Giorgis Hadzilacos, Ryan Li, Paul Harrington, Shane Latchman, John Hillier, Richard Dixon, Charlie New, Alex Alabaster and Tanya Tsapko
The 2015/16 storms caused the most extreme flooding on record, with parts of the UK impacted by heavy precipitation and extreme wind over a four-month period. These extreme weather events occurred in quick succession, hindering relief efforts and accruing £1.3 billion in insured losses. Without adequate mitigation, such events may result in claims handling strain and capital risk for insurers. Recent research finds that above-average windstorm seasons are typically accompanied by above-average inland flooding. That raises a challenge for insurers: should they have adequate risk mitigation measures in place for periods that are both windy and wet? We argue that insurers need to reassess their model assumptions, especially as climate change might make wet years more frequent than in the past.
Fraser Drew, David Humphry, Michael Straughan and Eleanor Watson
For most of us buying insurance nowadays, price comparison websites offer plenty of choice. But how much competition in insurance markets is there? There are very few studies that address this question (see here for a summary), unlike for banking where there is a wide literature. We take an exploratory approach to address the question, applying benchmarks used in competition research to a unique set of reporting data across multiple UK insurance regulatory regimes, with the hope of stimulating further work. We find competition generally works well in UK life and non-life insurance markets, despite increases in life market concentration over the past 25 years. However, competition regulators have found practices in specific markets that harm consumers.
Data plays a central role in all technical aspects of insurance and actuarial work. However, utilisation is often still confined to aggregate premium and claims data. Not so in the case of telematics. Say the phrase ‘black box’ and most people will think of flight recorders fitted to aircraft. But Motor insurers also use the millions of data points generated by black boxes, fitted to more than a million cars in the UK, to price risks. What’s more Marine insurers are getting in on the act. In this post we take an actuarial vantage to explore the use of telematics data and consider whether insurers could be using this ‘gold mine’ of information even more widely.
Imagine you have just passed your driving test. After many hours of careful instruction, you are keen to put your good driving habits to the test on the open road. You phone up your insurance company but discover that your insurance premiums will cost you hundreds of pounds more than you can afford because “newly-qualified drivers are worse than average”. This post is about how developments in the car insurance market have the potential to revolutionise the way we drive and how we guard against the risks of bangs, scrapes and scratches. The increased use of telematics (also known as black boxes) has important implications for anyone who might consider driving, policymakers and for society as a whole.
Alex Ntelekos, Dimitris Papachristou and Juan Duan
The 2017 Atlantic hurricane season was the fifth most active in 168 years. It was also one of only six seasons to see multiple Cat 5 hurricanes (Irma & Maria). These two hurricanes, followed similar tracks and, together with Hurricane Harvey, occurred close together. This situation can hinder relief efforts. For insurers it may also lead to resource strain, disputes and unhedged risks, if insurers do not have enough ‘sideways’ reinsurance cover. Our post asks whether three major hurricanes occurring in the US in close succession really was exceptional or, as our analysis of recent data suggests, it might happen more often in future. Is the insurance industry underestimating the likely ‘clustering’ of major hurricanes?
Correlation matrices arise in many applications to model the dependence between variables. Where there is incomplete or missing information for the variables, this may lead to missing values in the correlation matrix itself, and the problem of how to complete the matrix. We show that some of these practical problems can be solved explicitly, via simple formulae, and we explain how to use mathematical tools to solve the more general problem where explicit solutions may not exist. “Simple” is, of course, a relative term, and the underlying matrix algebra and optimization necessarily makes this article more mathematically sophisticated than the typical Bank Underground post.
Risky asset prices plummeted following the collapse of Lehman Brothers in 2008. Whilst driven partly by deteriorations in fundamental news, these falls were amplified by ‘flighty’ investors that sold at the first signs of trouble. Conventional wisdom dictates that life insurers, with their long-term investment horizons, are better placed than most to ‘lean against the wind’ by looking through short-term fluctuations in asset prices. They could thereby stabilise prices when others are selling. But the structure of regulations can greatly influence insurers’ investment incentives. Using our model of insurers’ asset allocations, we find that new ‘Solvency II’ regulations reduce UK life insurers’ willingness to act as the white knights of financial markets, particularly in the face of falling interest rates.
A seismic shift is occurring in the European financial system. Since 2008, the aggregate size of bank balance sheets in the EU is essentially flat, while market-based financing has nearly doubled. This shift presents challenges for macroprudential policy, which has a mandate for the stability of the financial system as a whole, but is still focused mostly on banks. As such, macroprudential policymakers are focusing increasing attention on potential systemic risks beyond the banking sector. Drawing from a European Systemic Risk Board (ESRB) strategy paper which we helped write along with five others, we take a step back and set out a policy strategy to address risks to financial stability wherever they arise in the financial system.
David Elliott, Chris Jackson, Marek Raczko and Matt Roberts-Sklar.
Oil prices have fallen by more than 50% since mid-2014. For much of this period, financial market measures of both short-term and longer-term inflation expectations appear to have mirrored moves in oil prices, particularly in the US and euro area. But how strong is the relationship between oil prices and financial market inflation expectations, and what should we make of it?
Capital requirements for financial institutions are typically calculated using a statistical model and a risk measure such as VaR, whereas stress tests designed by regulators and risk managers are often based on subjective scenarios with no associated probability level. The stress test cannot therefore be easily linked to the capital measure. Taking insurance as an example, we show how to establish the link using intuitive tools which (i) respect the stress test designer’s intuition about causal direction, (ii) can be calibrated to pre-determined parameters such as correlations between risks, and (iii) can be easily communicated to and challenged by non-technical audiences.