Dynamic financial analysis (DFA) is a simulation approach that looks at an insurance enterprise's risks holistically as opposed to traditional actuarial analysis, which analyzes risks individually. Specifically, DFA reveals the dependencies of hazards and their impacts on the insurance company's financial well being such as business mix, reinsurance, asset allocation, profitability, solvency, and compliance.
In addition to projecting stochastic future economic scenarios through using scenario generators such as interest rate, underwriting cycle and jurisdictional risk models, DFA also links the scenarios with the financial models of the targeted insurance company that is being analyzed. Such models not only reveal the operation and the business structure of the company, but also uncover the dependencies among its business practices. Because DFA tries to account for every aspect of the company, it produces a vast amount of data. As a result, analyzing and presenting the outputs effectively is of great importance.
DFA is used primarily by financial practitioners to manage profitability and financial stability (the risk control function of DFA) [1] Not only do DFA users seek to maximize shareholder values, but they also try to maintain customer values. Furthermore, outputs from DFA could help managers identify strengths and weaknesses of the following areas. [2]
DFA consists of the following 3 parts: [2]
Careful calibration is required to ensure the accuracy of the scenarios and the correlations among business models.
The scenario generator must meet the following criteria: [2]
The interest rate generator is the core fundamental of DFA. Many sophisticated interest rate models were created in the effort to best imitate the real world interest rate behavior. Although none of the existing models are perfect, they have their own advantages and disadvantages. The following is a simple interest rate model used in a publicly access DFA model. [3]
The CIR interest rate model characterizes the short-term interest rate as a mean-reverting stochastic projection. Although CIR was first used to project continuous changes in the interest rates, it is also acceptable to use it to project discrete changes from one time period to another. Below is the formula.
where [3]
The CIR model has two components: a deterministic and a stochastic part. The deterministic part will go in the reverse direction of what the current short term rate is heading. In other words, the further the current interest rate is from the long term expected rate, the harder the deterministic part tries to reverse it back to the long term mean.
The stochastic part is purely random; it can either help the current interest rate deviate from its long term mean or the reverse. Because this part is multiplied by the square root of the current interest rate, when the current interest rate is low, its impact is minimum, thus leading to the unlikelihood of the interest rate going below zero, and the interest rate cannot be negative. However, the reverse is true if the current rate is high.
In the United States, each state has its own regulatory, jurisdictional and legislative bodies, and there are advantages and disadvantages for an insurance company conducting businesses in different states. For example, some states have restrictions on how much rate increase that an insurance company can charge for the risks on which it takes. Such risk can severely hamper the insurance entity's profitability and operation.
In DFA, jurisdiction risk is reflected in two ways. [3]
The number of policies an insurance company can sell depends on the macroeconomic environment of the insurance industry. The DFA scenario accounts for this risk factor to best simulate the nature of insurance business.
Below are four underwriting cycles that an insurance company may experience.
To estimate the impacts that the scenarios have on an insurance company, the company's business practices needed to be quantified and linked to the scenario factors such as interest rate and underwriting cycles.
Types of Models
Without effective analysis and presentation, managers can hardly make any sense out of the vast amount of data that DFA produces. The goal of DFA is to help the managers to find out whether the company's current strategies are in line with its financial goals. Below are some tricks of conducting and presenting DFA analysis. [4]
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