Central banks these days are actively looking to take the lead in the propagation of knowledge, risk metrics, and recommendations on climate risk variability impacts on the wider financial industry—commercial banks, mortgage lenders, credit firms, brokers, insurers, and reinsurers. From April to July at least three events were organized by central bankers in New York City and Paris to deliver this message to practitioners and academics.
I was fortunate to be invited by the Institut Louis Bachellier to attend these events. The Institut itself is named after the French mathematician who wrote his doctoral thesis in 1900 on the application of a random variable process to the modeling of stock returns on the Paris Bourse, and further showed that these returns fit into a log/normal distribution. Bachellier thus pioneered the modern sciences of financial and actuarial modeling.
At the time, however, Bachellier was spurned by the Paris scientific elite and could not even get a teaching job at the Sorbonne, so he left Paris to teach in the provinces. Financial modeling was not considered a sufficiently respectable occupation! Actuaries picked up on his work in the 1920s and started applying it to the modeling of historical fire claims in London, Copenhagen, and other big cities and to geo-clusters of insurance risk.
Today Banque de France and the Bank of England in particular position themselves as thought and action leaders in the drive to measure, manage, and remediate climate risk in the financial and insurance industries. As the governor of the Bank of England put it quite explicitly, “we cannot demand, but we can help, we can advise, and we can recommend.” With respect to our business space, the central banks propose that the (re)insurers are exposed to climate risk both on the asset and liability sides. These risks are classified as three main types—physical, transitional, and inherent liability.
- Physical risk results from direct damage to insured assets by weather and climatic events
- Transitional risk results from the general move of insured actors to a low carbon economy, particularly when such moves are poorly anticipated and managed by the insurers
- Inherent liability and reputation risk may result from the failure to account for investments in industries with high carbon footprints, or to account for and recognize claims settlements that directly result from acute concentrations of climatic risk
On the asset side, insurers should favor an assessment of the carbon footprint of their investments. On the liability side the (re)insurer risk profiles are subject not only to the physical risk of increased severity and frequency of catastrophe events but also to increased probability of mortality and tropical diseases. In this landscape of climate-related risks, the recommendations of the central banks to insurers are defined as desirable, multifold, and long-term action plans. They are:
- Improve the micro granularity of supervision and reporting of climate risk by liability class, asset class, and geographic region
- Integrate climate risk factors into your portfolio management practices by accounting for the impact on assets and liabilities—i.e., integrate various scenarios of temperature [1.5°C to 4°C] and sea-level variability into re/insurance contracts in your business case
- Reflect climate risk in (re)insurance premium pricing—the central banks’ current view is that climate risk is not yet properly accounted for
The difficulties in converting these recommendations into action are self-evident. While insurers have long-time experience modeling the impacts of climatic and catastrophic scenarios on their portfolios, a forecast outcome of temperature increase and sea-level rise poses a new and very different proposition, for which it will take time to come to an established consensus methodology.
In addition, the market acceptance of climate risk premium depends not only on the establishment of such consensus methodology but also on the support of many market factors, such as (re)insurance coverage demand and supply, which vary cyclically and over time, and are generally not entirely predictable.
