Global uncertainty has increased, and we are all aware of its triggers. Trade conflicts and geopolitics are compounded by the challenges arising from climate change, which we are already beginning to experience. In the financial sphere, wholesale markets become a vulnerability factor given the persistence of exceptionally loose financial conditions for longer than anticipated. This scenario may lead companies to delay their investment projects and consumers to postpone their spending and increase savings. <\/p>\n
For economic policymakers, it is crucial to understand both the origin of uncertainty and the dynamics of its development. Accurate diagnosis is fundamental to devising a treatment that successfully addresses the root of the problem and limits its potential negative consequences. This explains the calls from international organizations to strengthen alliances and multilateralism, demanding a combination of appropriate policies where supply-side measures and fiscal policy reduce the prominence of current monetary policy. <\/p>\n
Central banks are aware of the risks they assume by maintaining exceptionally loose financial conditions over time, with the expectation of extending them in the future if necessary. The threats to the economy and the reasons for inflation below the desirable medium-term level do not correspond to problems in financing supply or inadequate financial conditions. Once this reality is recognized, everything suggests that the objective of central banks in designing this unprecedented monetary policy is to mitigate the potential financial instability derived from the aforementioned uncertainties, while other measures are taken to alleviate their negative consequences or, simply, it is hoped that they will not be so severe. <\/p>\n
The latest monthly report from the European Central Bank (ECB) includes a brief note titled “Sources of uncertainty about economic policies in the euro area: a machine learning approach.” It presents an indicator of policy uncertainty levels derived from applying machine learning techniques to newspaper articles. The algorithm identifies the most representative words from different categories presented in over 171,107 articles between 2000 and 2019. In the case of monetary policy, and specifically in Spain, the most repeated words were: rates, ECB, monetary, inflation, Draghi, euro, interest, bank, and economy. These are not very different from those found in the rest of the analyzed countries. Monetary policy was a factor of uncertainty between 2016 and 2017, moving to the background thereafter as central banks began to shift their bias towards monetary normalization. The indicator now identifies trade, domestic regulation, and fiscal policy as factors of uncertainty. It is still too early to assess the impact that the new shift in central banks’ bias, including our ECB, may have before the summer, anticipating new expansionary measures. <\/p>\n
Analyses are once again emerging regarding the risks to financial stability, and especially to banking stability, derived from monetary policy. This is not about assessing potential weaknesses of credit institutions or the existence of excesses in their balance sheets. Banks are now more resilient and efficient, as part of the adaptation process to the digital transformation demanded by their clients. However, a monetary policy strategy like the current one entails uncertainties for banks that are not easy for institutions to manage. Just as families and businesses need certainty to set their spending and investment, banks also need greater certainty from exogenous factors to define their strategy. The success of banks’ strategy benefits us all. <\/p>\n