IFRS 17 will require stochastic modelling of financial options and guarantees (such as a guaranteed maturity value), which might not be a common practice in certain territories, as discussed in ‘Example – Stochastic and deterministic modelling‘. Options and guarantees should be recognised and measured on a current, market consistent basis. All cash flows, including fixed, guaranteed and cash flows variable with underlying items, should be measured on a probability-weighted basis using market variables, where relevant, and considering all possible scenarios. The measurement of options and guarantees will, in many cases, involve stochastic modelling or using a deterministic model, run multiple times, to reflect a range of scenarios because of the non-symmetric distribution of outcomes for those features. A single deterministic approach might, for example, omit valuing the scenarios where the expected investment return is less than a guaranteed return. For certain simple options and guarantees, a formula (such as ‘Black Scholes’) might exist which could be equivalent to stochastic modelling.
The most common methods for measuring financial options and guarantees on a market consistent, stochastic basis are the ‘risk neutral’ and ‘real world/deflator’ methods. In these methods, the financial options and guarantees are measured consistently with the cost of hedging the obligation (where observable) at the balance sheet date. This is achieved through the modelling of the interactions between cash flows that vary with underlying items and the discount rate for the contract as a whole. There are alternative ‘real world’ stochastic methods, used today in certain territories, where some asset classes (such as equity instruments and real estate) are assumed, based on historical market averages, to outperform fixed income asset classes. These ‘real world’ methods are not permitted under IFRS 17, because financial options and guarantees would not then be measured consistently with observable current market prices.
Non-market variables include all variables that cannot be observed, or derived directly, from the market. For insurers, non-market estimates and assumptions can include information about amounts, timing and uncertainty of incurred and future claims, lapse rates, mortality and morbidity rates, and expectations about how the insurer will exercise discretion in the future.
Entities can use both internal and external sources of non-market variables. Judgement is required to identify the most relevant information where both internal and external information is available. For example, mortality information is usually available both internally (from an entity’s accumulated data about mortality experience) and externally (such as mortality statistics of the country where the entity operates). Mortality statistics of a country might be irrelevant if an entity issues policies only in one region of the country. On the other hand, if such a company decides to expand its business from a single region to the whole country, its internally accumulated mortality experience might be irrelevant for the new portfolio, and country statistics or other external sources of information might be more relevant.
In some cases, non-market variables might correlate with market variables. For example, for a participating contract with an embedded guarantee of minimum returns, the lapse rate might correlate with market interest rates. That is, the probability of lapse decreases with a decrease in market interest rates. In such cases, entities should ensure in relevant scenarios that probabilities associated with non-market variables are consistent with observable market information.
Market variables are often associated with financial risk, and non-market variables with non-financial risks, but this will not always be the case. For example, debt and equity instrument prices and interest rates always represent financial risk but they are not always observable in the market. Non-market variables should be as consistent as possible with available market information.