Business margin protection

ARF can be a very efficient hedging instrument when the business margin needs to be protected, namely in the following business scenarios:

• Budgeted sales revenue or budgeted costs of goods sold are incurred with reliable regularity and spread evenly in time. This exposure needs to be hedged against the functional currency.
• The business is run in separate books with different functional currencies, FX exposure is determined and hedged against the respective functional currency of these books. Resulting margin can be budgeted with high degree of reliability and stability, is relatively small and needs to be hedged from the currency of the respective business book to the functional currency of the reporting entity.

Increased complexity

Hedging such FX exposure with conventional FX forwards would lead to a very high number of transactions, as well as data on the side of underlying FX exposure determination, resulting in a data flood and high administrative effort. A hedge accounting according the IFRS 9 rules is almost impossible due to high number of hedge relationships to manage. The complexity increases even more if treasury operations are centralized and the FX exposure has to be concentrated via intercompany FX transactions in the group treasury first.

If the ARF instruments are not directly supported by the used treasury management system (TMS), the users have to resort to replicating the single external ARF deal with a series of conventional FX forwards, creating individual FX forwards for each fixation date of the observation period. As the observation periods are usually long (at least 30 days) and rate fixation periodicity is usually daily, this workaround leads to a high count of fictitious deals with relatively small nominal, leading to an administrative burden described above. Moreover, this workaround prevents automated creation of deals via an interface from a trading platform and automated correspondence exchange based on SWIFT MT3xx messages, resulting in a low automation level of treasury operations.

Add-on for SAP TRM

Currently, the ARF instruments are not supported in SAP Treasury and Risk management system (SAP TRM). In order to bridge the gap and to help the centralized treasury organizations to further streamline their operations, Zanders has developed an add-on for SAP TRM to manage the fixing of the average rate over the observation period, as well as to correctly calculate the fair value of the deals with partially fixed average rate.

The solution consists of dedicated average rate FX forward collective processing report, covering:

• Particular information related to ARF deals, including start and end of the fixation period, currently fixed average rate, fixed portion (percentage), locked-in result for the fixed portion of the deal in the settlement currency.
• Specific functions needed to manage this type of deals: creation, change, display of rate fixation schedule, as well as creating final fixation of the FX deal, once the average rate is fully calculated through the observation period.

Figure 1 Zanders FX Average Rate Forwards Cockpit and the ARF specific key figures

The solution builds on the standard SAP functionality available for FX deal management, meaning all other proven functionalities are available, such as payments, posting via treasury accounting subledger, correspondence, EMIR reporting, calculation of fair value for month-end evaluation and reporting. Through an enhancement, the solution is fully integrated into market risk, credit risk and, if needed, portfolio analyser too. Therefore, correct mark-to-market is always calculated for both the fixed and unfixed portion of the deal.

Figure 2 Integration of Zanders ARF solution into SAP Treasury Transaction manager process flow

The solution builds on the standard SAP functionality available for FX deal management, meaning all other proven functionalities are available, such as payments, posting via treasury accounting subledger, correspondence, EMIR reporting, calculation of fair value for month-end evaluation and reporting. Through an enhancement, the solution is fully integrated into market risk, credit risk and, if needed, portfolio analyser too. Therefore, correct mark-to-market is always calculated for both the fixed and unfixed portion of the deal.

Zanders can support you with the integration of ARF forwards into your FX exposure management process. For more information do not hesitate to contact Michal Šárnik.

This exemption has been applied in a diverse way by supervisors and therefore, the treatment of Structural FX risk has been updated in recent regulatory publications. In this article we discuss these publications and market practice around Structural FX risk based on an analysis of the policies applied by the top 25 banks in Europe.

Based on the 1996 amendment to the Capital Accord, banks that apply for the exemption of Structural FX positions can exclude these positions from the Pillar 1 capital requirement for market risk. This exemption was introduced to allow international banks with subsidiaries in currencies different from the reporting currency to employ a strategy to hedge the capital ratio from adverse movements in the FX rate. In principle a bank can apply one of two strategies in managing its FX risk.

1. In the first strategy, the bank aims to stabilize the value of its equity from movements in the FX rate. This strategy requires banks to maintain a matched currency position, which will effectively protect the bank from losses related to FX rate changes. Changes in the FX rate will not impact the equity of a bank with e.g. a consolidated balance sheet in Euro and a matched USD position. The value of the Risk-Weighted Assets (RWAs) is however impacted. As a result, although the overall balance sheet of the bank is protected from FX losses, changes in the EUR/USD exchange rate can have an adverse impact on the capital ratio.
2. In the alternative strategy, the objective of the bank is to protect the capital adequacy ratio from changes in the FX rate. To do so, the bank deliberately maintains a long, open currency position, such that it matches the capital ratio. In this way, both the equity and the RWAs of the bank are impacted in a similar way by changes in the EUR/USD rate, thereby mitigating the impact on the capital ratio. Because an open position is maintained, FX rate changes can result in losses for the bank. Without the exemption of Structural FX positions, the bank would be required to hold a significant amount of capital for these potential losses, effectively turning this strategy irrelevant.

As can also be seen in the exhibit below, the FX scenario that has an adverse impact on the bank differs between both strategies. In strategy 1, an appreciation of the currency will result in a decrease of the capital ratio, while in the second strategy the value of the equity will increase if the currency appreciates. The scenario with an adverse impact on the bank in strategy 2 is when the foreign currency depreciates.

Until now, only limited guidance has been available on e.g. the risk management framework, (number of) currencies that can be in scope of the exemption and the maximum open exposure that can be exempted. As a result, the practical implementation of the Structural FX exemption varies significantly across banks. Recent regulatory publications aim to enhance regulatory guidance to ensure a more standardized application of the exemption.

Regulatory Changes

With the publication of the Fundamental Review of the Trading Book (FRTB) in January 2019, the exemption of Structural FX risk was further clarified. The conditions from the 1996 amendment were complemented to a total of seven conditions related to the policy framework required for FX risk and the maximum and type of exposure that can be in scope of the exemption. Within Europe, this exemption is covered in the Capital Requirements Regulation under article 352(2).

To process the changes introduced in the FRTB and to further strengthen the regulatory guidelines related to Structural FX, the EBA has issued a consultation paper in October 2019. A final version of these guidelines was published in July 2020. The date of application was pushed back one year compared to the consultation paper and is now set for January 2022.

The guidelines introduced by EBA can be split in three main topics:

1. Definition of Structural FX.
   The guidelines provide a definition of positions of a structural nature and positions that are eligible to be exempted from capital. Positions of a structural nature are investments in a subsidiary with a reporting currency different from that of the parent (also referred to as Type A), or positions that are related to the cross-border nature of the institution that are stable over time (Type B). A more elaborate justification is required for Type B positions and the final guidelines include some high-level conditions for this.
2. Management of Structural FX.
   Banks are required to document the appetite, risk management procedures and processes in relation to Structural FX in a policy. Furthermore, the risk appetite should include specific statements on the maximum acceptable loss resulting from the open FX position, on the target sensitivity of the capital ratios and the management action that will be applied when thresholds are crossed. It is moreover clarified that the exemption can in principle only be applied to the five largest structural currency exposures of the bank.
3. Measurement of Structural FX.
   The guidelines include requirements on the type and the size of the positions that can be in scope of the exemption. This includes specific formulas on the calculation of the maximum open position that can be in scope of the exemption and the sensitivity of the capital ratio. In addition, banks will need to report the structural open position, maximum open position, and the sensitivity of the capital ratio, to the regulator on a quarterly basis.

One of the reasons presented by the EBA to publish these additional guidelines is a growing interest in the application of the Structural FX exemption in the market.

Market Practice

To understand the current policy applied by banks, a review of the 2019 annual reports of the top 25 European banks was conducted. Our review shows that almost all banks identify Structural FX as part of their risk identification process and over three quarters of the banks apply a strategy to hedge the CET1 ratio, for which an exemption has been approved by the ECB. While most of the banks apply the exemption for Structural FX, there is a vast difference in practices applied in measurement and disclosure. Only 44% of the banks publish quantitative information on Structural FX risk, ranging from the open currency exposure, 10-day VaR losses, stress losses or Economic Capital allocated.

The guideline that will have a significant impact on Structural FX management within the bigger banks of Europe is the limit to include only the top five open currency positions in the exemption: of the banks that disclose the currencies in scope of the Structural FX position, 60% has more than 5 and up to 20 currencies in scope. Reducing that to a maximum of five will either increase the capital requirements of those banks significantly or require banks to move back to maintaining a matched position for those currencies, which would increase the capital ratio volatility.

Conclusion

The EBA guidelines on Structural FX that will to go live by January 2022 are expected to have quite an impact on the way banks manage their Structural FX exposures. Although the Structural FX policy is well developed in most banks, the measurement and steering of these positions will require significant updates. It will also limit the number of currencies that banks can identify as Structural FX position. This will make it less favourable for international banks to maintain subsidiaries in different currencies, which will increase the cost of capital and/or the capital ratio volatility.

Finally, a topic that is still ambiguous in the guidelines is the treatment of Structural FX in a Pillar 2 or ICAAP context. Currently, 20% of the banks state to include an internal capital charge for open structural FX positions and a similar amount states to not include an internal capital charge. Including such a capital charge, however, is not obvious. Although an open FX position will present FX losses for a bank which would favour an internal capital charge, the appetite related to internal capital and to the sensitivity of the capital ratio can counteract, resulting in the need for undesirable FX hedges.

The new guidelines therefore present clarifications in many areas but will also require banks to rework a large part of their Structural FX policies in the middle of a (COVID-19) crisis period that already presents many challenges.

Alternatives for a calculating compounded SARON

After explaining in the previous article the use of compounded SARON as a term alternative to CHF LIBOR, the Swiss National Working Group (NWG) published several options as to how a compounded SARON could be used as a benchmark in banking products, such as loans or mortgages, and financial instruments (e.g. capital market instruments). Underlying these options is the question of how to best mitigate uncertainty about future cash flows, a factor that is inherent in the compounding approach. In general, it is possible to separate the type of certainty regarding future interest payments in three categories . The market participant has:

• an aversion to variable future interest payments (i.e. payments ex-ante unknown). Buying fixed-rate products is best, where future cash flows are known for all periods from inception. No benchmark is required due to cash flow certainty over the lifetime of the product.
• a preference for floating-rate products, where the next cash flow must be known at the beginning of each period. The option ‘in advance’ is applicable, where cash flow certainty exists for a single period.
• a preference for floating-rate products with interest rate payments only close to the end of the period are tolerated. The option ‘in arrears’ is suitable, where cash flow certainty only close to the end of each period exists.

Based on the Financial Stability Board (FSB) user’s guide, the Swiss NWG recommends considering six different options to calculate a compounded risk-free rate (RFR). Each financial institution should assess these options and is recommended to define an action plan with respect to its product strategy. The compounding options can be segregated into options where the future interest rate payments can be categorized as in arrears, in advance or hybrid. The difference in interest rate payments between ‘in arrears’ and ‘in advance’ conventions will mainly depend on the steepness of the yield curve. The naming of the compounding options can be slightly different among countries, but the technique behind those is generally the same. For more information regarding the available options, see Figure 1.

Moreover, for each compounding technique, an example calculation of the 1-month compounded SARON is provided. In this example, the start date is set to 1 February 2019 (shown as today in Figure 1) and the payment date is 1 March 2019. Appendix I provides details on the example calculations.

Figure 1: Overview of alternative techniques for calculating compounded SARON. Source: Financial Stability Board (2019).

0) Plain (in arrears): The observation period is identical to the interest period. The notional is paid at the start of the period and repaid on the last day of the contract period together with the last interest payment. Moreover, a Plain (in arrears) structure reflects the movement in interest rates over the full interest period and the payment is made on the day that it would naturally be due. On the other hand, given publication timing for most RFRs (T+1), the requiring payment is on the same day (T+1) that the final payment amount is known (T+1). An exception is SARON, as SARON is published one business day (T+0) before the overnight loan is repaid (T+1).

Example: the 1-month compounded SARON based on the Plain technique is like the example explained in the previous article, but has a different start date (1 February 2019). The resulting 1-month compounded SARON is equal to -0.7340% and it is known one day before the payment date (i.e. known on 28 February 2019).

1) Payment Delay (in arrears): Interest rate payments are delayed by X days after the end of an interest period. The idea is to provide more time for operational cash flow management. If X is set to 2 days, the cash flow of the loan matches the cash flow of most OIS swaps. This allows perfect hedging of the loan. On the other hand, the payment in the last period is due after the payback of the notional, which leads to a mismatch of cash flows and a potential increase in credit risk.

Example: the 1-month compounded SARON is equal to -0.7340% and like the one calculated using the Plain (in arrears) technique. The only difference is that the payment date shifts by X days, from 1 March 2019 to e.g. 4 March 2019. In this case X is equal to 3 days.

2) Lockout Period (in arrears): The RFR is no longer updated, i.e. frozen, for X days prior to the end of an interest rate period (lockout period). During this period, the RFR on the day prior to the start of the lockout is applied for the remaining days of the interest period. This technique is used for SOFR-based Floating Rate Notes (FRNs), where a lockout period of 2-5 days is mostly used in SOFR FRNs. Nevertheless, the calculation of the interest rate might be considered less transparent for clients and more complex for product providers to be implemented. It also results in interest rate risk that is difficult to hedge due to potential changes in the RFR during the lockout period. The longer the lockout period, the more difficult interest rate risk can be hedged during the lockout period.

Example: the 1-month compounded SARON with a lockout period equal to 3 days (i.e. X equals 3 days) is equal to -0.7337% and known 3 days in advance of the payment date.

3) Lookback (in arrears): The observation period for the interest rate calculation starts and ends X days prior to the interest period. Therefore, the interest payments can be calculated prior to the end of the interest period. This technique is predominately used for SONIA-based FRNs with a delay period of X equal to 5 days. An increase in interest rate risk due to changes in yield curve is observed over the lifetime of the product. This is expected to make it more difficult to hedge interest rate risk.

Example: assuming X is equal to 3 days, the 1-month compounded SARON would start in advance, on January 29, 2019 (i.e. today minus 3 days). This technique results in a compounded 1-month SARON equal to -0.7335%, known on 25 February 2019 and payable on 1 March 2019.

4) Last Reset (in advance): Interest payments are based on compounded RFR of the previous period. It is possible to ensure that the present value is equivalent to the Plain (in arrears) case, thanks to a constant mark-up added to the compounded RFR. The mark-up compensates the effects of the period shift over the full life of the product and can be priced by the OIS curve. In case of a decreasing yield curve, the mark-up would be negative. With this technique, the product is more complex, but the interest payments are known at the start of the interest period, as a LIBOR-based product. For this reason, the mark-up can be perceived as the price that a borrower is willing to pay due to the preference to know the next payment in advance.

Example: the interest rate payment on 1 March 2019 is already known at the start date and equal to -0.7328% (without mark-up).

5) Last Recent (in advance): A single RFR or a compounded RFR for a short number of days (e.g. 5 days) is applied for the entire interest period. Given the short observation period, the interest payment is already known in advance at the start of each interest period and due on the last day of that period. As a consequence, the volatility of a single RFR is higher than a compounded RFR. Therefore, interest rate risk cannot be properly hedged with currently existing derivatives instruments.

Example: a 5-day average is used to calculate the compounded SARON in advance. On the start date, the compounded SARON is equal to -0.7339% (known in advance) that will be paid on 1 March 2020.

6) Interest Rollover (hybrid): This technique combines a first payment (installment payment) known at the beginning of the interest rate period with an adjustment payment known at the end of the period. Like Last Recent (in advance), a single RFR or a compounded RFR for a short number of days is fixed for the whole interest period (installment payment known at the beginning). At the end of the period, an adjustment payment is calculated from the differential between the installment payment and the compounded RFR realized during the interest period. This adjustment payment is paid (by either party) at the end of the interest period (or a few days later) or rolled over into the payment for the next interest period. In short, part of the interest payment is known already at the start of the period. Early termination of contracts becomes more complex and a compensation mechanism is needed.

Example: similar to Last Recent (in advance), a 5-day compounded SARON can be considered as installment payment before the starting date. On the starting date, the 5-day compounded SARON rate is equal to -0.7339% and is known to be paid on 1 March 2019 (payment date). On the payment date, an adjustment payment is calculated as the delta between the realized 1-month compounded SARON, equal to -0.7340% based on Plain (in arrears), and -0.7339%.

There is a trade-off between knowing the cash flows in advance and the desire for a payment structure that is fully hedgeable against realized interest rate risk. Instruments in the derivatives market currently use ‘in arrears’ payment structures. As a result, the more the option used for a cash product deviates from ‘in arrears’, the less efficient the hedge for such a cash product will be. In order to use one or more of these options for cash products, operational cash management (infrastructure) systems need to be updated. For more details about the calculation of the compounded SARON using the alternative techniques, please refer to Table 1 and Table 2 in the Appendix I. The compounding formula used in the calculation is explained in the previous article.

Overall, market participants are recommended to consider and assess all the options above. Moreover, the financial institutions should individually define action plans with respect to their own product strategies.

Conclusions

The transition from IBOR to alternative reference rates affects all financial institutions from a wide operational perspective, including how products are created. Existing LIBOR-based cash products need to be replaced with SARON-based products as the mortgages contract. In the next installment, IBOR Reform in Switzerland – Part III, the latest information from the Swiss National Working Group (NWG) and market developments on the compounded SARON will be explained in more detail.

Appendix I – II

Contact

For more information about the challenges and latest developments on SARON, please contact Martijn Wycisk or Davide Mastromarco of Zanders’ Swiss office: +41 44 577 70 10.

The other articles on this subject: 

Transition from CHF LIBOR to SARON, IBOR Reform in Switzerland, Part I
Compounded SARON and Swiss Market Development, IBOR Reform in Switzerland, Part III
Fallback provisions as safety net, IBOR Reform in Switzerland, Part IV

References

1. Mastromarco, D. Transition from CHF LIBOR to SARON, IBOR Reform in Switzerland – Part I. February 2020.
2. National Working Group on Swiss Franc Reference Rates. Discussion paper on SARON Floating Rate Notes. July 2019.
3. National Working Group on Swiss Franc Reference Rates. Executive summary of the 12 November 2019 meeting of the National Working Group on Swiss Franc Reference Rates. Press release November 2019.
4. National Working Group on Swiss Franc Reference Rates. Starter pack: LIBOR transition in Switzerland. December 2019.
5. Financial Stability Board (FSB). Overnight Risk-Free Rates: A User’s Guide. June 2019.
6. ISDA. Supplement number 60 to the 2006 ISDA Definitions. October 2019.
7. ISDA. Interbank Offered Rate (IBOR) Fallbacks for 2006 ISDA Definitions. December 2019.
8. National Working Group on Swiss Franc Reference Rates. Executive summary of the 7 May 2020 meeting of the National Working Group on Swiss Franc Reference Rates. Press release May 2020

Two building blocks

The appropriate approach and level of complexity in the mortgage valuation depend on the underlying purpose. Examples of valuation purposes are: regulatory, accounting, risk or sales of the mortgage portfolio. For example BCBS, IRRBB, Solvency, IFRS and the EBA ask for (specific) valuation methods of mortgages. The two building blocks for a ‘fair value’ calculation of mortgages are expected cash flows and a discount curve.

The market value is the sum of the expected cash flows at the moment of valuation, which are derived by discounting future expected cash flows with an appropriate curve. For both building block models, choices have to be made resulting in a tradeoff between the accuracy level and the computational effort.

Figure 1: Constructing the expected cash flows from the contractual cash flows for a loan with an annuity repayment type.

Cash flow schedule

The contractual cash flows are projected cash flows, including repayments. These can be derived based on the contractually agreed loan components, such as the interest rate, the contractual maturity and the redemption type.

The three most commonly used redemption types in the mortgage market are:

• Bullet: interest only payments, no contractual repayment cash flows except at maturity
• Linear: interest (decreasing monthly) and constant contractual repayment cash flows
• Annuity: fixed cash flows, consisting of an interest and contractual repayment part

However, the expected cash flows will most likely differ from this contractually agreed pattern due to additional prepayments. Especially in the current low interest rate environment, borrowers frequently make prepayments on top of the scheduled repayments.

Figure 1 shows how to calculate an expected cash flow schedule by adding the prepayment cash flows to the contractual cash flow. There are two methods to derive : client behavior dependent on interest rates and client behavior independent of interest rates. The independent method uses an historical analysis, indicating a backward looking element. This historical analysis can include a dependency on certain contract characteristics.

On the other hand, the interest rate dependent behavior is forward looking and depends on the expected level of the interest rates. Monte Carlow simulations can model interest dependent behavior.

Another important factor in client behavior are penalties paid in case of a prepayment above a contractually agreed threshold. These costs are country and product specific. In Italy, for example, these extra costs do not exist, which could currently result in high prepayments rates.

Discount curve

The curve used for cash flow discounting is always a zero curve. The zero curve is constructed from observed interest rates which are mapped on zero-coupon bonds to maturities across time. There are three approaches to derive the rates of this discount curve: the top down-approach, the bottom-up approach or the negotiation approach. The first two methods are the most relevant and common.

In theory, an all-in discount curve consists of a riskfree rate and several spread components. The ‘base’ interest curve concerns the risk-free interest rate term structure in the market at the valuation date with the applicable currency and interest fixing frequency (or use ccy- and basis-spreads). The spreads included depend on the purpose of the valuation. For a fair value calculation, the following spreads are added: liquidity spread, credit spread, operational cost, option cost, cost of capital and profit margin. An example of spreads included for other valuation purposes are offerings costs and origination fee.

Top-down versus Bottom-up

The chosen calculation approach depends on the available data, the ability to determine spread components, preferences and the purpose of the valuation.

A top-down method derives the applied rates of the discount curve from all-in mortgage rates on a portfolio level. Different rates should be used to construct a discount curve per mortgage type and LTV level, and should take into account the national guaranteed amount (NHG in the Netherlands). Subtract all-in mortgage rates spreads that should not part of the discount curve, such as the offering costs. Use this top-down approach when limited knowledge or tools are available to derive all the individual spread components. The all-in rates can be obtained from the following sources: mortgage rates in the market, own mortgage rates or by designing a mortgage pricing model.

Figure 2

The bottom-up approach constructs the applied discount curve by adding all applicable spreads on top of the zero curve at a contract level. This method requires that several spread components can be calculated separately. The top-down approach is quicker, but less precise than the bottom-up approach, which is more accurate but also computationally heavy. Additionally, the bottom-up method is only possible if the appropriate spreads are known or can be derived. One example of a derivation of a spread component is credit spreads determined from expected losses based on an historical analysis and current market conditions.

In short

A fair value calculation performed by a discounted cash flow method consists of two building blocks: the expected cash flows and a discount curve. This requires several model choices before calculating a fair value of a mortgage (portfolio).

The expected cash flow model is based on the contractual cash flows and any additional prepayments. The mortgage prepayments can be modeled by assuming interest dependent or interest independent client behavior.

To construct the discount curve, the relevant spreads should be added to the risk-free curve. The decision for a top-down or bottom-up approach depends on the available data, the ability to determine spread components, preferences and the purpose of the valuation.

These important choices do not only apply for fair value calculations but are applicable for many other mortgage valuation purposes.

 Zanders Valuation Desk

Independent, high quality, market practice and accounting standard proof are the main drivers of our Valuation Desk. For example, we ensure a high quality and professionalism with a strict, complete and automated check on the market data from our market data provider on a daily basis. Furthermore, we have increased our independence by implementing the F3 solution from FINCAD in our current valuation models. This permits us to value a larger range of financial instruments with a high level of quality, accuracy and wider complexity.

For more information or questions concerning valuation issues, please contact Pierre Wernert: p.wernert@zanders.eu.

The standard model, as defined by IFRS 17, of measuring the value of insurance contracts is the ‘building blocks approach’. In this approach, the value of the contract is measured as the sum of the following components:

• Block 1: Sum of the future cash flows that relate directly to the fulfilment of the contractual  obligations.
• Block 2: Time value of the future cash flows. The discount rates used to determine the time value reflect the characteristics of the insurance contract.
• Block 3: Risk adjustment, representing the compensation that the insurer requires for bearing the uncertainty in the amount and timing of the cash flows.
• Block 4: Contractual service margin (CSM), representing the amount available for overhead and profit on the insurance contract. The purpose  of the CSM is to prevent a gain at initiation of the contract.

Risk adjustment vs risk margin

IFRS 17 does not provide full guidance on how the risk adjustment should be calculated. In theory, the compensation required by the insurer for bearing the risk of the contract would be equal to the cost of the needed capital. As most insurers within the IFRS jurisdiction capitalize based on Solvency II (SII) standards, it is likely that they will leverage on their past experience. In fact, there are many similarities between the risk adjustment and the SII risk margin.

The risk margin represents the compensation required for non-hedgeable risks by a third party that would take over the insurance liabilities. However, in practice, this is calculated using the capital models of the insurer itself. Therefore, it seems likely that the risk margin and risk adjustment will align. Differences can be expected though. For example, SII allows insurers to include operational risk in the risk margin, while this is not allowed under IFRS 17.

Liability adequacy test

Determining the impact of IFRS 17 is not straightforward: the current IFRS accounting standard leaves a lot of flexibility to determine the reserve value for insurance liabilities (one of the reasons for introducing IFRS 17). The reserve value reported under current IFRS is usually grandfathered from earlier accounting standards, such as Dutch GAAP. In general, these reserves can be defined as the present value of future benefits, where the technical interest rate and the assumptions for mortality are locked-in at pricing.

However, insurers are required to perform liability adequacy testing (LAT), where they compare the reserve values with the future cash flows calculated with ‘market consistent’ assumptions. As part of the market consistent valuation, insurers are allowed to include a compensation for bearing risk, such as the risk adjustment. Therefore, the biggest impact on the reserve value is expected from the introduction of the CSM.

The IASB has defined a hierarchy for the approach to measure the CSM at transition date. The preferred method is the ‘full retrospective application’. Under this approach, the insurer is required to measure the insurance contract as if the standard had always applied. Hence, the value of the insurance contract needs to be determined at the date of initial recognition and consecutive changes need to be determined all the way to transition date. This process is outlined in the following case study.

A case study

The impact of the new IFRS standards is analyzed for the following policy:

• The policy covers the risk that a mortgage owner deceases before the maturity of the loan. If this event occurs, the policy pays the remaining notional of the loan.
• The mortgage is issued on 31 December 2015 and has an initial notional value of € 200,000 that is amortized in 20 years. The interest percentage is set at 3 per cent.
• The policy pays an annual premium of € 150. The annual estimated costs of the policy are equal to 10 per cent of the premium.

In the case of this policy, an insurer needs to capitalize for the risk that the policy holder’s life expectancy decreases and the risk that expenses will increase (e.g. due to higher than expected inflation). We assume that the insurer applies the SII standard formula, where the total capital is the sum of the capital for the individual risk types, based on 99.5 per cent VaR approach, taking diversification into account.

The cost of capital would then be calculated as follows:

• Capital for mortality risk is based on an increase of 15 per cent of the mortality rates.
• Capital for expense risk is based on an increase of 10 per cent in expense amount combined with an increase of 1 per cent in the inflation.
• The diversification between these risk types is assumed to be 25 per cent.
• Future capital levels are assumed to be equal to the current capital levels, scaled for the decrease in outstanding policies and insurance coverage.
• The cost-of-capital rate equals 6 per cent.

At initiation (i.e. 2015 Q4), the value of the contract under the new standards equals the sum of:

• Block 1: € 482
• Block 2: minus € 81
• Block 3: minus € 147
• Block 4: minus € 254

Consecutive changes

The insurer will measure the sum of blocks 1, 2 and 3 (which we refer to as the fulfilment cash flows) and the remaining amount of the CSM at each reporting date. The amounts typically change over time, in particular when expectations about future mortality and interest rates are updated. We distinguish four different factors that will lead to a change in the building blocks:

Step 1. Time effect
Over time, both the fulfilment cash flows and the CSM are fully amortized. The amortization profile of both components can be different, leading to a difference in the reserve value.

Step 2. Realized mortality is lower than expected
In our case study, the realized mortality is about 10 per cent lower than expected. This difference is recognized in P&L, leading to a higher profit in the first year. The effect on the fulfilment cash flows and CSM is limited. Consequently, the reserve value will remain roughly the same.

Step 3. Update of mortality assumptions
Updates of the mortality assumptions affect the fulfilment cash flows, which is simultaneously recognized in the CSM. The offset between the fulfilment cash flows and the CSM will lead to a very limited impact on the reserve value. In this case study, the update of the life table results in higher expected mortality and increased future cash outflows.

Step 4. Decrease in interest rates
Updates of the interest rate curve result in a change in the fulfilment cash flows. This change is not offset in the CSM, but is recognized in the other comprehensive income. Therefore a decrease in the discount curve will result in a significant change in the insurance liability. Our case study assumes a decrease in interest rates from 2 per cent to 1 per cent. As a result, the fulfilment cash flows increase, which is immediately reflected by an increase in the reserve value.

The impact of each step on the reserve value and underlying blocks is illustrated below.

Onwards

The policy will evolve over time as expected, meaning that mortality will be realized as expected and discount rates do not change anymore. The reserve value and P&L over time will evolve as illustrated below.

The profit gradually decreases over time in line with the insurance coverage (i.e. outstanding notional of the mortgage). The relatively high profit in 2016 is (mainly) the result of the realized mortality that was lower than expected (step 2 described above).

As described before, under the full retrospective application, the insurer would be required to go all the way back to the initial recognition to measure the CSM and all consecutive changes. This would require insurers to deep-dive back into their policy administration systems. This has been acknowledged by the IASB by allowing insurers to implement the standards three years after final publication. Insurers will have to undertake a huge amount of operational effort and have already started with their impact analyses. In particular, the risk adjustment seems a challenging topic that requires an understanding of the capital models of the insurer.

Zanders can support in these qualitative analyses and can rely on its past experience with the implementation of Solvency II.

1. Accounting for the forward element in foreign currency forwards

Each FX forward contract possesses a spot and forward element. The forward element represents the interest rate differential between the two currencies. Under IFRS 9 (similar to IAS 39), it is allowed to designate the entire contract or just the spot component as the hedging instrument. When designating the spot component only, the change in fair value of the forward element is recognised in OCI and accumulated in a separate component of equity. Simultaneously, the fair value of the forward points at initial recognition is amortised, most expected linearly, over the life of the hedge.

Again, this accounting treatment is only allowed in case the critical terms are aligned (similar). If at inception the actual value of the forward element exceeds the aligned value, changes in the fair value based on the aligned item will go through OCI. The difference between the fair value of the actual and aligned forward elements is recognized in P&L. In case the value of the aligned forward element exceeds the actual value at inception, changes in fair value are based on the lower of aligned versus actual and go to OCI. The remaining change of actual will be recognized in P&L.

Please refer to the example below:

In this example, we consider an entity X which is hedging a future receivable with an FX forward contract.

MtM change of the forward = 105,000 (spot element) + 15,000 (forward element) = 120,000.
MtM change of the hedged item = 105,000 (spot element) + 5,000 (forward element) = 110,000.

 We look at alternatives under IAS39 and IFRS9 that show different accounting methods depending on the separation between the spot and forward rates.

Under IAS39 and without a spot/forward separation, the hedging instrument represents the sum of the spot and the forward element (105 000 spot + 15 000 forward= 120 000). The hedged item consisting of 105 000 spot element and 5 000 forward element and the hedge ratio being within the boundaries, the minimum between the hedging instrument and hedged item is listed as OCI, and the difference between the hedging instrument and the hedged item goes to the P&L.

However, with the spot/forward separation under IAS39, the forward component is not included in the hedging relationship and is therefore taken straight to the P&L. Everything that exceeds the movement of the hedged item is considered as an “over hedge” and will be booked in P&L.

Line 3 and 4 under IFRS9 characterise comparable registration practices than under IAS39. The changes come in when we examine line 5, where the forward element of 5 000 can be registered as OCI. In this case, a test on both the spot and the forward element is performed, compared to the previous line where only one test takes place.

2. Rebalancing in a commodity hedge relation

Under influence of changing economic circumstances, it could be necessary to change the hedge ratio, i.e. the ratio between the amount of hedged item and the amount of hedging instruments. Under IAS 39, changes to a hedge ratio require the entity to discontinue hedge accounting and restart with a new hedging relationship that captures the desired changes. The IFRS 9 hedge accounting model allows you to refine your hedge ratio without having to discontinue the hedge relationship. This can be achieved by rebalancing.

Rebalancing is possible if there is a situation where the change in the relationship of the hedging instrument and the hedged item can be compensated by adjusting the hedge ratio. The hedge ratio can be adjusted by increasing or decreasing either the number of designated hedging instruments or hedged items.

When rebalancing a hedging relationship, an entity must update its documentation of the analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its remaining term.

Please refer to the example below:

Entity X is hedging a forecast receivable with a FX call.

MtM change of the option = 100,000 (intrinsic value) + 40,000 (time value) = 140,000.
MtM change of the hedged item = 100,000 (intrinsic value) + 30,000 (time value) = 130,000.

In example 3, we consider entity X to be hedging a forecast receivable via an FX call. Note that under IAS39 the hedged item cannot contain an optionality if this optionality is not present in the underlying exposure. Hence, in this example, the hedged item cannot contain any time value. The time value of 30,000 can be used under IFRS9, but only by means of a separate test (see row 5).

In line 1, we can see that without a time-intrinsic separation, the hedge relationship is no longer within the 80-125% boundary; therefore, it needs to be discontinued and the full MtM has to be booked in the P&L. In line 2, there is a time-intrinsic separation, and the 40 000 representing the time value of the option are not included in the hedge relationship, meaning that they go straight to the P&L.

Under IFRS9 with no time-intrinsic separation (line 3), the hedging relationship is accounted for in the usual manner, as the ineffectiveness boundary is not applicable, with 100 000 going representing OCI, and the over hedged 40 000 going to the P&L.

However, the time-intrinsic separation under IFRS9 in line 4 is similar to line 2 under IAS39, in which we choose to immediately remove the time value for the option from the hedging relationship. We therefore have to account for 40 000 of time value in the P&L.

In the last line, we separate between time and intrinsic values, but the time value of the option is aimed to be booked into OCI. In this case, a test on both the intrinsic and the time element is performed. We can therefore comprise 100 000 in the intrinsic OCI, 30 000 in the time OCI, and 10 000 as an over hedge in the P&L.

4. Cross-currency basis spread are considered a cost of hedging

The cross-currency basis spread can be defined as the liquidity premium of one currency over the other. This premium applies to exchanges of currencies in the future, e.g. a hedging instrument like an FX forward contract. If a cross currency interest rate swap is used in combination with a single currency hedged item, for which this spread is not relevant, hedge ineffectiveness could arise.

In order to cope with this mismatch, it has been decided to expand the requirements regarding the costs of hedging. Hedging costs can be seen as cost incurred to protect against unfavourable changes. Similar to the accounting for the forward element of the forward rate, an entity can exclude the cross-currency basis spread and account for it separately when designating a hedging instrument. In case a hypothetical derivative is used, the same principle applies. IFRS 9 states that the hypothetical derivative cannot include features that do not exist in the hedged item. Consequently, cross-currency basis spread cannot be part of the hypothetical derivative in the previously mentioned case. This means that hedge ineffectiveness will exist.

Please refer to the example below:

In example 4, we consider an entity X hedging a USD loan with a CCIRS.

MtM change of CCIRS = 215,000 – 95,000 (cross-currency basis) = 120,000.
MtM change hedged = 195,000 – 90,000 (cross-currency basis) = 105,000.

Under IAS39, there is only one way to account for CCIRS. The full amount of 120 000 (including the – 95 000 cross-currency basis) is considered as the hedging instrument, meaning that 105 000 can be listed as OCI and 15 000 of over hedge have to go to the P&L.

Under IFRS9, there is the option to exclude the cross-currency basis and account for it separately.

In line 2, we can see the conditions under IFRS9 when a cross-currency basis is included: the cross-currency basis cannot be comprised in the hedged item, so there is an under hedge of 75 000.

In line 3, we exclude the cross-currency basis from the test for the hedging instrument. By registering the MtM movement of 195 000 as OCI, we then account for the 95 000 of cross-currency basis, as well as -/- 20 000 of over hedge in the P&L. In line 4, the cross-currency basis is included in a separate hedge relationship – we therefore perform an extra test on the cross-currency basis (aligned versus actual values) . From the first test, -/- 195,000 is registered as OCI and -/- 20,000 (“over hedge” part) in P&L; from the cross-currency basis test 90,000 represents OCI and 5,000 has to be included in P&L.

Impact

IFRS 9 will, amongst others, rock the balance sheet, affect business models, risk awareness, processes, analytics, data and systems across several dimensions.

We will name a few related to the financials:

• Transition from IAS 39 to IFRS 9 will lead to a change in the level of provision for credit losses. The transition of the current provisions, which are based only on actual losses and incurred but not reported (IBNR) losses, to an expected loss is likely to have significant impact on shareholder equity, net income and capital ratios.
• P&L volatility is expected to increase after transition, since deterioration in credit quality or changes in expected credit loss will have a direct impact on P&L. The P&L volatility will, however, significantly differ per type of credit portfolio, also depending on counterparty ratings and remaining maturity. Portfolios with loans rated below investment grade will move faster from ‘state 1’ to ‘state 2’ (see box), since a move within investment grade ratings is not seen as a credit quality deterioration. Portfolios with long maturities will face large P&L volatility when moving from state 1 to state 2.
• Capital levels and deal pricing will be affected by the expected provisions.

Total P&L over time will not change, since the expected credit loss provision is booked against the actual credit losses during lifetime. If there is no actual credit loss, all provisions will fall free as profit towards maturity.

Forward-looking

IFRS 9 requires financial institutions to adjust the current backward-looking incurred loss based credit provision into a forward-looking expected credit loss. This sounds logical for an accounting provision and it assumes that existing relevant models within risk management may be applied. However, there are some difficulties to overcome.

Incorporating forward-looking information means moving away from the through-the-cycle approach towards an estimation of the ‘business cycle’ of potential credit losses. A forward-looking expected credit loss calculation should be based on an accurate estimation of current and future probability of default (PD), exposure at default (EAD), loss given default (LGD), and discount factors. Discount factors according to IFRS 9 are based on the effective interest rate; this subject will not be further addressed here. The EAD can mainly be derived from current exposure, contractual cash flows and an estimate of unscheduled repayments and an expectation of the use of undrawn credit limits. Both unscheduled repayments and undrawn amounts are known to be business cycle dependent. Forecasting these items can be derived from historical observations.

Of course, the best calibration is on defaulted data since we determine exposure at default. If insufficient data is available, cycle dependent unscheduled repayments and drawing of credit limits can be derived from the entire credit portfolio, preferably corrected with some expert judgement to reflect the situation at default.

Banks have internal rating models in place to assign a PD to a counterparty and for trenching the portfolio in different levels with a specific PD. From a capital point of view, these ratings are mostly calibrated to a through-the-cycle level of observed defaults. Now using all the bank’s forward-looking information may improve estimates if business cycle(s) can be identified, potential scenarios of the development of the cycle in the future can be forecasted, including how the cycle affects a bank’s PD term structure. This would be a macroeconomic and econometric heaven if there were sufficient data available to derive accurate and statistically significant models. Otherwise, banks need to rely more on expert judgement and external macroeconomic reports.

Next to the PD term structures, LGD term structures are required to calculate a life time expected loss. Deriving an accurate LGD term structure from realized defaults requires a large default database. Deriving a business-cycle dependent LGD term structure requires an even bigger database of accurately and timely documented losses. The level of business cycle dependency of LGD significantly differs per type of counterparty, industry, and collateral. Subordination is not much cycle dependent, while loans covered with collateral, such as mortgage loans, may result in large movements in LGDs over time. Hence, this requires different LGD term structures for different LGD types and levels.

Economic scenarios

Incorporating forward-looking information means modeling business cycle dependency in your PD and LGD. For significant drivers, future scenarios are required to calculate expected credit loss. At most banks, these forward-looking scenarios are commonly the domain of economic research departments. Macroeconomic forecasting concentrates mainly on country-specific variables. Growth of domestic product, unemployment rates, inflation indices and interest rates are typical projected variables.

Usually, only large international banks with an economic research department are able to project consistent economic outlooks and scenarios. Next to macro scenarios, industry specific forecasts are important. Industry risk models enable a bank to make forecasts for a certain industry segment, e.g. chemicals, automotive or oil & gas. Industry models are often based on variables such as market conditions, barriers to entry and default data. At some banks, industries are analyzed and scored by economic researchers. At others, usually smaller banks, industries are ranked by sector business specialists.

Industry scorings often form input for rating models and are important factors for portfolio management purposes. Therefore, caution is required in correlation between drivers of ratings and drivers of the PD term structure.

Credit portfolios

For homogenous retail exposures, forward-looking elements can be considered on a portfolio level by modeling the dependencies of PD and LGD percentages for realized defaults and losses; in essence this is a bottom-up approach. For mortgage portfolios, cycle dependency relates, for example, to unemployment and house price indices, among other factors. However, statistically significant parameters and models for default relations are difficult to obtain since there is a common time gap in observing and administrating both defaults and business cycle.

Model significance can be improved by adding additional variables with increasing risk of overfitting. Even if there is statistical proof for macroeconomic dependencies in PD and LGD rates, it is advised to be cautious, since it also requires designing credible macroeconomic scenarios. As business cycles are difficult to predict, this could lead to extra P&L volatility and an increase in the complexity and ‘explainability’ of figures. Therefore, regular back-testing and continuous monitoring are important for an accurate and robust provision mechanism, especially in the first years after the model is introduced.

For non-retail exposures, country and industry risk are, if embedded in the credit rating models, already part of the annual individual credit review and rating assignment processes. In the monthly financial reporting, additional country and industry risk factors can be taken into account on a portfolio basis, making provisions more forward looking; in essence a top-down approach. If necessary, risk management can make adjustments on an individual basis for wholesale counterparties, and facilities. A forward-looking overlay should improve the accuracy of provisions and a timely and adequate recognition of credit risk, instead of “too little, too late” as under the existing rules.

Governance

Because of the forward-looking character of IFRS 9, and the increasing role of risk models, a transparent and robust governance framework will become more important. Coordination and communication are required across risk, finance, business units, audit and IT.

Risk management typically delivers the expected credit loss parameters and calculations to finance on a monthly basis. Proposals for retail and nonretail adjustments briefly described above, must be discussed and agreed upon, after which the final proposal is submitted to the approval authority.

The governance framework should be documented and reviewed on an annual basis, and highlight key functions, stakeholders, definitions, data management, model (re)development, model implementation, portfolio monitoring and validation. In addition, all parties involved should speak the same credit risk language, have access to detailed data underlying the calculation of the provision and a good under- standing of the model and implications of decisions and parametrization. Only then can the finance department obtain an accurate understanding of the level and change of the provision and clearly inform the board and other stakeholders.

Zanders recommends preparing early for IFRS 9 and having a deep and thorough understanding of the impact, as well as the robust tooling and processes in place. Don’t just wait and ‘watch the hare running’, but start early, and at least run a shadow period during daylight to allow sufficient time.

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A replicating framework enables a bank to base its interest rate risk measurement and management on investments with a fixed maturity and price – while the deposits have no contractual maturity or price. In addition, a bank can use the framework to transfer the interest rate risk from the business lines to the central treasury, by turning the investments into contractual obligations. There are two commonly used methodologies for constructing the replicating portfolios: the marginal investment strategy and the portfolio investment strategy. These strategies have the same objective, but have different effects on margin and interest-rate term, given certain scenarios.

Strategies defined

An investment strategy determines the monthly allocation of the investable volume across various maturities. The investable volume in month t ( It ) consists of two parts:

The first part is equal to the decrease or increase in the volume of savings deposits compared to the previous month. The second part is equal to the total principal of all investments in the investment portfolio maturing in the current month (end date m = t ), Σi,m=t vi,m.

By investing or re-investing the volume of these two parts, the total principal of the investment portfolio will equal the savings volume outstanding at that moment. When an investment is generated, it receives the market interest rate relating to the maturity at that time. The portfolio investment return is determined as the principal weighted average interest rate.

The difference between a marginal investment strategy and a portfolio investment strategy is that in a marginal investment strategy, the volume is invested with a fixed allocation across fixed maturities. In a portfolio strategy, these parameters are flexible, however investments are generated in such a way that the resulting portfolio each month has the same (target) proportional maturity profile. The maturity profile provides the total monthly principal of the currently outstanding investments that will mature in the future.

In the savings modelling framework, the interest rate risk profile of the savings portfolio is estimated and defined as a (proportional) maturity profile. For the portfolio investment strategy, the target maturity profile is set equal to this estimated profile. For the marginal investment strategy, the ‘investment rule’ is derived from the estimated profile using a formula. Under long lasting constant or stable volume of savings deposits, the investment portfolio given the investment rule converges to the estimated profile.

Strategies illustrated

In Figure 1, the difference between the two strategies is graphically illustrated in an example. The example provides the development of replicating portfolios of the two strategies in two consecutive months upon increasing savings volume. The replicating portfolios initially consist of the same investments with original maturities of one month, 12 months and 36 months. To this end, the same investments and corresponding principals mature. The total maturing principal will be reinvested and the increase in savings volume will be invested.

Figure 1: Maturity profiles for the marginal (figure on top) and portfolie (figure below) investment strategies given increasing volume.

Note that if the savings volume would have remained constant, both strategies would have generated the same investments. However, with changing savings volume, the strategies will generate different investments and a different number of investments (3 under the marginal strategy, and 36 under the portfolio strategy).

The interest rate typical maturities and investment returns will therefore differ, even if market interest rates do not change. For the quantitative properties of the strategies, the decision will therefore focus mainly on margin stability and the interest rate typical maturity given changes in volume (and potential simultaneous movements in market interest rates).

Scenario analysis

The quantitative properties of the investment strategies are explained by means of a scenario analysis. The analysis compares the development of the duration, margin and margin stability of both strategies under various savings volume and market interest rate scenarios.

Client interest rate
As part of the simulation of a margin, a client interest rate is modeled. The model consists of a set of sensitivities to market interest rates (M1,t) and moving averages of market interest rates (MA12,t). The sensitivities to the variables show the degree to which the bank has to reflect market movements in its client interest rate, given the profile of its savings clients.

The model chosen for the interest rate for the point in time t (CRt) is as follows:

Up to a certain degree, the model is representative of the savings interest rates offered by (retail) banks.

Investment strategies
The investment rules are formulated so that the target maturity profiles of the two strategies are identical. This maturity profile is then determined so that the same sensitivities to the variables apply as for the client rate model. An overview of the investment strategies is given in Table 1.

The replication process is simulated for 200 successive months in each scenario. The starting point for the investment portfolio under both strategies is the target maturity profile, whereby all investments are priced using a constant historical (normal) yield curve. In each scenario, upward and downward shocks lasting 12 months are applied to the savings volume and the yield curve after 24 months.

Example scenario

The results of an example scenario are presented in order to show the dynamics of both investment strategies. This example scenario is shown in Figure 2. The results in terms of duration and margin are shown in Figure 3.

As one would expect, the duration for the portfolio investment strategy remains the same over the entire simulation. For the marginal investment strategy, we see a sharp decline in the duration during the ‘shock period’ for volume, after which a double wave motion develops on the duration. In short, this is caused by the initial (marginal) allocation during the ‘stress’ and subsequent cycles of reinvesting it.

With an upward volume shock, the margin for the portfolio strategy declines because the increase in savings volume is invested at downward shocked market interest rates. After the shock period, the declining investment return and client rate converge. For the marginal strategy this effect also applies and in addition the duration effects feed into the margin development.

Scenario spectrum
In the scenario analysis the standard deviation of the margin series, also known as the margin volatility, serves as a proxy for margin stability. The results in terms of margin stability for the full range of market interest rate and volume scenarios are summarized in Figure 4.

Figure 4: Margin volatility of marginal (left-hand figure) and portfolio strategy (right-hand figure) for upward (above) and downward (below) volume shocks.

From the figures, it can be seen that the margin of the marginal investment strategy has greater sensitivity to volume and interest rate shocks. Under these scenarios the margin volatility is on average 2.3 times higher, with the factor ranging between 1.5 and 4.5. In general, for both strategies, the margin volatility is greatest under negative interest-rate shocks combined with upward or downward volume shocks.

Replication in practice

The scenario analysis shows that the portfolio strategy has a number of advantages over the marginal strategy. First of all, the maturity profile remains constant at all times and equal to the modeled maturity of the savings deposits. Under the marginal strategy, the interest rate typical maturity can vary from it over long periods, even when there are no changes in market interest environment or behavior in the savings portfolio.

Secondly, the development of the margin is more stable under volume and interest rate shocks. The margin volatility under the marginal investment strategy is actually at least one and a half times higher under the chosen scenarios.

An intuitive process
These benefits might, however, come at the expense of a number of qualitative aspects that may form an important consideration when it comes to implementation. Firstly, the advantage of a constant interest-rate profile for the portfolio strategy, comes at the expense of intuitive combinations of investments. This may be important if these investments form contractual obligations for the transfer of the interest rate risk.

The strategy, namely, requires generating a large number of investments that can even have negative principals in case of a (small) decline of savings volume. Secondly, the shocks in the duration in a marginal strategy might actually be desirable and in line with savings portfolio developments. For example, if due to market or idiosyncratic circumstances there is high inflow of deposit volume, this additional volume may be relatively more interest rate sensitive justifying a shorter duration.

Nevertheless, the example scenario shows that after such a temporary decline a temporary increase will follow for which this justification no longer applies.

The choice

A combination of the two strategies may also be chosen as a compromise solution. This involves the use of a marginal strategy whereby interventions trigger a portfolio strategy at certain times. An intervention policy could be established by means of limits or triggers in the risk governance. Limits can be set for (unjustifiable) deviations from the target duration, whereas interventions can be triggered by material developments in the market or the savings portfolio.

In its choice for the strategy, the bank is well-advised to identify the quantitative and qualitative effects of the strategies. Ultimately, the choice has to be in line with the character of the bank, its savings portfolio and the resulting objective of the process.

1. The profile shown is a summary of the whole maturity profile. In the whole profile, 5.97% of the replicating volume matures in the first month, 2.69% per month in the second to the 12th month, etc.
2. Note that this is a proxy for the duration based on the weighted average maturity of the target maturity profile.

An extended version of this article is published in our Savings Special. Would you like to read it? Please send an e-mail to marketing@zanders.eu.

More articles about ‘The modeling of savings’:

• Why is modeling essential
• Approaches to modeling liquidity maturity
• The freedom of variable savings

Solvency II aims to unify the EU insurance market and will come into effect on January 1st 2016. The technical specifications published by EIOPA will be used for interim reporting during 2015.

Although the specifications are not yet finalized, it is unlikely that they will change extensively. The technical specifications consist of two parts; part one focuses on the valuation and calculation of the capital requirements and part two focuses on the long-term guarantee (LTG) package. The LTG package was agreed upon in November 2013 and has been one of the key areas of debate in the Solvency II legislation.

Artificial volatility

The LTG package consists of regulatory measures to ensure that short-term market movements are appropriately treated with regards to the long-term nature of the insurance business. It aims to prevent ‘artificial’ volatility in the ‘own funds’ of insurers, while still reflecting the market consistent approach of Solvency II. When insurance companies invest long-term in fixed income markets, they are exposed to credit spread fluctuations not related to an increased probability of default of the counterparty.

These fluctuations impact the market value of the assets and own funds, but not the return of the investments itself as they are held to maturity. The LTG package consists of three options for insurers to deal with this so-called ‘artificial’ volatility: the Volatility Adjustment, the Matching Adjustment and transitional measures.

Figure 1

The transitional measures allow insurers to move smoothly from Solvency I to Solvency II and apply to the risk-free curve and technical provisions. However, the most interesting measures are the Volatility Adjustment and the Matching Adjustment. The impact of both measures is difficult to assess and it is a strategic choice which measure should be applied.

Both try to prevent fluctuations in the own funds due to artificial volatility, yet their requirements and use are rather different. To find out more about these differences, we immersed ourselves into the impact of the Volatility Adjustment and the Matching Adjustment.

The Volatility Adjustment

The Volatility Adjustment (VA) is a constant addition to the risk-free curve, which used to calculate the Ultimate Forward Rate (UFR). It is designed to protect insurers with long-term liabilities from the impact of volatility on the insurers’ solvency position. The VA is based on a risk-corrected spread on the assets in a reference portfolio. It is defined as the spread between the interest rate of the assets in the reference portfolio and the corresponding risk-free rate, minus the fundamental spread (which represents default or downgrade risk).

The VA is provided and updated by EIOPA and can differ for each major currency and country. The VA is added to the liquid part of the risk-free zero-coupon rates, i.e. until the so-called Last Liquid Point (LLP). After the LLP, the curve converges to the UFR. The resulting rates are used to produce the relevant risk-free curve.

The Matching Adjustment

The Matching Adjustment (MA) is a parallel shift applied to the entire basic risk-free term structure and serves the same purpose as the VA. The MA is calculated based on the match between the insurers’ assets and the liabilities. The MA is corrected for the fundamental spread. Note that, although the MA is usually higher than the VA, the MA can possibly become negative. The MA can only be applied to a portfolio of life insurance obligations with an assigned portfolio of assets that covers the best estimate of the liabilities.

The mismatch between the cash flows of the assets and the cash flows of the liabilities must not be a material risk in relation to the risks inherent to the insurance business. These portfolios need to be identified, organized and managed separately from other activities of the insurers. Furthermore, the assigned portfolio of assets cannot be used to cover losses arising from other activities of the insurers.

The more of these portfolios are created for an insurance company, the less diversification benefits are possible. Therefore, the MA does not necessarily lead to an overall benefit.

Differences between VA and MA

The main difference between the VA and the MA is that the VA is provided by EIOPA and based on a reference portfolio, while the MA is based on a portfolio of the insurance company.

Other differences include:

• The VA is applied until the LLP, after which the curve converges to the UFR, while the MA is a parallel shift of the whole risk-free curve;
• The MA can only be applied to specifically identified portfolios;
• The VA can be used together with the transitional measures in the preparatory phase, the MA cannot;
• The MA has to be taken into account for the calculation of the Solvency Capital Requirement (SCR) for spread risk. The VA does not respond to SCR shocks for spread risks.

Figure 2: Graphical representations of balance sheets. The blue box represents the assets, the red box the liabilities, and the green box the available capital.

The impact of the VA and MA is twofold. Both adjustments have a direct impact on the available capital and next to this, the MA impacts the SCR. As a result, the level of free capital is affected as well. While the exact impact of the adjustments depends on firm-specific aspects (e.g. cash flows, the asset mix), an indication of the effects on available capital as well as the SCR is given in Figure 2. Please note that this is an example in which all numbers are fictitious and used merely for illustrative purposes.

Impact on available capital

Both the VA and the MA are an addition to the curve used to discount the liabilities, and will therefore lead to an increase in the available capital. The left chart in Figure 2 shows the Base scenario, without adjustment to the risk-free curve. Implementing the VA reduces the market value of the liabilities, but has no effect on the assets. As a result, the available capital increases, which can be seen in the middle chart.

A similar but larger effect can be seen in the right chart, which displays the outcome of the MA. The larger effect on the available capital after the MA compared to the VA is due to two components.

1. The MA is usually higher than the VA, and
2. the MA is applied to the whole curve.

Impact on the SCR

The calculation of the total SCR, using the Standard Formula, depends on several marginal SCRs. These marginal SCRs all represent a change in an associated risk factor (e.g. spread shocks, curve shifts), and can be seen as the decrease in available capital after an adverse scenario occurs. The risk factors can have an impact on assets, liabilities and available capital, and therefore on the required capital.

Take for example the marginal SCR for spread risk. A spread shock will have a direct, and equal, negative impact on the assets for each scenario. However, since a change in the assets has an impact on the level of the MA, the liabilities are impacted too when the MA is applied. The two left charts in Figure 3 show the results of an increase in the spread, where, by applying the spread shock, the available capital decreases by the same amount (denoted by the striped boxes).

Figure 3: Graphical representations of balance sheets after a positive spread shock. The lined boxes represent a decrease of the corresponding balance sheet item. Note that, in the MA case, the liabilities decrease (striped red box) due to an increase of the MA.

Hence, the marginal SCR for the spread shock will be equal for the Base case and the VA case. The right chart displays an equal effect on the assets. However, the decrease of the assets results in an increase of the MA. Therefore, the liabilities decrease in value too. Consequently, the available capital is reduced to a lesser extent compared to the Base or VA case.

The marginal SCR example for a spread shock clearly shows the difference in impact on the marginal SCR between the MA on the one hand, and the VA and Base case on the other hand. When looking at marginal SCRs driven by other risk factors, a similar effect will occur. Note that the total SCR is based on the marginal SCRs, including diversification effects. Therefore, the impact on the total SCR differs from the sum of the impacts on the marginal SCRs.

Impact on free capital

The impact on the level of free capital also becomes clear in Figure 3. Note that the level of free capital is calculated as available capital minus required capital. It follows directly that the application of either the VA or the MA will result in a higher level of free capital compared to the Base case. Both adjustments initially result in a higher level of available capital.

In addition, the MA may lead to a decrease in the SCR which has an extra positive impact on the free capital. The level of free capital is represented by the solid green boxes in Figure 3. This figure shows that the highest level of free capital is obtained for the MA, followed by the VA and the Base case respectively.

Conclusion

Our example shows that both the VA and the MA have a positive effect on the available capital. Apart from its restrictions and difficulties of the implementation, the MA leads to the greatest benefits in terms of available and free capital.

In addition, applying the MA could lead to a reduction of the SCR. However, the specific portfolio requirements, practical difficulties, lower diversification effects and the possibility of having a negative MA, could offset these benefits.

Besides this, the MA cannot be used in combination with the transitional measures. In order to assess the impact of both measures on the regulatory solvency position for an insurance company, an in-depth investigation is required where all firm specific characteristics are taken into account.

The introduction of the UFR was an attempt to address three problems. Firstly, as interest rates currently stand, applying the UFR has the effect of increasing rates with a maturity of 20 years or more (see figure 1). This causes the present value of long-term liabilities to fall, which means funding ratios and capital ratios rise. Secondly, the interest rate market for long maturities is assumed to be insufficiently liquid to permit a reliable market valuation, which means the value of liabilities may be very volatile.

Figure 1: Spot yield curve with UFR (red) and without UFR (blue) as of September 30, 2013

The third problem addressed by the UFR is the desire to escape the vicious circle which is created when interest rate risks are hedged. Due to demand among pension funds and insurers for swaps with long maturities, these interest rates are falling, necessitating further interest rate hedging and triggering a renewed rise in demand.

Risk management

The UFR, however, is raising questions about risk management by insurers and pension funds, who are required to use the UFR when valuing their liabilities in their regulatory reports. From a risk management perspective, however, there are important arguments against hedging interest rate risks on the basis of the UFR.

The UFR is not an economic reality: there are no instruments on the market which generate the same returns as the UFR-adjusted interest rates. Consequently, there is an imbalance between the value as reported to DNB and the available instruments on the market for managing the risks. Furthermore, the UFR is only applied to the liabilities on the balance sheet, and not to the assets. This creates a discrepancy between the economic reality of the assets and the ‘paper’ UFR reality of the liabilities. If a company’s assets and liabilities have identical interest rate profiles, the company does not run an interest rate risk; nonetheless, its UFR-based funding ratio does change in line with interest rate movements on the market. There is also greater interest rate sensitivity around the 20-year interest rate point: past this point, market interest rates are partially or entirely disregarded. Lastly, there is a political risk (which cannot be hedged) that the UFR method may be revised by the regulator – a fact underlined by recent developments.

Insurers and pension funds are compelled to keep two different sets of records: a ‘UFR report’ for the regulator and an economic version on which the interest rate risk is actually managed. Both records have their own, specific risks.

Insurers: debate and uncertainty

Understandably, the UFR has created quite a furor among insurers. In June 2013, EIOPA published the results of a survey of insurers who offer long-term guarantee products. Interestingly, EIOPA acknowledges in this publication that the UFR entails significant risks. Potentially, the UFR could mislead regulators, meaning that any action is taken too late. Moreover, the design of the UFR – specifically, the speed at which the forward rate converges towards the UFR – has long been a source of uncertainty. EIOPA advises using what is known as the ‘20+40’ convergence (whereby market interest rates are used up to and including 20 years and, 40 years later, the forward rate has converged to the UFR). Both insurers and the European Parliament, however, are pressing for a switch to a ‘20+10’ convergence.

Proponents of this shorter convergence period point to the lower sensitivity to shocks in (long-term) market interest rates, which would help stabilize the valuation of liabilities. One drawback of a short convergence period is the increased volatility of own funds. This is because the assets are discounted at market interest rates and are sensitive to changes in interest rates, whereas the liabilities are not. Moreover, the potential impact of a change in the level of the UFR is greater when the convergence period is shorter.

While the debate continues among European insurers, DNB has already compelled Dutch insurers to use the UFR. In so doing, DNB is largely taking its cue from EIOPA’s latest advice. However, there is a high risk that the convergence period will change in the definitive Solvency II legislation, meaning that, eventually, insurers will have to switch to a different UFR.

Pensions: DNB is pursuing its own course

The UFR committee
In October 2013, the UFR committee advised the Dutch cabinet to abandon the current method for pension funds, which involves a fixed UFR of 4.2%. The committee advises using the UFR as an ultimate rate, based on the average forward rates of the last 120 months, with an infinite convergence period.

The UFR will then become a moving target based on current market rates. As things currently stand, this would mean a UFR of 3.9% – which is significantly different to the current UFR.

The cabinet informed the UFR committee in a response that the recommendation of applying a moving target UFR will be implemented from 2015 onwards. This will only accentuate the contrast between Europe and the Dutch pension landscape. In addition to an economic report and the current UFR report, it will compel pension funds to also prepare an adjusted UFR report for 2015.

The situation as regards pension funds illustrates the political risk. Following criticism in Dutch academic circles about the high sensitivity affecting the 20-year forward rate, DNB adapted the rules specifically for pension funds. These funds must now continue applying the forward rate past the 20-year point (with fixed weightings) and the spot rates are averaged over the last three months.

Since then, in its advisory report, the UFR committee has proposed a completely new calculation method (see insert), which may have a big impact on funding ratios. It is not inconceivable that, if the yield curve fluctuates significantly, the UFR will yet again be changed. In addition, there are also long-term risks to be taken into account. The UFR could potentially create discrepancies between the pension entitlements of current and future pensioners.

The higher funding ratio resulting from the application of the UFR reduces the likelihood of increases in contributions and cuts to pensions at the present time – which is an advantage for current pensioners. If, however, the yield turns out lower than assumed, future pensioners will have fewer funds at their disposal. Potentially, therefore, pension rights may end up being transferred from younger to older generations.

Conclusion

The EIOPA study and the UFR committee illustrate that the introduction of the UFR has made the world of insurance more complex. In risk management terms, it has created two landscapes and it is not yet clear exactly what the UFR landscape will look like. From an economic perspective, the majority of risk managers will give priority to hedging risks. To prevent interference by the regulator, however, the UFR value must always be closely monitored. Furthermore, the impending change to the UFR method for pension funds reaffirms that the political risk is a significant, unmanageable factor.