Marcel Fratzscher Tobias Heidland Lukas Menkhoff Lucio Sarno Maik Schmeling: 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.
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.
Information from news reports
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.
Benchmarking with actual intervention data...
more at CEPR
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