Artigo_alm

INTRODUCTION TO DYNAMIC FINANCIAL ANALYSIS

ROGER KAUFMANN, ANDREAS GADMER, AND RALF KLETT

Abstract. In the last few years we have witnessed growing interest in Dynamic Financial

Analysis (DFA) in the nonlife insurance industry. DFA combines many economic

and mathematical concepts and methods. It is almost impossible to identify and describe

a unique DFA methodology. There are some DFA software products for nonlife

companies available in the market, each of them relying on its own approach to DFA.

Our goal is to give an introduction into this field by presenting a model framework comprising

those components many DFA models have in common. By explicit reference to

mathematical language we introduce an up-and-running model that can easily be implemented

and adjusted to individual needs. An application of this model is presented

as well.

1. What is DFA

1.1. Background. In the last few years, nonlife insurance corporations in the US, Canada

and also in Europe have experienced, among other things, pricing cycles accompanied by

volatile insurance profits and increasing catastrophe losses contrasted by well performing

capital markets, which gave rise to higher realized capital gains. These developments

impacted shareholder value as well as the solvency position of many nonlife companies.

One of the key strategic objectives of a joint stock company is to satisfy its owners by

increasing shareholder value over time. In order to achieve this goal it is necessary to

get an understanding of the economic factors driving shareholder value and the cost of

capital. This does not only include identifying the factors but investigating their random

nature and interrelations to be able to quantify earnings volatility. Once this has been

done various business strategies can be tested in respect of meeting company objectives.

There are two primary techniques in use today to analyze financial effects of different

entrepreneurial strategies for nonlife insurance companies over a specific time horizon.

The first one – scenario testing – projects business results under selected deterministic

scenarios into the future. Results based on such a scenario are valid only for this specific

scenario. Therefore, results obtained by scenario testing are useful only insofar as

the scenario was correct. Risks associated with a specific scenario can only roughly be

quantified. A technique overcoming this flaw is stochastic simulation, which is known

as Dynamic Financial Analysis (DFA) when applied to financial cash flow modelling of

a (nonlife) insurance company. Thousands of different scenarios are generated stochastically

allowing for the full probability distribution of important output variables, like

surplus, written premiums or loss ratios.

Date: April 26, 2001.

Key words and phrases. Nonlife insurance, Dynamic Financial Analysis, Asset/Liability Management,

stochastic simulation, business strategy, efficient frontier, solvency testing, interest rate models, claims,

reinsurance, underwriting cycles, payment patterns.

The article is partially based on a diploma thesis written in cooperation with Zurich Financial Services.

Further research of the first author was supported by Credit Suisse Group, Swiss Re and UBS AG through

RiskLab, Switzerland.

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2 R. KAUFMANN, A. GADMER, AND R. KLETT

1.2. Fixing the Time Period. The first step to compare different strategies is to fix

a time horizon they should apply to. On the one hand we would like to model over as

long a time period as possible in order to see the long-term effects of a chosen strategy.

In particular, effects concerning long-tail business only appear after some years and can

hardly be recognized in the first few years. On the other hand, simulated values become

more unreliable the longer the projection period, due to accumulation of process and

parameter risko ver time. A projection period of five to ten years seems to be a reasonable

choice.