Seminars and conferences

This paper studies the role of narratives for macroeconomic fluctuations. Microfounding narratives as directed acyclic graphs, we show how exposure to different narratives can affect expectations in an otherwise-standard macroeconomic framework. We capture such competing narratives in news media reports on the US yield curve inversion in 2019, using techniques from natural language processing. Linking this to data from Twitter, we show that exposure to the narrative of an imminent recession is associated with a more pessimistic sentiment, while exposure to a more neutral narrative implies no such change in sentiment. In a model with frictions in financial intermediation, these effects of narrative-driven beliefs create a novel trade-off: extended periods of quantitative easing make narrative-driven waves of pessimism more frequent, but smaller in magnitude.

Based on a representative survey of the German population in 2021 with more than 7000 observations, we investigate how perceptions about public debt figures influence attitudes towards public finances and fiscal rules. On average, people strongly underestimate the public debt-to-GDP ratio, overestimate the debt-service ratio, favor a stricter version of the German debt brake and prefer spending cuts to finance public investment or to close budget gaps. Higher education or financial literacy comes with substantially lower estimation errors, better knowledge about the debt brake plus a more lenient view on it, and a stronger preference for financing public investment or budget gaps with credit. In an information treatment experiment, people consider public debt to be a more (less) severe problem, once they learn the actual public debt figures to be higher (lower) than their estimates. However, these treatment effects vanish when participants’ beliefs are anchored with past information on public debt figures. Moreover, we find no treatment effect on attitudes towards the debt brake or financing options for public investment and budget gaps. We rationalize this behavior by psychological surprise effects and conclude that better information on public debt figures would foster a more rational debate about public finances.

This paper studies monetary policy transmission mechanisms during QE. Using high frequency yield curve event studies of monetary policy announcements in combination with a dynamic term structure model, we can identify four types of monetary policy surprises: action (working through effective policy rates), signalling (working through expected policy rates), policy uncertainty and QE-specific bond supply (both working through term premia). Applying the method to the case of the UK, we find that these channels have often operated together. Importantly, the main QE channels are transmitted to financial markets and the real economy in different ways, and only signalling is found to have ultimately affected inflation significantly.

Financial cycles are assumed to reflect the dynamics and interconnectedness between the credit, housing and stock markets, which are all important components of the overall financial stability. Estimates and accurate financial cycle forecasts could be useful for sound macro-prudential policy making and investment planning. In this study, we estimate the financial cycle for Slovakia and use machine learning techniques to predict 3- and 6-month-ahead levels of the financial cycle.  The prediction accuracy is compared across multiple models and driven by a set of 170 potential predictors, including indicators related to banks, financial market, monetary policy, labor market, economic activity, business and consumer confidence and calendar effects.

This paper evaluates the impact of an online game-based financial education tool on students’ financial literacy levels. Based on a Randomized Controlled Trial of 2587 students across four countries, our findings show that the intervention significantly increased students’ financial literacy levels. As the implementation cost of the program is low, it has a favourable cost-benefit ratio. This study contributes to the nascent academic literature about evaluations of learning-by-playing financial education interventions. The involvement of participants from four countries provides relevance in terms of cross-comparison of the results, stimulating the discussions about specific-country peculiarities in terms of the financial literacy of youth.

We provide evidence of the usefulness of alternative data-sources in nowcasting Ukrainian GDP during the early stages of the full-scale 2022 Russian military invasion. This exercise is motivated by the lack of official statistic releases for several months at a time when policymaking critically needed to evaluate the speed and depth of the contraction. The nature of the shock, of varying regional and temporal intensity, poses particular challenges for established macroeconometric nowcasting and forecasting exercises. Our preliminary results show that even with minimal modelling, remotely sensed data on nightlight intensity combined with internet search trends from Google Trends and social media activity from Twitter offer a very good in-sample fit of official GDP data and intuitive out-of-sample results after the invasion both at national and regional levels. We show a very dramatic contraction of over 30% in March at national level (driven by very large contractions in regions directly affected by violence) and a rebound in April and May (driven by lifting the siege of Kyiv). Our results also turn out to be a close match to preliminary national statistics released recently by official sources.

Macroprudential policy implementation in the mortgage market has generally involved a policy tightening, as policies have been introduced in settings where no such policies previously existed. In this paper we produce rare evidence on an episode of loosening under the macroprudential regime for mortgages. We exploit a reform of the Irish borrower-based measures in 2017 that increased LTV limits for a cohort of First Time Buyers. We show in response to the reform that borrowers bunched at the new maximum LTV of 90, increasing LTVs relative to the counterfactual. We highlight an adjustment mechanism that has important policy implications: we find no evidence that treated borrowers purchased more expensive properties; rather, we find that treated borrowers post lower downpayments after the reform, displaying a preference for cash retention once the opportunity arises. While economic intuition leads one to expect house price amplification after a policy-induced credit loosening, we show that borrowers’ choices to rebalance towards greater cash retention dampened this channel in the Irish case in 2017.

Participants to an online study in Luxembourg are presented fake real-estate advertisements and tasked to make an offer to the shown properties. A random subset is also shown sellers’ names that are strongly framed to signal their origins. Our randomised procedure allows us to conclude that, keeping everything else constant, sellers with African-sounding surnames are systematically offered lower prices. Our most conservative estimates suggest that the average racial penalty stemming from the demand-side of the housing market is equal to 22,000 euros. Last, we show that this penalty hides important differences across respondents: it is null for the youngest and most educated ones, as well as for those without some personal ties to the African diaspora, but can amount up to around 65,000 euros for those above 40 years of age and without post-secondary education.

Inflation in most western advanced economies has been rising at a fast pace since the middle of 2021. The necessary condition for central banks to maintain price stability is to prevent temporary shocks to inflation from feeding into the mechanisms of wage and price formation (Bernanke 2007, Draghi 2014). The monetary theory prescription to avoid these self-fulfilling inflation expectations and prevent long-term inflation expectations to de-anchor is to follow Taylor principle, according to which the nominal interest rate should rise more than proportionally with inflation. We show that once the inflation is high, the distribution of prices across products widens and inflation uncertainty rises, the Taylor principle is no longer sufficient for inflation stability. The anchoring of inflation expectations instead calls for strict inflation targeting and abstaining from virtually any aim to stabilize the real side of the economy.

The Covid-19 pandemic caused a sharp decline in corporate sector activity in 2020 as a series of epidemiological measures limited or even prohibited the business activity of corporates, most notably in services industries. However, the joint policy response (often including fiscal, monetary, and bank supervisory measures) contained the shock caused by a relative increase in fixed costs allowing the corporate sector to remain liquid and solvent and therefore preventing the cascade of bankruptcies but rising concerns regarding the potential zombification at the same time. After lifting the majority of the epidemiological measures, new challenges emerged for the corporate sector across the EU. Input prices increase resulting from supply chain disruptions were additionally exacerbated by the geopolitical shocks. As variable costs increased, largelly reflecting the developments in energy and material prices and availability, the interest in the corporate sector’s resilience and its ability to pass the price increase to buyers intensified.  

Given the challenges the corporate sector went through during the last three years but also its importance for the financial sector performance and economic growth, a more detailed analysis of the corporate sector is required for Financial Stability Monitoring. Apart from acknowledging that operating level of corporate activity is as important for the Financial Stability as the financial one, this also requires strengthening the current corporate sector analysis capacity, mainly by corporate finance and micro econometric techniques application. 

While there exists a rich empirical literature on the causes and consequences of home ownership (typically looking at the household main residence), there is very limited evidence on the multiple-property owners. At the same time, it is argued that (speculative) real estate investments contribute significantly to the credit cycle and market overheating. Using the most recent EU-level microdata from the 3^rd wave of Household Finance and Consumption Survey (collected at around 2017/2018) we analyze the socio-economic determinants driving the household (speculative) real estate investment appetite. Additionally, we investigate the impact of macroprudential policy tightening experienced during the period of 2014-2018 on the household real estate investments. Results on determinants of household multiple-property ownership are conventional: richer, better educated households are more likely to hold several properties. Interestingly, tightening of the borrowed based measures (BBMs) significantly decreases the propensity to engage in (speculative) real estate investments. This result is robust to possible endogeneity issue when tested by conventional 2SLS IV framework and employed few of the recently suggested instrument(s) in the financial stability literature. However, we also report a substantial heterogeneity in effects of macroprudential tightening conditional on various household characteristics. Our results are among the first to inform policy about the effectiveness of the BBMs on the multiple-property ownership in a cross-country setup.

Financial markets can support the transition to a low-carbon economy by redirecting funds from highly emissive to clean investments.  We study whether European stock markets incorporate carbon prices in company valuations and to what degree they discriminate between firms with different carbon intensities. Using a novel dataset of stock prices and carbon intensities of 338 European publicly traded companies between 2013 and 2021, we find a strongly statistically significant relationship between weekly carbon price changes and stock returns. Crucially, this relationship depends on firms’ carbon intensity: the higher the carbon costs a firm faces, the poorer its stock performance during the periods of carbon price increases. Emissions that firms cover with free allowances however do not impact this relationship, illustrating how both carbon pricing and disclosures are needed for financial markets to foster climate change mitigation. The uncovered relationship can provide an incentive for firms to decarbonize. We argue that more ambitious carbon pricing policies should be implemented in the EU, as this would strengthen the stock-market incentive channel while causing only limited financial stability risk for stocks.

Growth at risk modelling has been a cornerstone for research and policymaking recently as a way to model tail risk in the macroeconomy. However, the majority of the research has almost exclusively focused on US data using financial stress indices to capture vulnerability. Nevertheless, recent events have highlighted that financial risk is just one source of vulnerability in growth. Geopolitical events have emerged as an important alternative measure. Importantly such events impact all assets, asset classes, sectors and countries: high geopolitical risk results in a decline in real activity, plummeting stock returns, and in capital flows transferred from emerging economies towards advanced economies. It is not surprising that geopolitical risk indices are becoming attractive peers/ competitors of financial stress indices. The aim of the paper is to complete the growth at risk model for Hungary by using the global geopolitical risk index (GPRI) of Caldara and Iacoviello. When incorporating different types of risk indices it is important to only include ones that are important to our data generating process. However, these indices are highly correlated making it difficult ex-ante to select the “perfect” measure. To tackle this problem a variable selection framework is applied to identify which variables are key determinants in different parts of the Hungarian GDP distribution. Importantly this paper develops a methodology that can handle variable selection task in small sample settings, which is usually the case in emerging markets. Using the methodology the authors are able to show that the global geopolitical risk index (GPRI) has recently become a key element in expressing macroeconomic vulnerability in Hungary.

We revisit the finite-sample behavior of just-identified instrumental variables (IV) estimators, arguing that in most microeconometric applications, just-identified IV bias is negligible and the usual inference strategies likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form t1>c, where t1 is the first-stage t-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting on the first-stage F-statistic exacerbates bias and distorts inference. We show, however, that median bias is both minimized and roughly halved by setting c=0, that is by screening on the sign of the estimated first stage. This bias reduction is a free lunch: conventional confidence interval coverage is unchanged by screening on the estimated first-stage sign. To the extent that IV analysts sign-screen already, these results strengthen the case for a sanguine view of the finite-sample behavior of just-ID IV.

The ongoing trend in global warming needs prompt and timely policy reaction. This reaction and the consequent transformation to carbon-neutral economy results in worldwide economic and financial losses to business, households, and governments. Beside the impact on the real economy, central banks and supervisors follow the impact on the financial system as well. This paper studies the potential impact of climate change risks on banks in Slovakia over the horizon of four-years. We focus mainly on transition risks, i.e. risks posed by the transition to carbon-neutral economy. We propose an analytical framework to quantify impact of transformation policies on bank’s credit risk exposures based on scenarios prepared by Network for Greening the Financial System (NGFS). We document negative impact along credit risk channel in the form of increased non-performing loans losses from both households and non-financial corporations loan portfolio. The main drivers of credit risk are the shock to GDP in case of corporates and the increase in unemployment rate in case of households. Although losses are significantly lower compared to adverse stress testing scenarios, they are sensitive to energy price increase.

In U.S. data, inflation and output are negatively related in the long run. A Bayesian VAR with stochastic trends generalized to be piecewise linear provides robust reduced-form evidence in favor of a threshold level of trend inf;ation below which potential output is independent of trend inflation, and above which, instead, potential output is negatively affected by trend inflation. The threshold level of inflation is slightly lower than 4%, above which every percentage point increase in inflation is related to about 1% decrease in potential output per year. A New Keynesian model generalized to admit time-varying trend inflation and estimated via particle filtering provides theoretical foundations to this reduced-form evidence. The structural long-run Phillips Curve implied by the estimated New Keynesian model is not statistically different from the one implied by the reduced-form piecewise linear BVAR model.

The explosion in ESG research has led to a strong reliance on ESG rating providers. We document widespread changes to the historical ratings of a key rating provider, Refinitiv ESG (formerly ASSET4). Depending on whether the original or rewritten data are used, ESG-based classifications of firms into ESG quantiles and tests that relate ESG scores to returns change. While there is a positive link between ESG scores and firms’ stock market performance in the rewritten data, we fail to observe such a relationship in the initial data. The ESG data rewriting is an ongoing rather than a one-off phenomenon.

In this paper, we first describe which countries have implemented the counter cyclical buffer (CCyB) regime under Basel III. In a second step, we review the hypothetical performance of the Basel CCyB guidance before the great financial crisis in 2007/08. In a last step, we analyze the CCyB decision process of all countries worldwide that have implemented the CCyB under Basel III and report their aggregate credit data to the Bank for International Settlements (BIS). In contrast to opinions expressed in some of the recent literature, the hypothetical CCyB under the Basel guidance would have worked really well in many (European) countries, leading to approximately 250-300 Bn EUR additional capital requirements that could have been released during the crisis. Nevertheless, since the legal implementation of the CCyB framework in 2015, countries do not take the Basel guidance into account when setting their CCyB. We even estimate a negative loading of the suggested Basel CCyB on the CCyB decision.

We show how the Joslin, Singleton, and Zhu (2011) factor extraction approach to estimating the Gaussian term structure model can be modified to handle the interest rate lower bound. Our novel approach is exact in a sense that it does not require any approximation in the estimation process. This is important from the perspective of using the model for economic inference, such as measuring term premium or policy expectation. Compared with the standard shadow rate estimation approach based on the Kalman filter, this improves convergence and greatly reduces the computation time. It has the added advantage of producing more robust estimates of the lower bound parameter and the path of the shadow rate. We show that expected inflation and real activity are important unspanned macro factors that drive term premiums as in Joslin, Priebsch, and Singleton (2014).

How do estimates of firm-level markups that rely on production function estimations depend on common data limitations? With a tractable analytical framework, simulation from a quantitative model, and firm-level administrative production and pricing data, we study biases due to the use of revenue instead of quantity, and due to production function misspecification. Estimates from revenue mismeasure the level of markups, but do contain useful information about true markups. Conversely, misspecified production functions have little effect on the estimated average markup but reduce their information content. Finally, revenue and quantity markups display similar correlations with variables such as profitability and market share in our data.

In many countries, market power in goods markets has increased over the last decades. To examine the implications for monetary policy, we present a menu-cost model with endogenous markups. This model rationalizes the observed increases in market power via productivity increases for some firms. Aggregate productivity is procyclical as resources are reallocated across firms over the course of the business cycle. We identify a new amplification mechanism that relies on a direct effect of market concentration on the demand for individual goods. This mechanism strengthens endogenous fluctuations of aggregate productivity as well as monetary non-neutrality and thus helps to understand the flattening of the Phillips curve.