Peer Analytics’ consultants have been assisting clients in the asset allocation process for over thirty years. Together we have conducted over 250 asset/liability studies and developed asset allocation models for virtually every type of investment organization.
Michael Kantor and Dave Newsom developed the first stochastic asset allocation model for insurers in 1989.
We provide both strategic asset allocation consulting and license our cloud-based, user-friendly, transparent DFA/ALM models.
Dynamic Financial Analysis (DFA) or Asset Liability Modeling (ALM) is a stochastic simulation approach to quantifying multiple types of risk (surplus, net income, risk-based-capital requirements, etc.) by simulating company financial results thousands of times for each asset mix, so that all potential outcomes associated with individual asset mixes can be considered in advance. The analysis embodies a complete insurance company financial model, and considers all of the interrelationships between the asset and liability sides of the business. The model provides both an expected value and a distribution of possible values for each of the parameters evaluated. The approach allows clients to evaluate the relationships among multiple dimensions of risk: asset risk, underwriting risk, reinsurance risk and business risk.
The approach takes into account all of the variables which affect the financial results of the company. By simulating investment, underwriting, and premium growth results we are able to assign probabilities and provide ranges of potential outcomes given changes in variables. This provides decision makers with an analysis that evaluates the interrelationship among all the various risks that the company faces, rather than simply considering investment risk in isolation.
The analysis offers an integrated perspective of risks, rather than the classic financial analysis in which different aspects of the company were considered in isolation from each other. Specifically, DFA / ALM models the reactions of the company in response to a large number of interrelated risk factors, including both underwriting risks – detailed by lines of business – as well as asset risk and leverage.
The models simulate inflation, real interest rates, the shape of the Treasury yield curve, credit spread levels and their corresponding volatility, stock and bond market returns, the impact of reinsurance, costs to settle liability claims, and premium growth rates.
Interest rates and yield curve shape changes are modeled using the Cox, Ingersoll, Ross methodology which enforces necessary lognormality and mean-reversion. Yields and credit spreads, both absolute level and changes, drive bond returns, which in turn impact the various equity class returns.
These fundamental asset return drivers are combined with corresponding asset and liability cash flows and simulated 100 thousand times to develop net cash flow patterns for the company. All of this is done within a framework facilitating the generation of financial statements over the projected period.
The following liability assumptions can be made using statutory Schedule P as a starting point and adjusted as desired.
Premiums are defined as the product of Rate Change, Exposure Change, and Trend draws from separate uniform distributions. A Trend factor provides for a worst-case possibility that rate changes develop a downward trend. Users can define a Percent of Worst assumption as well as a distribution minimum and maximum for Trend.
Losses are defined as the product of a Severity (lognormal) and a Frequency draw (Poisson). Loss frequency rates may include a user assumed potential tendency to Spiral.
LAE are defined as the product of a Severity draw (lognormal) and a Frequency draw (Poisson). LAE frequency rates may include a potential trend.
ULAE increase with Inflation draw plus any user defined adjustment.
Our liability modeling approach was designed by actuaries and vetted by both property casualty and health actuaries as well as an independent actuarial consulting firm. Our models are intended to be transparent and easily understood and vetted by client staff.
Capital market assumptions (risk, return, yield, duration and correlations) for individual asset classes and for inflation may be supplied by the client, third party advisors, or by Peer Analytics. Peer Analytics assumptions are based on a combination of Capital Market Theory, historical relationships across asset classes, and current market conditions. Our capital market assumptions are intended to be both tactically neutral and conservative. We typically model one or two alternative sets of capital market assumptions to depict the sensitivity of results to assumptions.
Liability assumptions for premium growth, loss ratios, reserve payout patterns and reinsurance strategy by line of business are made either by Peer Analytics based on review of historical statutory data or in conjunction with the client actuary. Further details are here.
Risk/reward relationships for a range of potential asset mixes are considered beginning with the client’s current asset mix as well as a range of alternative asset mixes.
Finally, we include a DFA/ALM Peer Company Risk Analysis (the same stochastic analysis of individual peer companies) to describe the client’s asset, liability and surplus, net income, and Risk Based Capital risk trade-offs in context with those same risk positions of individual peer companies.