Working paper series / Institute for Monetary and Financial Stability
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185
Trotz der von der EZB eingeleiteten Zinswende in der zweiten Jahreshälfte 2022 als späte Reaktion auf die deutlich unterschätzte Persistenz hoher Inflationsraten im Euroraum sind die Realzinsen sowohl in der Ex-post-Betrachtung als auch in der Ex-ante-Betrachtung keineswegs als restriktiv einzuschätzen. Die Banken haben allerdings recht rasch strengere Vergaberichtlinien beschlossen, und die Nachfrage im Wohnungsbau und bei den Hypothekarkrediten ist stark eingebrochen.
Die Autoren thematisieren die Bedeutung von Zahlungsstromeffekten bei Annuitätenkrediten und analysiert hier vor allem den sogenannten Front-Loading-Effekt. Danach führen höhere Nominalzinsen selbst bei vollständig antizipierten Inflationsraten und unveränderten Realzinsen zu starken finanziellen Zusatzbelastungen in den ersten Phasen der typischerweise langen Kreditlaufzeit. Derartige Liquiditätseffekte können die Zahlungsfähigkeit bzw. die Zahlungsbereitschaft der privaten Investoren empfindlich verringern. Dies gilt vor allem bei Darlehen in Form der Prozentannuität, da hier zusätzlich ein Laufzeitenverkürzungseffekt auftritt. Solche Darlehen sind in Deutschland recht populär.
Mit Blick auf die Zukunft sehen die Autoren auch eine reale Gefahr für den Bestand an Wohnungsbaukrediten, wenn es zu einer Refinanzierung des großen Bestands an billigen Wohnungsbaukrediten kommt, ein Risiko, das auch Auswirkungen auf die makroökonomische und finanzielle Stabilität hat.
188
In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries.
The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. They authors find that a neural network performs best out-of-sample. They use this algorithm to generally classify historical EMU data, and to determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Their findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. They show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.
194
Whatever it takes to understand a central banker : embedding their words using neural networks
(2023)
Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. In this paper, the author propose a novel language model that is able to capture subtleties of messages such as one of the most famous sentences in central bank communications when ECB President Mario Draghi stated that "within [its] mandate, the ECB is ready to do whatever it takes to preserve the euro".
The authors utilize a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantication from computational linguistics. This allows them to provide high-quality central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. Their findings indicate that these deviations in communication significantly impact monetary policy actions, substantially reducing the reaction towards inflation deviation in the US.
196
We investigate how unconventional monetary policy, via central banks’ purchases of corporate bonds, unfolds in credit-saturated markets. While this policy results in a loosening of credit market conditions as intended by policymakers, we report two unintended side effects. First, the policy impacts the allocation of credit among industries. Affected banks reallocate loans from investment-grade firms active on bond markets almost entirely to real estate asset managers. Other industries do not obtain more loans, particularly real estate developers and construction firms. We document an increase in real estate prices due to this policy, which fuels real estate overvaluation. Second, more loan write-offs arise from lending to these firms, and banks are not compensated for this risk by higher interest rates. We document a drop in bank profitability and, at the same time, a higher reliance on real estate collateral. Our findings suggest that central banks’ quantitative easing has substantial adverse effects in credit-saturated economies.
186
We analyze the repercussions of different kinds of uncertainty on cash demand, including uncertainty of the digital infrastructures, confidence crises of the financial system, natural disasters, political uncertainties, and inflationary crises. Based on a comprehensive literature survey, theoretical considerations and complemented by case studies, we derive a classification scheme how cash holdings typically evolve in each of these types of uncertainty by separating between demand for domestic and international cash as well as between transaction and store of value balances. Hereby, we focus on the stabilizing macroeconomic properties of cash and recommend guidelines for cash supply by central banks and the banking system. Finally, we exemplify our analysis with five case studies from the developing world, namely Venezuela, Zimbabwe, Afghanistan, Iraq, and Libya.
184
We assemble a data set of more than eight million German Twitter posts related to the war in Ukraine. Based on state-of-the-art methods of text analysis, we construct a daily index of uncertainty about the war as perceived by German Twitter. The approach also allows us to separate this index into uncertainty about sanctions against Russia, energy policy and other dimensions. We then estimate a VAR model with daily financial and macroeconomic data and identify an exogenous uncertainty shock. The increase in uncertainty has strong effects on financial markets and causes a significant decline in economic activity as well as an increase in expected inflation. We find the effects of uncertainty to be particularly strong in the first months of the war.
187
Climate change has become one of the most prominent concerns globally. In this paper, the authors study the transition risk of greenhouse gas emission reduction in structural environmental-macroeconomic DSGE models. First, they analyze the uncertainty in model prediction on the effect of unanticipated and pre-announced carbon price increases. Second, they conduct optimal model-robust policy in different settings. They find that reducing emissions by 40% causes 0.7% to 4% output loss with 2% on average. Pre-announcement of carbon prices affects the inflation dynamics significantly. The central bank should react slightly less to inflation and output growth during the transition risk. With optimal carbon price designs, it should react even less to inflation, and more to output growth.
178
Highly interconnected global supply chains make countries vulnerable to supply chain disruptions. The authors estimate the macroeconomic effects of global supply chain shocks for the euro area. Their empirical model combines business cycle variables with data from international container trade.
Using a novel identification scheme, they augment conventional sign restrictions on the impulse responses by narrative information about three episodes: the Tohoku earthquake in 2011, the Suez Canal obstruction in 2021, and the Shanghai backlog in 2022. They show that a global supply chain shock causes a drop in euro area real economic activity and a strong increase in consumer prices. Over a horizon of one year, the global supply chain shock explains about 30% of inflation dynamics. They also use regional data on supply chain pressure to isolate shocks originating in China.
Their results show that supply chain disruptions originating in China are an important driver for unexpected movements in industrial production, while disruptions originating outside China are an especially important driver for the dynamics of consumer prices.
190
The forward guidance trap
(2023)
This paper examines the policy experience of the Fed, ECB and BOJ during and after the Covid-19 pandemic and draws lessons for monetary policy strategy and ist communication. All three central banks provided appropriate accommodation during the pandemic but two failed to unwind this accommodation in a timely manner. The Fed and ECB guided real interest rates to inappropriately negative levels as the economy recovered from the pandemic, fueling high inflation. The policy error can be traced to decisions regarding forward guidance on policy rates that delayed lift-off while the two central banks continued to expand their balance sheets. The Fed and the ECB fell into the forward guidance trap. This could have been avoided if policy were guided by a forward- looking rule that properly adjusted the nominal interest rate with the evolution of the inflation outlook.
179
Output gap revisions can be large even after many years. Real-time reliability tests might therefore be sensitive to the choice of the final output gap vintage that the real-time estimates are compared to. This is the case for the Federal Reserve’s output gap. When accounting for revisions in response to the global financial crisis in the final output gap, the improvement in real-time reliability since the mid-1990s is much smaller than found by Edge and Rudd (Review of Economics and Statistics, 2016, 98(4), 785-791). The negative bias of real-time estimates from the 1980s has disappeared, but the size of revisions continues to be as large as the output gap itself.
The authors systematically analyse how the realtime reliability assessment is affected through varying the final output gap vintage. They find that the largest changes are caused by output gap revisions after recessions. Economists revise their models in response to such events, leading to economically important revisions not only for the most recent years, but reaching back up to two decades. This might improve the understanding of past business cycle dynamics, but decreases the reliability of real-time output gaps ex post.