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By DR. KIM G. BALLS, vice president of life product development and head of development for life insurance and investment modeling technology at DFA Capital Management Inc.

At the core of financial risk analysis are systems that enable companies to model possible future economic and financial scenarios, along with their reaction to those external events. These systems help them gain insight into the financial risks that could damage their firms.

The traditional risk management system consists of two main components: an economic scenario generator, a tool that is used to model the economy, financial markets and other external influences; and a stochastic modeling tool, which applies the economic scenarios to the company's risk exposures to simulate results of financial decisions and evaluate performance and risk metrics based on the output.

How these two components work, individually and together, critically affects the reliability of the model outputs and whether they truly support better risk decision-making. Here's how to gauge whether your system is up to snuff.

ECONOMIC SCENARIO GENERATORS

ESGs are a suite of models that jointly produce a simulation of the future economy and financial markets. The most important elements within a system are equity and interest rate models. The best models incorporate stochastic volatility and include multiple sources and types of random shocks.

These make it possible to reproduce the kinds of extreme scenarios observed in the real markets and are critical to assessing the risk in investment portfolios and uncovering weaknesses in hedging strategies.

An ESG should always be looked at as a whole. Can it properly model the correlations between different components, such as the full-term structure of interest rates and multiple equity indices? Can you use the ESG to model a rich set of asset classes, including derivatives and other complex instruments?

The ideal ESGs model defaultable securities through both systemic and idiosyncratic components of risk, at security level, ensuring that simulations will reflect accumulation and concentration risk. This means that they should be able to faithfully reproduce rating transition dynamics, default rates and the interaction between equity markets, along with credit spreads by rating class.

Given the importance of granularity in producing accurate risk models, it is surprising that many insurance companies take "safe harbor" in economic scenarios that are provided by regulators or parent companies.

While these scenarios may provide an adequate measure of aggregate risk exposure, they do not model the individual securities held and should not be used for risk management. For example, insurers using these safe harbor scenarios in the recent market downturns with extremely high volatility have significantly understated the value of implied guarantees for variable annuity guaranteed minimum benefits. This may be distorting financial reporting and capital adequacy of come insurers.

STOCHASTIC MODELING TOOLS

Insurers can take the output from the ESG models--projections of the economy and financial markets external to a company--and best apply them to their company's risk exposures through a stochastic simulation tool that faithfully represents the risk management practices of the firm. A whole-company model supports aggregation of data within complex business structures.

Corporate risk managers today are demanding many of the same key features in a stochastic modeling tool requested by industry regulators. These include the ability to:

--model all business segments on the same platform

--model interdependencies between business segments

--model dynamic management behavior

--store output at detailed level, allowing full drill-down analysis of results

--view the model in all its transparency while discouraging or preventing internal code manipulation

--view the company at the level of the enterprise or the individual line

--present output in both economic (fair value) and accounting-based views

For the insurance business that has significant embedded market risk, such as variable annuities or products with benefits tied to long-term inflation, the use of an integrated risk platform helps to mitigate common problems faced in these sectors.

For example, an integrated platform uses the same methodologies and controls for daily management of the business and regulatory reporting. All uses of the platform are driven by a common set of assumptions and are stored in a common data repository. Additionally, all uses of the platform should be based on the same high-quality ESG, which facilitates focusing on the full set of risks in all risk management activities.

BEYOND MODELING

When it comes to choosing the right risk management system, the modeling tool is only one piece of the puzzle. Another important component is ensuring that the corporate culture avoids the typical silo approach to running a business--essential to a successful implementation.

Enterprisewide risk management and business performance management work hand-in-hand, along with implementation of the right tools.

If this approach is taken, companies will indeed be better prepared as they enter 2009, especially in terms of minimizing unnecessary risk and maximizing the prospects for long-term success.

January 19, 2009

Copyright 2009© LRP Publications