Climate risk assessment of the sovereign bond portfolio of European Insurers

In the first collaboration between climate economists, climate financial risk modellers and financial regulators, we apply the CLIMAFIN framework described in Battiston at al. (2019) to provide a forward-looking climate transition risk assessment of the sovereign bonds’ portfolios of solo insurance companies in Europe. We consider a scenario of a disorderly introduction of climate policies that cannot be fully anticipated and priced in by investors. First, we analyse the shock on the market share and profitability of carbon-intensive and low-carbon activities under climate transition risk scenarios. Second, we define the climate risk management strategy under uncertainty for a risk averse investor that aims to minimise her largest losses. Third, we price the climate policies scenarios in the probability of default of the individual sovereign bonds and in the bonds’ climate spread. Finally, we estimate the largest gains/losses on the insurance companies’ portfolios conditioned to the climate scenarios. We find that the potential impact of a disorderly transition to low-carbon economy on insurers portfolios of sovereign bonds is moderate in terms of its magnitude. However, it is non-negligible in several scenarios. Thus, it should be regularly monitored and assessed given the importance of sovereign bonds in insurers’ investment portfolios

Enhanced news sentiment analysis using deep learning methods

We explore the predictive power of historical news sentiments based on financial market performance to forecast financial news sentiments. We define news sentiments based on stock price returns averaged over one minute right after a news article has been released. If the stock price exhibits positive (negative) return, we classify the news article released just prior to the observed stock return as positive (negative). We use Wikipedia and Gigaword five corpus articles from 2014 and we apply the global vectors for word representation method to this corpus to create word vectors to use as inputs into the deep learning TensorFlow network. We analyze high-frequency (intraday) Thompson Reuters News Archive as well as the high-frequency price tick history of the Dow Jones Industrial Average (DJIA 30) Index individual stocks for the period between 1/1/2003 and 12/30/2013. We apply a combination of deep learning methodologies of recurrent neural network with long short-term memory units to train the Thompson Reuters News Archive Data from 2003 to 2012, and we test the forecasting power of our method on 2013 News Archive data. We find that the forecasting accuracy of our methodology improves when we switch from random selection of positive and negative news to selecting the news with highest positive scores as positive news and news with highest negative scores as negative news to create our training data set.Published versio