Barrie & Hibbert Blog
Risk Management Round Table with Barrie & Hibbert Asia and Ernst & Young
Posted on 08-04-2009 by Carolynn Duthie | 0 comments
Introduction
In February 2009, Barrie & Hibbert Asia hosted two joint breakfast seminars with Ernst & Young. The aim was to provide an informal platform for CROs, CFOs and Chief Actuaries of the top life insurance firms to discuss the critical issues related to the use of stochastic models in the areas of risk management, capital management and asset allocation in these tumultuous times. Topics clearly at the forefront of top management thinking as demonstrated by the strong attendance at the events by senior staff, at both the Hong Kong and Singapore venues. While a lot of ground was covered in these events, these notes aim to summarise some of the more interesting and Barrie & Hibbert centric elements discussed in the Hong Kong event only.
Fitting for the location, Barrie & Hibbert founding partner John Hibbert, focussed the initial discussions on the appropriateness of using methodologies developed from more developed economies to other perhaps less well developed ones – a topic particularly relevant to a number of the Asia Pacific economies. Whilst it was acknowledged that the magnitude of some of the issues faced may be different, there was a general agreement with (and certainly no vocal objection against) the view that sound methods, grounded in economic theory, should be portable across regions. In some cases minor modifications may need to be made, but in general, that there should be no need for fundamental changes in approach.
Yield Curve Extrapolation Methodology
The recent example of Barrie & Hibbert’s new yield curve extrapolation methodology sparked the initial debate. Even in developed markets there will be the need to value liability cash flows beyond the term of the longest (suitable) market instrument, whether this is 15 years as in Hong Kong or just under 50 years in the United Kingdom. A few comments supported the Barrie & Hibbert approach as it improved stability of the longer forward rates from calibration to calibration (as supported by general economic theory) as well as removing the sensitivity of the longest rates to small changes or errors in the prices of the very longest market instruments (an issue with the method currently recommended by the CFO forum), which is typically where the market is less deep and liquid. In summary, while this is clearly a controversial topic, it’s hard to disagree (and no-one did) with the underlying principle that yield curves that are (unnecessarily) volatile at extrapolated maturities can be of no benefit to any of the key users of the financial information based on such yield curves: shareholders, analysts, regulators or management.
Ultra-Long Risk-Free Rates
The discussion turned to the estimation of ultra-long risk-free rates (an input to the above methodology) and whether these in particular, but also more generally whether all long-term assumptions, should be country or region specific. With the 120 year risk-free forward rate, there was no disagreement that it would be difficult and likely spurious to try and differentiate between the equilibrium risk-free rates over such a long time horizon, and hence that a global target is probably appropriate. In other cases, there will be arguments to the contrary, and country or region specific long-term targets will be more appropriate. A challenging area and like many aspects of long-term financial modelling, not one amenable to a one size fits all solution.
Global Market Movements
The ‘Insurer’s perfect storm’ followed with the scene being set with the global market movements over 2008 being placed in to their historic context. Even going back over 100 years, the 2008 global equity market falls are some of the most severe, both in magnitude, and the number of markets impacted. This has resulted in considerable strain being placed on market-based, risk-based capital adequacy levels and market consistent measures of profitability, which in turn has prompted discussions on market consistent reporting and Solvency II.
Model Performance & Risk Management
The year has also been a severe test for financial asset models and modellers . Though we remain in the midst of the financial tsunami, we can still start to assess how the models (and their calibrations) have performed in the face of such an extreme tail event as 2008. To set the scene, the following insightful anecdote was used, which brought an uncomfortable laugh, or knowing nod from the participants:
Barrie & Hibbert have made models (and calibrations) available to clients for quite a number of years now which would allow them to capture the key characteristics that have characterised the global equity market movements over the last 12 months: excess kurtosis or ‘fat downside tails’ in equity returns; and the correlation ‘spike’ effect between global equity markets, whereby the correlation between equity markets increased substantially, severely limiting the magnitude of the famed ‘diversification benefit’. So why then are almost none of Barrie & Hibbert’s clients using these models?
The answer must lie somewhere in the observation that by using models that can capture such extreme tail events as occurred in 2008, estimated capital will likely be higher, and quite possibly significantly higher than current levels.
Prior to 2008, it is easy to see the challenges a risk manager would have faced in asking their board for more funding for a model which demonstrates that the company requires significantly more capital to support an extreme scenario which does not feature in the historical record. Even more so when you consider that the competition may not be so capital disadvantaged.
As one of the participants pointed out however – it is only ‘one data point’. Or more precisely the case actually made was that they would be interested in seeing the error bounds on the average 10 day correlation figure of 87% between global equity markets (average between US, UK, Japan, Germany and France) in 2008 that was mentioned. A point well made, and clearly motivated by the need to be able to robustly justify to management any model or calibration changes that would have such a material impact on calculated capital. I think we all agree that while the answer is almost certainly not 87% (maybe it’s somewhere between 70% or 95%), we can be pretty sure that it doesn’t remain at a constant level (say 50%) in all scenarios, as is a common modelling assumption. So perhaps the extra data point called the financial tsunami will help companies make the decision to transition to using models that can better capture these more extreme dynamics. Then, if the conversation moves on to the topic of calibration rather than model choice, another step towards better risk management will have been taken.
Calibrating Models & Extreme Tails
Inevitably there was the question of how do you meaningfully calibrate models when the point of interest in the distribution falls in the extreme tails (i.e. 99.5% or 99.98%)? With different approaches, models and calibrations giving such a wide scope for interpretation, the question was raised whether regulators and policymakers need to be more precise about what their words actually mean? One alternative put forward was to pull the confidence level down to a point where there can be more confidence in modelling the distributions (say a 70% point rather than a 99.5%) and then apply some appropriate scaling factor. Whatever the approach though, it was noted that while models by design, are only simplified versions of reality, they still can and do add significant value, and good models can give the modeller a good sense of ‘new’ plausible future scenarios.
Conclusions
The topic was rounded off with the observation that whilst the industry will need to consider the practical challenges of applying market-based techniques, it is crucial to recognise that the benefits of a market risk-based approach to costs and capital are better risk measurement transparency and better risk management incentives. A corollary to this is that any revisions should not undermine the long-term benefits of the last five years’ improvements in market risk measurement and management.
Before handing the floor over to Ernst & Young, the first session was wrapped up with a brief look at future developments before finishing with some Q&A time.
Misplaced reliance on sophisticated maths?
Posted on 31-03-2009 by John Hibbert | 0 comments
The UK regulator’s Turner report and comment in the financial press (Financial Times “Maths and markets” editorial, March 21) succeed in highlighting the important role of financial models to the financial services industry. Lessons drawn in the context of the banking sector fiasco are likely to be applicable elsewhere so the issues raised are worthy of careful consideration. However, as a self-confessed geek, in the business of bridging the gap between clever theory and pragmatic application, I believe commentators fail to give sufficient weight to two major overarching challenges in the use of models.
The basic task of the modeller is two-fold. First, for a specific task, to select and implement a suitable mathematical model from today’s vast array of possibilities. It is true that these models can (and will) always be improved but, believe me, there is already plenty of choice. Second, to choose assumptions i.e. to ‘calibrate’ the model to some window of past data or some other source of information or expert opinion. At best models can give valuable management information and insight into risk exposures. At worst they can obscure reality and create a false sense of security. The potential for poor choices gives rise to what is known as ‘model risk’. In some situations these model risks may be ‘first order’ (i.e. they really matter) in decision making and so the modeller needs to be much more than a mathematician.
However, in my opinion, by far the most difficult (and potentially costly) challenges for modellers arise in their interactions with firm management, accountants and regulators. First, the more sophisticated the maths, the less likely are senior management to understand models and their sensitivities and vulnerabilities. Does the modeller simplify (and obscure) or build more complexity (and obscure)? It does suggest that firms who choose to hold complex exposures need strong technicians at a very senior level. Second, is the apparent lack of incentives for senior management to fully incorporate the available insights of the models into their business model and decision making. A strong governance framework can provide some protection from the risks which flow from choice around models and assumptions. Too often these choices have been left unchecked by the lack of deep knowledge and credulity of regulators and accountants. Meeting the challenges posed does not just require smarter maths but also a focus on governance, managerial technical competence and understanding model risk. Whilst all stakeholders would tell us these questions are on their respective agendas, we all now need to give real force to the words.