Macroeconomic News Sentiment: Enhanced Risk Assessment for Sovereign Bonds

Abstract

We enhance the modelling and risk assessment of sovereign bond spreads by taking into account quantitative information gained from macro-economic news sentiment. We investigate sovereign bonds spreads of five European countries and improve the prediction of spread changes by incorporating news sentiment from relevant entities and macro-economic topics. In particular, we create daily news sentiment series from sentiment scores as well as positive and negative news volume and investigate their effects on yield spreads and spread volatility. We conduct a correlation and rolling correlation analysis between sovereign bond spreads and accumulated sentiment series and analyse changing correlation patterns over time. Market regimes are detected through correlation series and the impact of news sentiment on sovereign bonds in different market circumstances is investigated. We find best-suited external variables for forecasts in an ARIMAX model set-up. Error measures for forecasts of spread changes and volatility proxies are improved when sentiment is considered. These findings are then utilised to monitor sovereign bonds from European countries and detect changing risks through time.

Keywords

• sentiment analysis
• switching correlation
• sovereign bonds
• ARIMAX
• risk assessment