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Lack of publicly available data has long hindered empirical analysis of central bank intervention in foreign exchange markets. Hence, researchers need proxies for intervention activity. This column argues that changes in foreign exchange reserves, a popular proxy in the literature, are a crude measure of interventions because reserves change for many reasons unrelated to interventions. Instead, the authors propose an approach based on applying textual analysis to news reports. This ‘news proxy’ of official intervention has a much lower probability of false alarms and hence a lower noise-to-signal than existing proxies when benchmarked against actual interventions.
Foreign exchange (FX) intervention involves the purchase and/or sale of foreign currency by the central bank. It is a long-standing policy instrument designed to impact exchange rates and foster stable currency markets. Many emerging economies use it frequently, indicating its (perceived) usefulness (e.g. Gelos et al. 2020). While most central banks in advanced economies use this instrument more sporadically, Switzerland and Israel have intervened extensively since the global financial crisis (Cukierman 2018), and the Bank of Japan (BoJ) has been the most active major monetary authority in the currency markets in recent decades. Indeed, around 22 September 2022, the BoJ sold a hefty US$20 billion to buy and support a sharply weakening Japanese yen. At a time reminiscent of the strong US dollar of the 1980s that led to some of the biggest FX intervention actions coordinated by central banks around the world, the major central banks seem likely to trade in currency markets to support their currencies, independently or in a coordinated fashion. The IMF (2022) has recently fine-tuned its institutional view on the management of capital flows, seeing stabilisation potential not only in times of crisis but also for prevention (Korinek et al. 2022). Thus, FX interventions are not an urchin of capital account management but have been welcomed back into the family of tools policymakers can use.
In contrast to its widespread use by many central banks, hard evidence on the impact of FX interventions remains limited. The main reason is simply the paucity of reliable data because, unlike monetary policy, there is no consistent data source. In recent research (Fratzscher et al. 2022), we contribute to filling this gap by providing a new dataset on FX interventions for 49 countries over up to 22 years. These data are publicly available and come with some advantages over alternatives that we discuss below. Let us start by discussing briefly how these FX intervention data are generated.
The basic idea is to retrieve information about FX interventions from publicly available news reports to overcome the lack of official data. This procedure was pioneered by Klein (1996) and a few papers have used it, with the limitation that all of them are case studies. Nowadays vast news archives can be searched and analysed with the help of textual analysis. The main constraint is pre-selecting, reading, and classifying the large number of intervention-related news. For this, we develop a text classification algorithm that provides information on the incidence of FX interventions for a large number of countries and over long timeframes. The first step was to train the news classification algorithm so it is capable of correctly classifying news items that cover FX interventions. The news items were retrieved from Factiva. Then, several thousand news items were hand-coded with a double-entry technique to build a training dataset.
This trained algorithm is quite reliable in correctly identifying which news cover intervention. At the monthly frequency, hand-coded news and algorithmically-coded news agree in 99% of cases. Then the trained algorithm is used to classify more currencies and extend the data period from 2011 to 2016 for which news items were not hand-coded. The result is a dataset that indicates every month whether there was an FX intervention or not.
more at CEPR
