FINCAD recently hosted a round-table discussion in London. The event offered a forum for key players in insurance, pensions and asset management to discuss the top challenges and trends of today’s liability-driven investment (LDI) landscape.
The event was chaired by Erik Vynckier of Foresters Friendly Society. Speakers included Prasun Mathur of the Phoenix Group, Sumit Mehta of Legal and General, Craig Turnbull of Standard Life Investments, Dick Rae of BMO Global Asset Management, and Anne-Marie Shepherd of Deutsche Asset Management.
LDI is an investment strategy that has been used primarily by pension funds since the late 1990s. Its aim is to decrease pension deficits through effective interest rate and inflation hedging. Below is a summary of key takeaways from our LDI discussion.
Improving Accuracy on Assets and Liabilities
A top trend in LDI today is that a growing number of corporate pensions are looking at insurance buy-out as an ‘end game’ solution, while attempting to reduce market volatility in their short-term plans. This trend is often seen in plans where there is a significant mismatch in plan assets and liabilities. Undoubtedly the combination of these variables and the current low interest rate environment make LDI more challenging. As such, firms across the board are very focused on improving accuracy on both the asset and liability sides of the equation.
The Liquidity Dimension
In a push for yield in today’s low interest rate environment, a key issue for pensions and insurers is the proportion of illiquid assets that can and should be held in asset portfolios to match their liability profiles. Firms must consider the cost of liquid assets vs. illiquid assets that are locked in over time. These may require a significant mark-to-market (MtM) haircut in light of liquidity demands proposed by new regulations.
In certain scenarios insurers may reduce derivatives and invest in real assets, however liquidity and risk adjusted return challenges remain given the overlays that may be required to match their liabilities over time. In other cases, insurers may utilize derivatives to better match Solvency II discount rates. But, in a central clearing world, it remains important to consider all sources of demand for asset liquidity and derivative collateral calls.
The Burden of Regulatory Reporting
Meeting the onerous demands of Basel III and Solvency II remains a challenge for firms across Europe. For example, under Solvency II balance sheet constraints state that if just one asset is incorrectly applied to matching adjustments, it would be considered a breach by the regulator. Thus, for a UK insurer, a detailed justification to the Prudential Regulation Authority (PRA) would be required. The time firms are spending on adapting to new requirements detracts significantly from their focus on business growth.
Increased regulatory oversight also means that firms need to run threshold tests on a monthly basis. The required number crunching is, for many, a significant burden—even just for re-pricing liabilities. Examples of computational analytics required by regulators on Limited Price Index inflation derivatives may include the running of Value at Risk (VaR) or scenarios.
Participants also agreed that the right analytic focus would be on real-world path projections of cash flows, liquidity and defaults, rather than neglecting these aspects in favor of mark-to-market considerations as Solvency II requires. As a result, many firms are adopting a ‘multiple models’ approach, looking at things from both regulatory and economic model perspectives. This can impact the types of business opportunities that firms pursue, as a trade may look advantageous from an economic perspective, but not from a regulatory standpoint.
Nowadays the large amount of data firms are working with, in addition to the sheer number crunching involved in managing investments, is unprecedented. This is a challenge for insurers, pension funds and asset managers alike. There is also a need to reconcile data from different sources and outputs. There are certain tasks where it is absolutely essential to speak the same language, e.g. when calculating DV01 risk ladders and across maturity buckets for hedging mandates. Our participants felt that for complex trades it makes sense to use similar tools across asset owners and asset managers. However, for simple trades this is not as important.
What all this means is that firms are devoting significant time and money on meeting regulatory reporting requirements, rather than innovating quantitative analytics tools that can help them carve out competitive advantage.
To Clear or Not to Clear
Bifurcation between cleared and non-cleared derivatives was another key topic at our roundtable. Availability of the models used by central counterparties (CCPs) in their initial margin and variation margin (IM/VM) calculations poses a significant challenge for firms looking to adapt to new market realities.
One specific challenge in the pre-clearing/bi-lateral trading world is that a level of opacity exists in dealing with individual brokers and ‘dirty’ credit support annexes (CSAs) on complex derivatives transactions. That is, there is not always transparency between the counterparty’s valuations and margin requirements, as this is inherently subjective based on the sell-side cost of capital and funding.
As a result, advanced technology for collateral projection is a requirement for the buy-side. This of course takes into account the need to centrally clear standardized derivatives under regulations such as EMIR.
Discussion participants felt that it would benefit firms to better understand the impact that derivatives and other trading may have on future collateral requirements, and embed that knowledge into their strategic asset allocation.
Value Adjustments for Credit, Funding and “K”apital
Institutions utilizing complex derivatives must recognize the impact of credit valuation adjustments (CVA) on their hedging decisions, and understand their potential future exposures. Under an LDI strategy, firms need to ensure there is enough collateral available in the future (e.g. ten years’ time) to fund against their derivatives book. The right modelling mix may be a combination of value at risk (VaR), stressed scenarios and potential future exposure, among others.
The ability to employ up-to-date collateral discounting analytical frameworks reflecting collateral terms has been shown to impact valuations of portfolios by as much as 10%. This can result in a significant price and risk discrepancy on a large portfolio of swaps. This issue is felt acutely in a multi-currency CSA environment. Third-party systems improve the accuracy of such calculations, helping firms achieve fast ROI.
With the advent of clearing, not only are buy-side firms looking to deploy smarter collateral management processes, but they are also focused on obtaining better pre-trade analytics. Such analytics can help them understand where to place trades for maximum benefit in terms of the collateral they deploy.
For more information on LDI, check out a case study on a top tier UK defined benefit corporate pension scheme, a client of ours that optimized LDI portfolio performance using F3.