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 methodology which quantifies multiple forms of risk 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 the 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 risks.
The model simulates inflation, interest rates and 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 cash flow patterns and simulated thousands of 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 specified pro forma period.
The following liability assumptions can be made using statutory Schedule P as a starting point and adjusted/changed 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.
The model reflects the full financial structure of the sample company, including the impact of regulatory and tax structures, which allows projections to be made for the balance sheet and for the profit-and-loss account of the client company.
Capital market assumptions (risk, return, yield, duration and correlation) for individual asset classes and for inflation are made by Peer Analytics 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 tactically neutral and conservative. We typically model one or two alternative sets of capital market assumptions to depict the sensitivity of results to assumptions. These may be supplied by the client, third party advisors, or by Peer Analytics.
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 defined beginning with the client’s current asset mix as well as alternative more diversified asset mixes.
Finally, we include a DFA analysis of client peer companies (DFA Peer Risk analysis) to describe client’s asset, liability and income/balance sheet risk relative to those same risk positions of individual peer companies.
Clients gain an understanding of the impact of current risk postures on potential future operating results, as well as how a change to investment strategy will impact the range of future surplus, net income and risk-based-capital levels. Clients will be able to communicate a clear and objective rationale for any changes in investment policy.