inflation. We introduce a unique dataset containing information on local inflation and security portfolios of more than 2,000 clients of a German bank between 1920 and 1924, covering the German hyperinflation. We find that individual investors buy less (sell more) stocks when facing higher local inflation. This effect is more pronounced for less sophisticated investors. Moreover, we document a positive relation between local inflation and forgone returns following stock sales. Our findings are consistent with individual investors suffering from money illusion. Alternative explanations such as consumption needs are unlikely to drive our results.

We estimate the indirect costs of financial distress due to lost sales by exploiting real estate shocks and cross-supplier variation in real estate assets and leverage. We show that for the same client buying from different suppliers, the client’s purchases from distressed suppliers decline by an additional 13% following a drop in local real estate prices. The effect is more pronounced in more competitive industries, manufacturing, durable goods, less-specific goods, and when the costs of switching suppliers are low. Our results suggest that clients reduce their exposure to suppliers in financial distress.

We employ local projections to analyse the responses of Swiss asset prices to scheduled policy decisions of the European Central Bank (ECB) as a case study of ECB policy spillovers to European countries outside the euro area. Focusing on ECB policy shocks that are related to different policy instruments of the ECB, our empirical results leave the impression that surprises related to the ECB target policy rate and to the ECB’s longer-term forward guidance or its asset purchases tend to move Swiss interest rates and stock prices in the same direction. Shocks explicitly designed to capture pure ECB monetary policy and information effect shocks are weakly associated with movements in Swiss asset prices on average.

Using a novel sample covering 3783 US public firms from 2007 to 2020, we examine how negative media coverage of firm‐level environmental, social, and governance (ESG) practices affects a firm's debt choice. We find that firms with higher ESG reputation risk rely more on public bond than bank loan. The social and governance components, in particular, matter. Moreover, firms that receive more negative news coverage display a higher propensity to issue new bonds as opposed to securing new bank debt. Overall, our study presents empirical evidence on the relation between firm ESG reputation risk and debt financing.

Climate physical risk assessment is crucial to inform adaptation policies and finance. However, science-based and transparent solutions to assess climate physical risks are still missing. This is a main limitation to fill the adaptation gap. We provide a methodology that quantifies physical risks on geolocalized productive assets, considering their exposure to both chronic and acute impacts (hurricanes) across the scenarios of the Intergovernmental Panel on Climate Change. Then, we translate asset-level shocks into economic and financial losses. We illustrate the methodology in an application to Mexico, a country that is highly exposed to physical risks, and attracts adaptation finance and foreign investments. We find that investor losses are underestimated up to 70% when neglecting asset-level information, and up to 82% when neglecting acute risks. Therefore, neglecting the asset-level and acute dimensions of physical risks can lead to large errors in the identification of the relevant adaptation policy response, investments and finance tools aimed to build resilience to climate change.

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

The internal ratings-based (IRB) approach maps bank risk profiles more adequately than the standardized approach. After switching to IRB, banks’ risk-weighted asset (RWA) densities are thus expected to diverge, especially across countries with different supervisory strictness and risk levels. However, when examining 52 listed banks headquartered in 14 European countries that adopted the IRB approach, we observe a convergence of their RWA densities over time. We test whether this convergence can be entirely explained by differences in the size of the banks, loss levels, country risk, and/or time of IRB implementation. Our findings indicate that this is not the case. Whereas banks in high-risk countries with lax regulation, reduce their RWA densities, banks elsewhere increase theirs. Especially for banks in high-risk countries, RWA densities underestimate banks’ economic risk. Hence, the IRB approach enables regulatory arbitrage, whereby authorities only enforce strict supervision on capital requirements if they do not jeopardize bank viability.

This paper studies the contribution of analysts to the functioning and failure of the market for Initial Coin Offerings (ICOs). The assessments of freelancing analysts exhibit biases due to reciprocal interactions of analysts with ICO team members. Even favorably rated ICOs tend to fail raising some capital when a greater portion of their ratings reciprocate prior ratings. 90 days after listing on an exchange the market capitalization relative to the initial funds raised is smaller for tokens with more reciprocal ratings. These findings suggest that conflicts of interest help explain the failure of ICOs.

In the digital paradigm, collaborative value creation takes place across organisational boundaries while the consumer moves to the centre. Value will be created in the future through the combination of artificial intelligence, augmented reality, the internet of things, blockchain, cloud and quantum computing, and the customer journey will span different sectors. The golden triangle of digital ecosystems includes commerce, social media, and finance. Companies that understand the benefits of the platform economy can achieve exponential growth and become winners of the digital transformation. They can share knowledge and resources across organisational boundaries, react quickly to market changes and find the balance between existing businesses and new markets. The future of work and leadership in open and digital ecosystems requires rethinking and new dynamic capabilities such as openness, agility, and ambidexterity. The book is based on years of research. It expands traditional management models with pragmatic strategy and action concepts, which are explained with diverse practical examples of tech companies from the USA, cross-industry ecosystems from China and healthcare platforms from Switzerland.