Second, we broaden the discussion by analyzing a non-eurozone case from the Central-Eastern region of the European Union. First, we apply a quantitative text analysis framework which utilizes a dictionary tailored to the domain of monetary policy (as opposed to working with general purpose, or even financial, keywords). We offer a three-fold contribution to the literature. These results point to the potential importance of context and the varying effectiveness of very similar signaling techniques across domains and time as-at least in some research for Western European countries-forward guidance has a more significant impact further down the yield curve. Longer term yields are only cointegrated-i.e.: have a long-run equilibrium-with the policy rate. Our results show that central bank talk is not cheap when it comes to sovereign debt in Hungary-but only in regard to intra-year bond yields. We estimate the effects of these documents on bond yields by analyzing the common stochastic trends between the sentiment of MC press releases, interest rate decisions and bond yields using autoregressive-distributed lag models. In order to capture the effect of central bank communication, we measure the latent hawkish or dovish sentiment of MC press releases from 2005 to 2019 by applying a sentiment dictionary, a staple in the text mining toolkit. While prior research demonstrated that forward guidance, such as council meeting minutes can be used as predictors of rate decisions, we are interested in whether they are able to directly influence asset returns as well. This research ties into the advances made in the financial and political economy literature which rely on extensive textual data and quantitative text analysis tools. In this article we fill this gap in the literature by investigating how the public communication of the Hungarian Central Bank’s Monetary Council (MC) affects Hungarian sovereign bond yields. The CEE cases offer a new perspective as these are small, open economies which are more vulnerable to external shocks and, therefore, may highlight a different dynamic vis-á-vis bigger and less globalized countries. The closest examples of using textual variables are a few studies on the effect of macroeconomic announcements on composite stock returns or on the effect of central bank communication on media narratives. Furthermore, there is some evidence for verbal comments by monetary policymakers to have an influence on exchange rates. In their review, Brzeszczynski, Gajdka and Kutan find that the scarce literature on the CEE region shows that asset prices are vulnerable to external spillover effects from the US monetary policy. However, these text mining techniques are rarely used in a monetary policy context when it comes to Central and Eastern Europe (CEE). These may or may not contain a quantitative interest rate prognosis, therefore the intentions of policy-makers have to be extracted from these sources using quantitative text analysis techniques. The key instruments for forward guidance are interviews, press releases and policy council minutes. In more recent studies the focus has been extended to central bank communication as it carries a ‘forward guidance’ for market participants. Treasury bond market to macroeconomic news. In one example, Altavilla, Giannone, and Modugno analyzed the reaction of the U.S. Sovereign bond yields were also scrutinized in similar research designs. These studies were rooted in the text as data paradigm which allows for the quantitative analysis of qualitative data, such as textual sources. More recently, the role of macroeconomic news (both as a dependent and explanatory variable) gained prominence. The key metrics, such as stock returns and bond yields, were traditionally explained by financial asset pricing models and macroeconomic fundamentals ). The volatility of asset prices, spreads and returns is a premier field of study in monetary and financial economics.
0 kommentar(er)
