<abstract><p>Central banks communication has lately become an important tool to guide expectations and its impact on the economy has been acknowledged by the literature. Nowadays central banks speeches face an increasing variety of topics, which are not discriminated by text analysis. In this paper we build a topic-weighted central bank sentiment index as a combination of machine learning and text analysis techniques to investigate large datasets. First, we develop a methodological framework to grid search the best Latent Dirichlet Allocation (LDA) model to uncover the latent topics in central banks' speeches and releases published between 2000 and 2021. Then, we build a topic-specific sentiment index based on dictionary techniques. Next, we summarise the results in a topic-weighted Central Bank Sentiment Index (CBSIw) for the Bank of Canada (BoC), the Bank of England (BoE), the European Central Bank (ECB) and the Federal Reserve (Fed). We find that the main common driver of the CBSIw is the monetary policy topic, followed by macroprudential policy and payments and settlements. We also uncover bank-specific topics and topics related to new challenges, for example innovation and climate change. Moreover, we find that the CBSIw decreases after the Great Recession, signalling a worsening in sentiment, as well as during the COVID-19 crisis. Finally, we employ a probit regression to further assess the predictive power of our monetary policy topic-specific index. We find that the indicator helps predicting future changes in policy rate, corroborating the evidence that central banks communication signals future monetary policy decisions.</p></abstract>
We forecast monthly value at risk (VaR) and conditional value at risk (CVaR) using option market data and four different econometric techniques. Independent from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever-changing market scenario. All proposed option-based approaches outperform or are equally good to different "traditional" forecasts that use historical returns as input. The extensive robustness of our results shows that the real driver of the better forecasts is the use of option market data as inputs for the analysis, more than the type of econometric approach implemented.
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