cummins & philips (2009)

Capital Adequacy and Insurance Risk-Based Capital Systems
By
J. David Cummins and Richard D. Phillips
October 12, 2009
Please address correspondence to:
J. David Cummins
Temple University
Philadelphia, PA 19122, USA
Phone: 610-520-9792
Fax: 610-520-9790
[email protected]
J. David Cummins
Department of Risk, Insurance,
and Healthcare Management
Temple University
617 Alter Hall
1801 Liacouras Walk
Philadelphia, PA 19122
Richard D. Phillips
Georgia State University
Atlanta, GA 30302, USA
Phone: 404-413-7478
Fax: 404-413-7499
[email protected]
Acknowledgements: The authors are grateful to J. Tyler Leverty of the University of Iowa and
Mary A. Weiss of the National Association of Insurance Commissioners and Temple University
for valuable comments and input regarding capital adequacy and solvency regulation. The authors
are solely responsible for the conclusions of the paper, the opinions expressed therein, and any
errors or admissions.
2
Capital Adequacy and Insurance Risk-Based Capital Systems
1. Introduction
The insurance industry is heavily regulated in every developed economy worldwide, with
regulation focusing primarily on solvency. During the past fifteen years, nearly every major
regulatory jurisdiction has either revised or is considering major revisions in its regulatory system
with respect to solvency surveillance, with an emphasis on introducing risk-based capital
regulation. Risk-based capital (RBC) regulatory systems for insurance were first introduced in
Canada and the U.S. in 1992 and 1994, respectively. Japan introduced its Solvency Margin
Standard in 1996, and Australia adopted a risk-based system in 2001. The United Kingdom
adopted its “enhanced capital assessment framework” in 2004, the Netherlands introduced a new
system in 2006, and Switzerland adopted the Swiss Solvency Test (SST) in 2006. Efforts are
currently underway to harmonize solvency regulation in the European Union (E.U.) with the
implementation of the Solvency II risk-based capital standards anticipated in 2012 to replace the
Solvency I system that was adopted in 2001. In the U.S., the National Association of Insurance
Commissioners (NAIC) announced its Solvency Modernization Initiative in 2008, which will
include a reevaluation of the U.S. RBC system, among other objectives.
The movement towards the adoption of new capital standards for insurance companies has
been motivated by several related factors. In the U.S., the adoption of risk-based capital was
driven by a surge in insurer insolvencies that occurred in the late 1980s and early 1990s, arising
from a liability crisis for property-liability insurers and asset quality problems for life insurers.
European insurers were hard-hit by equity market declines in the early 2000s because they were
more heavily invested in equities than their U.S. counterparts. Although there were few actual
insolvencies in Europe, many insurers were severely weakened, including some of the market
leaders. The financial crisis that began in 2008 and the accompanying general financial market
3
turmoil, affecting both debt and equity securities, reinforced the need for regulators to revise their
solvency surveillance systems. Threats of catastrophic mortality events such as pandemics and,
conversely, long-term improvements in longevity have created concerns about insurer solvency,
along with continuing concerns about the risks of property catastrophes and global warming. Life
insurers also are exposed to insolvency risk due to minimum return guarantees.
The implementation of the Basel II regulatory standards for banks also has given impetus
to the development of comparable standards for insurance. Among other factors, the introduction
of operational risk charges in banking as well as the occurrence of operational risk events in the
insurance industry has brought this risk to the attention of insurance regulators. Economic
integration, deregulation, and globalization also have provided motivating factors, as insurers are
increasingly exposed to cross-border risks. Finally, enhanced understanding of the finance and
economics of insurance markets and dramatic improvements in technical and modeling capabilities
have provided regulators with opportunities to develop improved solvency surveillance systems.
The objective of this paper is to consider the implications of these developments for the
U.S. solvency surveillance system. In particular, we evaluate the U.S. RBC system and compare it
to the E.U.’s Solvency II system and the Swiss Solvency Test (SST). These two systems were
selected for comparison because they are the most recent, are in many respects the world’s most
innovative systems, and are likely to be the most influential systems in the future. The paper
proceeds by reviewing the recent history of insurance insolvencies in the U.S. The discussion then
turns to causes of insolvencies, focusing on triggering events and underlying causes. The three
solvency surveillance systems are then briefly outlined and compared, with an emphasis on the a
critique of the U.S. RBC system and the implications of Solvency II and the SST for potential
future revision of the U.S. system.
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2. U.S. Insurance Insolvencies: Recent History and Underlying Causes
2.1. History of U.S. Insurance Insolvencies
The impairment rate and combined ratio for the U.S. property-casualty insurance industry
are shown in Figure 1 for the time period 1969-2008. The impairment rate is defined as the
number of insolvencies divided by the number of companies in the market at the beginning of the
year, and the combined ratio is the sum of the loss ratio and expense ratio. The impairment rate
has several peaks, reflecting underwriting loss and investment events that adversely affected the
industry. The spike in the impairment rate in the mid-1970s was primarily due to a crisis in the
medical malpractice market, whereas the spike in the mid-1980s was due to the general liability
insurance crisis and falling interest rates. The 1992 spike was caused by Hurricane Andrew, and
the increase in 2001 was due to the World Trade Center terrorist attacks. Finally, the somewhat
smaller increase in 2008 was due to the financial crisis, as well as severe weather events.
The maximum impairment rate was 1.77% in 1984, and the minimum was 0.17% in 2006.
The average impairment rate for the entire period was 0.80%, and the average for the period since
the adoption of risk-based capital was about the same. Hence, there has not been a statistically
significant overall drop in the property-casualty impairment frequency since RBC was adopted. It
is clear from Figure 1 that the combined ratio and impairment rate are highly correlated, and in fact
the bivariate correlation coefficient is 63%.
Hence, impairments are driven primarily by
underwriting events in the property-casualty insurance industry.
The importance of underwriting events is further explored in Figure 2, which graphs the
property-casualty impairment frequency rate and the number of points of the combined ratio
attributable to catastrophes. There are spikes in the impairment frequency rate following Hurricane
Hugo in 1989 and around the time of both Hurricane Andrew in 1992 and the World Trade Center
terrorist attacks in 2001. However, the insolvency rate was relatively low in response to the
5
Northridge earthquake in 1994, and the severe hurricane season of 2005 had virtually no impact on
the impairment rate. The 2005 experience is generally attributable to better risk management on the
part of the insurance industry, reflecting the availability of better risk modeling and exposure
management models. The lower impact in 2005 also reflects in part higher capitalization in the
industry. E.g., the ratios of net premiums written to surplus at the time of Hugo and Andrew were
1.25 and 1.14, respectively, compared with 0.79 in 2005. In fact, the correlation coefficient
between the number of Cat points in the combined ratio and the impairment frequency is 21.6%
from 1977-2004 but only 1.2% (not statistically significant) for the period as a whole. Thus, the
high correlation between the overall combined ratio and the impairment rate does not appear to be
driven primarily by property catastrophes. In fact, the correlation coefficient for 1997-2008
between the non-catastrophe component of the combined ratio and the impairment frequency rate
is 54.5%.
The life-health insurance industry impairment rate is shown in Figure 3 for the period
1976-2008. The maximum impairment rate was 3.1% in 1991, the minimum was 0.19% in 2006,
and the average for the period as a whole was 0.82%. The average after the adoption of RBC was
0.58%, significantly lower than for the period as a whole, although it is not clear that the difference
is primarily or entirely due to RBC. Also shown in Figure 3 is the after-tax profit margin for the
life-health insurance industry. The correlation between the impairment rate and the profit margin
is obviously not as strong as the correlation with the combined ratio for property-casualty insurers.
Nevertheless, the correlation is statistically significant, equal to -21.8% for the period as a whole
and -37.7% if the financial crisis year of 2008 is excluded.
Further analysis of the determinants of insurance insolvencies is provided by consideration
of the triggering events. As discussed further below, these are not necessarily the underlying
causes of insolvencies but rather are immediate triggers that lead to insolvency.
6
The triggering events for property-casualty (P-C) insurers are shown in Figure 4. The
primary triggering event for the P-C insurers is deficient loss reserves/inadequate pricing,
accounting for 38% of the total number of insolvencies. The second leading cause, rapid growth,
is associated with 14% of insolvencies. Alleged fraud, impairment of an affiliate, and catastrophe
losses each accounts for about 8% of insolvencies, while investment problems are the primary
cause of 7% of insolvencies for the P-C industry. The finding with regard to catastrophe losses
reinforces the conclusion that cat losses are important but not a major driver of insolvency in this
industry. Reinsurance failure and significant change in business are each the primary proximate
cause of 4% of the P-C insolvencies. Thus, overall, P-C insurers encounter financial difficulties
primarily due to their insurance underwriting operations rather than investments, reflecting the
generally conservative investment policies in the industry.
The insolvency triggering events for life-health (L-H) insurers are shown in Figure 5.
Inadequate pricing is also the primary triggering cause of insolvencies in the L-H insurance
industry, being the major cause in 27% of the cases. Affiliate problems are the second leading
cause of L-H insolvencies, accounting for 18% of impairments. Rapid growth is the primary
trigger in 15% of the L-H insolvencies. Investment problems are more important for L-H insurers
than for P-C insurers, accounting for 15% of insolvencies. Overall, management problems other
than insurance underwriting are much more important for L-H insurers than for P-C insurers. This
reflects the fact that underwriting risk is relatively unimportant for L-H insurers in comparison
with P-C firms, whereas investment and general management risk are relatively more significant
for the L-H companies. Of course, catastrophic underwriting events such as mortality spikes due
to pandemics are a potential underwriting risk for life insurers.
Claimants against insolvent insurers in the U.S. receive partial or full reimbursement of
their claims from insurance guaranty funds. Like insurance regulation in general, the guaranty
7
fund system operates at the state level, with one (or sometimes more) P-C guaranty fund(s) and
one L-H guaranty fund operating in each state. The guaranty funds obtain money to pay claims by
levying assessments on the solvent insurers operating in the state. Thus, a prime indicator of the
costs of insurance insolvencies is the total amount of guaranty fund assessments.
The P-C guaranty fund assessment history from 1978-2007 is shown in Figure 6, which
shows the overall assessments by year in dollar terms and as a percentage of premiums. The
maximum assessment was $1.3 billion in 2006, reflecting insolvencies resulting from the 2005
hurricane season. The minimum assessment was $18 million in 1980, and the average annual
assessment for the period as a whole was $450 million.
As percentages of premiums, the
maximum assessment was 0.466%, the minimum was 0.018%, and the average was 0.166%.
Putting these numbers in perspective, the average underwriting loss for the period 1978-2007 was
6.3%, such that average assessments accounted for about 2.6% of the underwriting loss. Overall,
therefore, although clearly undesirable, guaranty fund assessments have not been a major source of
losses for P-C insurers. In part, this reflects that fact that most insolvencies have involved
relatively small insurers. Thus, the capacity of the guaranty fund system to deal with large, multistate insolvencies has not been tested.
The L-H guaranty fund assessments are shown in Figure 7 for the period 1988-2007, both
in absolute terms and as percentages of total L-H industry premiums (including life insurance,
health insurance, and annuity premiums). The maximum assessment was $885.0 million in 1991,
and the minimum was $17.5 million in 2003. The average annual assessment for the period as a
whole was $324.5 million. As percentages of total premiums, the maximum assessment was
0.34%, the minimum was 0.0032%, and the average was 0.098%. Assessments in the L-H
industry have been particularly low since 1997, averaging only $90 million per year, less than
0.02% of premiums. By any standard, the L-H assessment experience has not been burdensome,
8
especially during the past decade. It remains to be seen whether intermediate or longer-term effects
of the financial crisis will lead to a surge of L-H insolvencies due to overly aggressive pricing of
variable annuities, minimum rate guarantees, toxic assets, or other problems.
2.2. Underlying Causes of Insurer Insolvencies
The events shown in Figures 4 and 5 are the imminent triggers of insurance insolvencies.
However, in most instance, the underlying causes of insolvencies run much deeper and reflect
flaws in managerial judgment or governance occurring years before the triggering event.1 This
conclusion is based on long-term analysis of the insurance industry by the authors and was also the
conclusion of an important study conducted for the Conference of Insurance Supervisory Services
of the Member States of the European Union (Sharma 2002). The study, generally known as the
Sharma report, after the chair of the committee conducting the research, conducted broad-based
research on the financial risks faced by European insurers, including twenty-one detailed case
studies of insolvencies and “near-misses.”
We can further elucidate the genesis of insurance insolvencies by considering the causal
chain diagrammed in Figure 8. The causal chain begins with the creation of underlying causes or
preconditions. Among the underlying causes identified by the Sharma committee were decision
making problems initiated by management, shareholders, or other external controllers.
The
problems included incompetence, operating outside of their area of expertise, lack of integrity,
conflicting objectives, and weaknesses in the face of inappropriate group decisions.
The
underlying preconditions, including poor managerial decision making, also are likely to create
1
For example, executives of insolvent P-C firms do not arrive at work one day to discover that their loss reserves
have become grossly inadequate overnight. Long-term under-pricing and low quality underwriting are required to
create reserve deficiencies sufficiently large to trigger an impairment. Confirming this view as well as the Sharma
(2002) findings, a study of 35 Canadian property-casualty insurer insolvencies reveals that inadequate reserves and
inadequate pricing are the leading proximate causes of insolvencies but that these involuntary exits can however be
linked back to the “quality and experience of governance/management, internal operational processes and risk
appetite” (Leadbetter and Dibra 2008).
9
intermediate problems. These underlying internal problems tend to lead to inadequate internal
controls and decision-making processes, resulting in inappropriate risk decisions. The underlying
and intermediate causes eventually converge and reach the stage of “critical mass.” The firm thus
becomes vulnerable to external or internal ‘trigger events’ which cause adverse financial outcomes
and, in some cases, losses to policyholders, shareholders, and (in the U.S.) other insurers through
guaranty funds. Essentially, firms infected by underlying managerial and governance problems
become more vulnerable to external triggering events than more competent insurers and hence
more likely to become insolvent. I.e., many triggering events such as underwriting and investment
shocks affect the entire industry, but only a small minority of firms becomes financially impaired.
A more detailed understanding of the causal chain leading to insurer insolvency is provided
by the risk map. A risk map is diagrammed in Figure 9. The map shows that firms in the
insurance industry are subject to both internal and external events that can trigger financial
difficulties. The firms in the industry are generally exposed to the same external triggering causes.
These include general economic fluctuations such as changes in interest rates, stock market
fluctuations, increasing inflation or unemployment rates, and financial crises. Insurers are also
subject to more localized triggering events that may be correlated with general economic events
but have a stronger impact on the insurance industry. These include underwriting cycles, adverse
trends in claim costs, property catastrophes, mortality spikes, and unexpected increases in
longevity rates. Even though these events affect most or all insurers, however, the majority of
firms do not become insolvent.
Hence, insolvencies also are triggered by internal causes,
including poor management, ineffective governance, or bad decisions by owners.
As the risk map shows, the underlying managerial, governance, and ownership defects of
an organization flow directly into the risk appetite decision. Firms with such problems are likely
to take on too much risk or enter into transactions for which risk management or pricing decisions
10
are poorly thought out. There may be a tendency to over-invest in high-yielding risky assets or to
expand too rapidly into unfamiliar lines of business or geographical areas. With globalization,
geographical risk has increased, particularly with cross-border operations, mergers, and
acquisitions. Expansion into new lines, assets, and markets may lead firms to take on risks that are
inaccurately modeled, poorly understood, or inadequately capitalized, as in the case of the credit
default swap operation of American International Group.
Underlying management defects also flow directly into the firm’s operational risk, which is
defined as “the risk of loss resulting from inadequate or failed internal processes, people and
systems, or from external events” (Basel Committee 2003, p.2). A wide range of risks are
encompassed in the operational risk category, including the following major sources of risk (Basel
Committee 2002): (1) Employment practices and workplace safety, e.g., losses arising from acts
inconsistent with employment, health, or safety laws; (2) internal fraud, e.g., losses due to
employee dishonesty such as misappropriation of property; (3) external fraud by a third party that
causes loss to the firm; (4) “clients, products, and business practices,” i.e., losses arising from
unintentional or negligent failure to meet professional obligations to specific clients, from the
design or non-performance of a product, improper trading activities, etc.; (5) business disruptions
and system failures, including computer hardware and software failure; and (6) execution, delivery,
and process management, i.e., losses from failed transaction processing or process management or
from relations with trade counterparties and vendors.
Although all firms are susceptible to
operational risk, firms with serious flaws in their management or governance systems are
particularly vulnerable to potentially catastrophic operational events.
Operational risk errors and inappropriate risk appetite feed into the firm’s risk decisions.
Firms that make erroneous decisions about their risk appetite and commit operational errors are
also likely to make bad decisions about their underwriting, investment, and reinsurance strategies.
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They may fail to accurately appraise their underwriting risk and thereby under-price their policies.
Such firms may also take too much investment risk by over-investing in equities and complex
asset-backed securities. They may also fail to appropriately manage their asset-liability risk and
develop sub-optimal reinsurance strategies, exposing the firm to potentially large losses.
As Figure 9 illustrates, the risk decisions and external triggering events feed into
potentially adverse financial outcomes. The firm’s financial position can be adversely affected by
fluctuations in the market values of its assets or defaults on its debt securities. The firm can suffer
excessive underwriting losses due to claims fluctuations or adverse reserve development, and can
commit reserving errors that understate its liabilities. Operational decisions and poor management
can also lead to a loss of goodwill among employees and customers and to reputational risk that
damages the firm’s position in the market place. These adverse financial outcomes can be
exacerbated if the firm fails to identify, evaluate, and appropriately interpret emerging problems.
The ultimate result of the adverse financial outcomes is the failure of the firm, which imposes costs
on policyholders, investors, and guaranty funds.
The risk map illustrates that regulators need to look beyond the usual evaluation of balance
sheets and financial ratios to conduct a thorough analysis of the firm’s managerial and governance
processes. Thus, while quantitative modeling of insurer risk is clearly an indispensable part of the
regulatory process, qualitative analysis also is required to appraise the firm’s management,
governance, and ownership risks, the firm’s decision making process, and its operational risks.
Once risks are identified, taking prompt corrective action is important to prevent ultimate losses to
policyholders and protect the stability of insurance markets.
3. Risk-Based Capital Systems: Overview and Analysis
This section begins by discussing the evolution of regulatory capital systems. We next
introduce some important statistical-probabilistic concepts in risk-based capital – value at risk
12
(VaR) and tail value at risk (Tail VaR). The section concludes by outlining three important riskbased capital systems – the U.S. risk-based capital (RBC) system, the European Union’s Solvency
II system, and the Swiss Solvency Test (SST) system.
3.1. Evolution of Solvency Capital Systems
The earliest systems used by regulators to define regulatory capital were static and tended
to be volume-based, i.e., such systems tended to consist of a set of fixed factor multipliers that
were applied to statutory balance sheet and income statement items. The multipliers were based on
balance sheet and income statement quantities with no attempt to differentiate asset and liability
classes by risk. The European Solvency I system is a good example of a static, volume based
system, even though it is not very old, having been adopted in 2002. Nevertheless, it is illustrative
of the solvency margin systems that were used historically. In the U.S., the NAIC’s Insurance
Regulatory Information System (IRIS) and the Financial Analysis Solvency Tracking (FAST)
System are examples of static, ratio-based systems, although particularly the FAST system
considers more potential risk indicators than Solvency I.
The Solvency I formula for life insurance illustrates a volume-based capital calculation:
Net Reserves
, 0.85]
Gross Reserves
Net Amount at Risk
+ 0.03* Net Amount at Risk*Max[
, 0.50]
Gross Amount at Risk
Regulatory Capital = 0.04* Reserves*Max[
where the net amounts are net of reinsurance and the amount at risk is the promised death benefit
less the amounts minus the amount of funds held.
There are numerous problems with the static, ratio-based approach to determining
regulatory capital. The Solvency I system, for example, does not consider asset risk and makes no
differentiation among lines of insurance in terms of relative riskiness. There is also no recognition
of market values, even though fluctuations in market values often lead to insurance insolvencies.
13
In addition, there is no recognition of operational risk or catastrophe risk and no testing of capital
robustness under various economic and insurance market scenarios. The system does not evaluate
the quality of management and provides little incentive for firms to practice better risk
management.
Among the first evolutionary steps in insurance regulatory capital systems away from the
static, volume-based approach was the adoption of the U.S. RBC system in 1994. Although the
system is static, it did make an attempt to vary the capital charges according to risk. For example,
the asset charges for bonds are graded according to bond ratings, and the reserve and written
premium charges for property-liability insurers are based on historical worst case scenarios.
Nevertheless, the system remains static and generally does not reflect market values. Further
analysis of the U.S. system is provided below.
The modern phase in the evolution of regulatory capital systems is exemplified by
Solvency II and the Swiss Solvency Test. These systems go beyond the traditional in that they are
dynamic and model based systems that explicitly take into account the underlying probability
distributions of returns on assets, liabilities, and other quantities. They also explicitly incorporate
an evaluation of management quality and provide incentives for the adoption of improved risk
management and modeling systems. These systems are also based on market consistent values of
assets, liabilities, and economic capital. The interaction of market consistent values is portrayed in
Figure 10. Market consistent valuation utilizes true market values where available and discounted
values when market values are not available, as in the case of some assets and most insurance
liabilities. Economic capital is then defined as the difference between the market consistent values
of assets and liabilities, and capital regulation focuses on economic rather than statutory capital.
These new systems are expected to provide much more flexible, adaptable, and accurate
evaluations of insurer capital positions than the traditional systems.
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3.2. Solvency Surveillance: Probabilistic Foundations
This section defines the important concepts of the value at risk (VaR) and tail value at risk
(Tail VaR). VaR plays and important role in Solvency II, whereas Tail VaR provides the basis for
the Swiss Solvency Test. The Tail VaR concept is similar to the expected policyholder deficit
(EPD), which provided the conceptual foundation for the development of the U.S. risk-based
capital system (see Butsic 1994).2
To define the EPD, we consider an insurer with assets equal to A and liabilities equal to L.
A and L are market-consistent values, and both assets and liabilities are stochastic. We assume
that solvency is evaluated at the end of one period. If assets exceed liabilities at the evaluation
date, the insurer is solvent; but if liabilities exceed assets, it has become insolvent. The expected
policyholder deficit is then defined as the expected loss to policyholders in the event of insolvency,
or E[Max(L-A,0)]. To define the EPD more precisely, we define the asset-to-liability ratio x =
A/L, where f(x) is the probability distribution of the ratio (see Cummins 1988). Then the EPD is
defined as follows:
1
EPD = L0 x f ( x) dx
0
where L0 = value of liabilities at the beginning of the evaluation period.
The EPD is conceptually equivalent to the Tail VaR. It is essentially the expected loss in
the event of a default. The VaR, on the other hand, is not an expected value but rather a percentile
of the probability distribution of x. Hence, VaR would be defined as follows:
1−α =
ﾥ
f ( x ) dx
VaR
I.e., the probability of x falling below the VaR is α , where α is a small number such as 0.01 or
0.005, giving a 1% or 0.5% value at risk. Thus, if capital is set equal to the EPD, the insurer has
2
As discussed below, the U.S. RBC system is not a VaR or TailVaR system. However, the designers of the system
did consider the expected policyholder deficit as a conceptual tool during the system’s development.
15
enough capital to withstand the expected loss in the event of default; and if capital is set equal to
VaR, the insurer has sufficient capital such that the probability of default is α . Financial scholars
who have analyzed solvency measures generally prefer Tail VaR to VaR because VaR does not
consider the potential costs in case the insurer defaults, i.e., two insurers could have the same VaR
but their Tail VaR’s could be significantly different (Gatzert and Schmeiser 2008).
The objective of a risk-based capital system based on the EPD is shown in Figure 11,
which reflects the fact that the EPD is a declining function of an insurer’s capital, i.e., other things
equal, insurers with higher capital have lower expected costs of default. In Figure 11, the EPD
curve is plotted as a function of capital for a high risk insurer and a low risk insurer, where the
high risk insurer has riskier assets and/or liabilities than the low risk insurer. Because of the higher
risk, the high risk insurer has a higher EPD for every level of capital than the low risk insurer.
Conceptually, a risk-based capital system attempts to equalize the EPD across insurers in the
industry, such that no insurer imposes higher expected default costs on the system. The risk-based
capital system accomplishes its goal by establishing a maximum permissible EPD, shown as the
horizontal line in Figure 11. Thus, the system would require the risky insurer to hold capital of c 2,
which is greater than the capital of c1 required for the low risk insurer.
The Swiss Solvency Test sets up the insolvency problem slightly differently; and it is
useful to consider their setup, which can also be used to elucidate Solvency II. 3 The Swiss system
also is set up in terms of a one-year solvency evaluation. The concept of risk-bearing capital (RC)
at time t is defined as: RCt = At – Lt, where At and Lt are the market-consistent values of assets and
liabilities at time t. The Swiss system then defines required capital in terms of the change in RC
over a one year period, i.e., using the variable S1, where S1 = e − r RC1 − RC0 , where r = the risk-free
rate. Since RC0 is assumed to be known, the random variable is RC1, which depends on the
3
For more details see Swiss Federal Office of Private Insurance (FOPI) (2006), Luder (2005), and Gatzert and
Schmeiser (2008).
16
stochastic properties of both assets and liabilities. The VaR is then defined as follows:
α=
VaRα
−ﾥ
S1 h( S1 ) dS1
Under Solvency II, target capital is determined using a 99.5% VaR, such that target capital is equal
to - VaR0.005 .4 Under the Swiss system, on the other hand, target capital is based on the expected
shortfall or Tail VaR ( TVaRα ), defined as:
ESα = TVaRα = − E ( S1 | S1 ﾣ VaRα ) = −
0
−ﾥ
S1 h( S1 ) dS1
here h(S1) = the probability distribution of the random variable S1. Or, in words, the target capital
at the VaRα = the expected shortfall of period 1 risk-bearing capital in comparison to beginning
risk-bearing capital. The SST is based on the Tail VaR for a VaR at the 99% level.
The concepts of VaR and Tail VaR are portrayed in Figure 12, which graphs the
probability distribution h(S1), where S1 = the shortfall of end of period risk-bearing capital in
comparison with beginning of period risk-bearing capital. If liabilities exceed assets at time 1, and
if the expected value of the difference wipes out the firm’s beginning capital, then the value of S 1
will be negative. Finding the point on the probability distribution h(S1) such that the probability
that S1 is less than this value with a probability of α yields the α -level VaR or VaRα . The
expected value of the shortfall will be less than VaRα because the shortfall, if it occurs, is unlikely
to be exactly equal to VaRα but will fall to the left of that value in the figure. Thus, for a given
level of α , the Tail VaR is a more conservative standard than the VaR. Even though Solvency II is
based on a 99.5% VaR whereas the SST is based on Tail VaR at the 99% VaR level, the SST could
give higher capital than Solvency II, depending upon the shape of the probability distribution of S1.
4
The VaR has to be multiplied by -1 because S1 will be negative if a shortfall occurs.
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3.3. Solvency Surveillance Systems: An Overview
The systems considered here are the U.S. National Association of Insurance
Commissioners (NAIC) risk-based capital (RBC) system, Solvency II, and the Swiss Solvency
Test (SST). The conceptual foundation for all three systems is the same, i.e., they are geared to the
probability distribution of an insurer’s capital. This linkage is explicit in Solvency II and the SST,
but nevertheless provides the conceptual foundation for the NAIC system as well. Because the
systems are discussed in detail in other sources, our discussion will provide a brief overview for
purposes of providing foundations for an evaluation of the NAIC’s RBC system.
3.3.1. The NAIC Risk-Based Capital System
The NAIC risk-based capital (RBC) system was created to provide a capital adequacy
standard that is related to risk, raises the safety net for insurers, is uniform across states, and
provides regulatory authority for timely action (see NAIC 2009). There is a separate RBC formula
for each of the principal types of insurance: life, property-casualty, and health. The RBC formulas
utilize a “generic formula” approach rather than a deterministic or stochastic modeling approach.
However, the life RBC formula incorporates some modeling elements relating to interest rate risk.
The RBC system has two main components: (1) the risk-based capital formulas, that establish
minimum capital levels for insurers, and (2) a risk-based capital model law that grants automatic
authority to the state regulator to take specific actions based on the level of impairment, determined
by comparing an insurer’s actual capital with its RBC. The second part of the system was deemed
particularly important because regulators previously had difficulty in closing down defaulting
insurers because their actions could be challenged in court. During the solvency crisis of the late
1980s and early 1990s, some regulators also engaged in regulatory forbearance such that defaulting
insurers were allowed to continue to run up deficits, which increased the required guaranty fund
assessments.
18
The NAIC RBC calculations are factor-based rather than model-based, again with some life
insurance elements relating to interest rate risk. An overview of the calculation is provided in Figure
13. RBC is calculated by multiplying risk-factor charges by various balance sheet and income
statement quantities. A covariance adjustment is then applied to yield RBC for each firm.
The components of the RBC formula differ by industry segment. For property-casualty
insurers, the following risk factors are included: (1) R0 - Asset risk for investments in subsidiary
insurance companies; R1 - asset risk for fixed income investment; R2 - asset risk for equity
investments; R3 – asset risk, credit; R4 - underwriting risk relating to reserves; and R5 underwriting risk relating to net written premiums. The risk factors consist of percentages that are
applied to balance sheet and income statement items from the NAIC’s regulatory annual statements.
E.g., the fixed income risk factors become progressively higher for bonds of lower rating quality. For
life insurance, the components are slightly different, with some overlap: C0 - asset risk relating to
affiliates; C1 – credit risk of assets; C2 – insurance risk; C3 – interest rate risk; and C4 – all other
business risk. The health insurance RBC formula consists of the following related components:
H0 – asset risk relating to affiliates; H1 – other asset risk; H2 – underwriting risk; H3 – credit
risk; and H4 – business risk. Thus, the property-casualty insurance RBC places more emphasis
on reserving and underwriting risk, and life insurance is the only category with a separate
charge for interest rate risk. The health insurance RBC places less emphasis on investment risk
because health insurance is a much shorter-tail line of business than property-casualty or life
insurance and hence does not generate invested assets to the same degree as the other two major
lines of business.
After calculating the charges for the various risk factors in the RBC formulas, the next
step is to combine the factor charges to come up with the overall RBC for each insurer. The
designers of RBC recognized that it would not be appropriate simply to add up the charges to
19
obtain overall RBC because it is unlikely that adverse experience would develop for all sources
of risk simultaneously. Rather, it was anticipated that diversification exists among the risk
factors, i.e., that adverse experience with one factor is likely to be offset by favorable experience
with other risk factors. Thus, it was determined that a covariance adjustment should be applied.
Because there was no general agreement on an approach for estimating correlations among the
risk factors, the approach adopted was simply to assume that all risk factors, except the charge
for asset risk relating to affiliates, were statistically independent. This assumption suggests a
square root approach to combining the factors. The square root factors differ somewhat by
industry segment but the property-casualty formula is representative:
RBC = R0 + R12 + R22 + R32 + R42 + R52
Once RBC has been calculated, it is then compared to the company’s total adjusted
capital, which is its total statutory capital and surplus, perhaps adjusted for some other balance
sheet items, such as the mandatory securities valuation reserve in the case of the life insurance
RBC formula. The ratio of adjusted capital to RBC is used to trigger regulatory actions,
according to a hierarchy of action levels. To define the action levels, we define the RBC ratio as
total adjusted capital divided by RBC. The regulatory action levels then are defined as:
RBC Ratio ≥ 2.0
No regulatory action
1.5 ≤ RBC Ratio < 2.0
Company Action Level: The company must file a report
with the regulatory identifying the conditions that led to
violation of the threshold and specifying proposals to
correct the financial problems.
1.0 ≤ RBC Ratio < 1.5
Regulatory Action Level. The company must file an
action plan and the regulator is required to analyze the
company’s operations and issue corrective orders.
0.7 ≤ RBC Ratio < 1.0
Authorized Control Level. The regulator is automatically
authorized to take control of the insurer even if the insurer
is still technically solvent.
20
RBC Ratio < 0.7
Mandatory Control Level. The regulator is required to
take control of the insurer, even if technically solvent.
The existence of the various control levels and the automatic authority granted to insurance
commissioners to enforce the system reflect the attempts to avoid regulatory forbearance and to avoid
having insolvency actions held up in lengthy court challenges.
3.3.2. Solvency II
The European Union’s Solvency II system, scheduled for implementation in 2012 adopts a
three pillars approach to capital regulation, analogous to the Basel II system adopted for the banking
industry (Basel Committee 2001). The three pillars, shown in Figure 14, consist of the following
major components: Pillar 1: quantitative requirements, Pillar 2: supervisory review, and Pillar 3:
market discipline through supervisory reporting and public disclosure. 5 Solvency II takes a holistic
(enterprise) risk management approach rather than considering individual risks separately. Thus, it
places great emphasis on the risk management process and explicitly allows for correlations among
the risks faced by the firm. The system also is designed to implement both quantitative and qualitative
evaluations of insurer solvency. The objectives of Solvency II are to protect policyholders, establish
capital requirements matched to the insurer’s risks, avoid unnecessary complexity, reflect market
developments, and avoid unnecessary overcapitalization.
The system also seeks to establish
regulatory principles that are not excessively prescriptive and also aims to harmonize solvency
regulation across the European Union.
Solvency II is designed to be a “Lamfalussy-style” Framework Directive, i.e., the Framework
Directive focuses mainly on specifying the principles underlying the solvency system. 6 The detailed
5
Solvency II is set forth in European Commission (2008). For further discussion, see also A.M. Best Company
(2008, 2009b), Swiss Re (2006), Eling, et al. (2007), CEIOPS (2008a, 2008b, 2008c), Eling and Holzmuller (2008),
and Elderfield (2009). Vaughan (2009) discusses the implications of Solvency II for U.S. insurance regulation.
6
The Lamfalussy-style process is named for Alexandre Lamfalussy, a European economist and central banker.
Lamfalussy chaired an E.U. commission that adopted a “four level” approach to financial services regulation in
2001. Level 1 involves the establishment of core principles for a financial law, level 2 is the establishment of
specific technical rules, level 3 is inter-country harmonization, and level 4 is compliance and enforcement.
21
technical rules will be put into place by the EC in the form of implementing measures. Hence, the
Directive itself is intended to be principles-based rather than rule-based, although the implementing
measures clearly will involve a wide set of rules. Solvency II is principles-based in the sense that an
insurer has the potential to convince regulators of its financial solidity using an individualized internal
model rather than having the same system or ratios applied to all insurers regardless of their
characteristics, as in the U.S. RBC system or Solvency I.
Pillar 1 of Solvency II establishes two levels of capital requirements: (1) the minimum capital
requirement (MCR), which is the minimum permissible to protect policyholders, and (2) the solvency
capital requirement (SCR), which is the insurer’s regulatory target capital. If the minimum capital
requirement is breached, regulatory action is triggered and authorization for the insurer to operate is
withdrawn.
The system envisions a “ladder of intervention” when capital falls between the MCR
and the SCR. The MCR is likely to be based on a simplified formula or a percentage of the SCR.
There are two major ways that an insurer can determine the SCR: (1) based on its own internal
models, provided they have been approved by the regulator; and (2) the use of a prescribed standard
model, the details of which are yet to be finalized. Solvency II encourages insurers to utilize internal
models. Insurers can also utilize a combination of internal models and the standard model in
determining the SCR. It is envisioned that smaller insurers will be more likely to utilize the standard
model because of the costs and expertise required to develop internal models. The SCR is designed to
target a 99.5% value at risk based on a one-year time horizon, i.e., a once in 200 years ruin
probability. Thus, Solvency II is based on VaR and not Tail VaR.
While Pillar 1 is highly quantitative, Pillar 2 sets forth principles for a qualitative Supervisory
Review Process (SRP), with the objective of evaluating the insurer’s risk management capabilities.
Specifically, the review is designed to consider the processes and reporting procedures established
by insurers and reinsurers to comply with Solvency II as well as the present and future risks the
22
undertaking faces and its ability to assess those risks. The review is expected to consider risk
management systems, adequacy of the insurer’s corporate governance and risk controls, the quality
of personnel systems, etc. In the event that deficiencies are revealed, the regulator has the
authority to order the insurer to take corrective actions and to impose additional capital
requirements, as necessary to protect policyholders.
Pillar 2 of Solvency II also will require insurers to conduct Own Risk Solvency
Assessments (ORSA) designed to regularly assess their overall solvency needs with a view
towards their own risk profile. ORSA is specifically designed to be forward looking and to require
insurers to assess possible future risks. The objectives of ORSA are two-fold: (1) To serve as an
internal assessment process that will be embedded in the firm’s strategic decision making process,
and (2) to provide an additional supervisory tool for regulators. However, ORSA is not designed
to be a “third capital standard.”
Pillar 3 of Solvency II is intended to facilitate market discipline by requiring that insurers
release to the public annual reports providing information on their solvency and financial condition
(EC 2008). Solvency II thus requires insurers to disclose financial information publicly to a far
greater extent than currently. Greater disclosure is expected to bring about market discipline, whereby
market players will be able to exercise greater effective supervision over insurers. Financially sound
insurers thus are expected to be rewarded with greater customer loyalty and lower financing costs.
The reporting is supposed to be coordinated with the evolving International Financial Reporting
Standards (IFRS) being developed by the International Association of Insurance Supervisors (IASB)
(Flamee 2008), although some have argued that the systems may be non-aligned in concept as well as
timing (Duverne and LeDoit 2009).
3.3.3. The Swiss Solvency Test (SST)
The Swiss Solvency Test (SST) is a principles-based stochastic solvency testing model that
23
encompasses market risk, underwriting risk, and credit risk.7 The SST was adopted in 2006. The SST
was developed by the Swiss Federal Office of Private Insurance (FOPI) in cooperation with the Swiss
insurance industry. Although developed independently of Solvency II, one of the objectives of the
SST is to be consistent with Solvency II. The structure of the SST is shown in Figure 15. As shown
in the figure, the SST quantifies market, credit, and insurance risk using a stochastic modeling
approach. The SST provides a standard model for this purpose. However, insurers are encouraged to
develop internal models, which may partially or entirely replace the standard model, contingent on
regulatory approval. The internal model must be integrated into the risk management processes of the
insurer. In fact, internal models are the default option – standard models can be used only if they
adequately quantify the insurer’s risk.
Risks other than market, credit, and underwriting are taken into account through scenario
analysis. Insurers are expected to analyze solvency using a set of standard scenarios defined by the
Supervisory Authority as well as “internal” scenarios specific to each insurer. Scenarios are designed
to cover the effects of events such as financial market crashes, natural disasters, pandemics, and
reinsurer defaults. The results of the risk-modeling exercise and the scenario analysis are integrated
through a prescribed aggregation method that takes into account covariability among risks.
Operational risks are not modeled quantitatively but are taken into account on a qualitative basis.
Thus, the SST consists of two parts: (1) the SST quantitative modeling exercise, which
specifies target capital, and (2) the SST report, which addresses qualitative elements.
An important element of the SST is the requirement for market consistent valuation of both
assets and liabilities. For assets, the market consistent value will be the market price where
available. For those assets that do not have a readily available market price, the market value of a
comparable asset with similar liquidity and other product specific features can be used.
In the
7
More detail information on the SST is provided in Swiss Federal Office of Private Insurance (FOPI) (2006), Luder
(2005), and Eling, et al. (2008).
24
event that the latter is not available, then a modeling approach might be used to find the asset
value. For liabilities, the SST breaks the market consistent value into two parts – the discounted
best estimates and the market value margin. The discounted best estimates are obtained as the
present value of expected future cash flows arising from the liabilities discounted at the risk-free
rate. The market value margin is the estimated adjustment to the discounted best estimate required
to attain the market consistent value of liabilities. The market value margin is approximated by the
cost-of-capital approach. It is intended to be the sum of discounted costs of capital for future
required regulatory capital for the run-off of the portfolio arising from liabilities and assets
replicated to the extent possible. The market value margin is assumed to be the amount an external
entity (such as another insurer, an investor, or a capital provider) would require to assume the
portfolio and run it off. The margin is calculated using a cost of capital equal to the risk-free rate
plus a risk spread. The spread was initially set at 6% (FOPI 2006).
Once the market consistent values of assets and liabilities and the expected shortfall ( ESα )
have been determined, we can define the target capital. The available capital is given by the riskbearing capital (RC), defined as the difference between the market-consistent values of the assets
and the discounted best estimates of the liabilities. The required or target capital equals the
expected shortfall plus the market value margin (MVM). Therefore, the regulatory solvency test is
as follows:
A − L ﾳ ESα + MVM
where A = market consistent value of assets, L = discounted best estimate of liabilities, and MVM
= the market value margin. As mentioned above, the expected shortfall is determined as the Tail
VaR for a 1% value at risk. Of course, if MVM is subtracted from both sides of the equation, the
criterion is that economic capital be at least equal to the expected shortfall.
4. Comparison of RBC, Solvency II, and SST and an Evaluation of RBC
25
In this section, we first provide a comparative analysis of the U.S. risk-based capital (RBC)
system, Solvency II, and the Swiss Solvency Test (SST). We then present information on capital
adequacy in the U.S. and provide an empirical analysis of the U.S. RBC system.
4.1. Comparative Analysis of U.S. RBC, Solvency II, and the Swiss Solvency Test
The U.S. risk-based capital system (RBC), Solvency II, and the Swiss Solvency Test (SST)
are compared in Table 1. The table reveals that Solvency II and the SST have many similarities
and that these systems have many fundamental differences with the U.S. RBC system. Solvency II
and the SST are generally principles-based, with the principles supplemented by technical rules,
especially for the standard models. Solvency II and the SST are based on VaR and Tail VaR,
respectively.
In contrast, the U.S. system is rules-based,
non-stochastic, and factor-based,
although the factors are supposed to be graded by risk. The U.S. system is “one-size fits all,”
whereas Solvency II and the SST allow for significant variability by company through the use of
internal models. Solvency II and the SST are both based on market consistent valuation of assets
and liabilities, while U.S. RBC is adheres to statutory accounting.
The systems also differ in the degree to which they take into account operational risk,
catastrophe risk, and qualitative factors. Solvency II has a quantitative charge for operational risk,
whereas the SST considers operational risk on a qualitative basis. In Solvency II, catastrophe risk is
part of underwriting risk, while the SST considers catastrophe risk as part of its scenario testing
module. Both Pillar 2 of Solvency II and the SST give consideration to risk management, and
Solvency II requires insurers to conduct an Own Risk and Solvency Assessment (ORSA). By
contrast, U.S. RBC does not consider risk management at all. Corporate governance is considered by
both Solvency II and the SST but is not considered in RBC. In terms of public disclosure, only
Solvency II actively promotes disclosure as part of Pillar 3. RBC is supposedly confidential, but riskbased capital and adjusted capital are reported in the statutory annual statements; and, hence,
26
effectively RBC is public. The SST does not require public disclosure.
Most of the differences between Solvency II and the SST, on the one hand, and U.S. RBC, on
the other, represent deficiencies in U.S. RBC. The U.S. system would be vastly improved if it relied
on market values and took a stochastic approach to solvency analysis. In addition, RBC should be
revised to recognize operational risk and catastrophe risk. The U.S. system also should consider the
quality of risk management and corporate governance and should require insurers to conduct ORSA.
As Sharma (2002) has shown, these qualitative factors are critically important determinants of
insolvency risk.
On the other hand, the U.S. system may not be quite as deficient as it seems in terms of
recognizing risk management and governance, in view of the facts that some insurance commissioners
do consider these factors on a qualitative basis and that these elements also are sometimes considered
by the NAIC’s Financial Analysis Working Group. Nevertheless, the system would be improved if
these elements were incorporated on a more formalized and consistent basis. The NAIC should also
begin to require public disclosure of solvency assessments, following the lead of Solvency II.
Although in principle, these results can be obtained from the statutory statements, in practice most
investors and insurance buyers do not have access to this information.
Whether the absence of internal models in the U.S. system is a serious weakness is perhaps
open to debate. The problem with internal models is that they are subject to modeling risk and
managerial moral hazard.
Modeling risk occurs when the modelers make serious errors and
underestimate the risks of certain types of underwriting and investment activities. Moral hazard
occurs when managers manipulate or ignore the models in order to mislead regulators, customers, or
investors and/or take excessive risk to maximize profits. Both modeling mistakes and moral hazard
were among the roots of the financial crisis that began in 2008. E.g., banks either manipulated or
ignored their models in order to operate at unsafe leverage ratios, and the credit default swap (CDS)
27
models developed by American International Group (AIG) convinced management that severe losses
on the CDS portfolio were virtually impossible. Moral hazard and modeling errors also played a role
elsewhere in the financial system and were at the root of the development of many toxic assets
including sub-prime mortgages and collateralized debt obligations (CDOs).
In spite of potential modeling and moral hazard risks, it seems that internal models are the
wave of the future in insurance regulation, and U.S. regulators should begin to consider the
introduction of such models. However, given the potential risks, regulators also need to develop
reliable stochastic regulatory standard models. Such models would provide a check on modeling
errors or managerial moral hazard at the firm level, and departures from the standard model regulatory
capital would need to be thoroughly justified. The NAIC likely will need to establish a Model
Evaluation Office analogous to its current Securities Valuation Office in order to develop sufficient in
house expertise to analyze and vet the internal models.
A qualitative evaluation of the NAIC RBC system, Solvency II, and the SST has been
conducted by Holzmuller (2009). She evaluates the three systems using the conceptual framework for
evaluating risk-based capital systems developed by Cummins, Harrington, and Niehaus (CHN)
(1994). CHN specify seven criteria for policymakers to use in evaluating RBC systems, and
Holzmuller adds four additional criteria. The CHN criteria include focusing on economic values,
establishing the appropriate incentives for management, and appropriately calibrating the formula so
that it is accurate and risk-sensitive. Holzmuller adds some important criteria including adequacy in
economic crises and anticipation of systemic risks. Her analysis reveals “various shortcomings of the
standard used in the United States” and the need for reform of the U.S. system (Hotzmuller 2009,
p.56). In contrast, Solvency II and the SST generally satisfy the criteria quite well.
4.2. U.S. Insurer Capital Adequacy and the Accuracy of RBC
Information on capitalization levels and trends in the U.S. insurance industry is provided in
28
Figure 16, which shows book-value capital-to-asset ratios for property-casualty and life insurers.
For purposes of comparison, the ratios are also shown for the banking sector. As Figure 16 shows,
capital-to-asset ratios are considerably higher for property-casualty insurers than for banks or life
insurers. E.g., in 2008, the ratio was 36.8% for property-casualty insurers, 13.6% for banks, and
5.9% for life insurers.
Although one cannot necessarily infer information about insolvency
probabilities from these ratios, it is clear that the margin for adverse fluctuations is much thinner
for life insurers and banks than for property-casualty insurers. Thus, insurance regulators would
be well advised to carefully scrutinize life insurers in the coming months, particularly in view of
the potential for problems from asset value fluctuations, bond defaults, and minimum rate
guarantees.
Further information on insurer capital adequacy is shown in Figure 17, which graphs the
premiums-to-surplus ratios for life-health and property-casualty insurers from 1986 through 2008.
The figure confirms that life-health insurers have higher leverage than property-casualty insurers.
For the period as a whole, the premiums-to-surplus ratio averaged 2.16 for life insurers and 0.98
for property-casualty insurers. There is a statistically significant downward trend in the ratios for
both life-health and property-casualty insurers, although the trend is stronger for the propertycasualty firms. The ratios increased in 2008, to 0.74 for property-casualty insurers and to 2.18 for
life insurers. This provides further evidence that regulators should be especially vigilant in
monitoring life-health insurer solvency as the financial crisis plays itself out.
It is also interesting to examine recent experience with RBC ratios. The distribution of
ratios across the industry for the period 1997-2007 is shown in Table 2. Panel 1 (panel 2) of the
table shows property-casualty (life) insurers. The results indicate that the vast majority of firms in
both industry segments clearly pass the RBC action level test. In 2007, for example, 97.5% of
property-casualty insurers and 92.9% of life insurers had ratios of adjusted capital-to-risk-based29
capital of 200% or greater. In the same year, only 0.8% of property-casualty insurers and 1.7% of
life insurers had ratios less than 100%, i.e., were in the Authorized Control Level or below. The
low number and percentage of insurers falling below the 200% regulatory threshold indicate that
U.S. RBC is more similar to the minimum capital requirement (MCR) in Solvency II rather than
the solvency capital requirement (SCR).
Another noteworthy finding from Table 2, which
reinforces the results in Figures 16 and 17, is that property-casualty insurers on average are much
more highly capitalized that life insurers. For example, in 2007, the average RBC ratio for
property-casualty insurers was 1271% compared to 406% for life insurers.
Several researchers have investigated the accuracy of the NAIC RBC formula. Cummins,
Harrington, and Klein (1995) test the accuracy of the property-casualty RBC formula in predicting
insolvencies over the period 1989-1993. Based on logistic regression analysis of a large sample of
solvent and insolvent insurers, they find that predictive accuracy is very low when the ratio of RBC to
actual capital is the sole explanatory variable. Accuracy improves significantly when the components
of the RBC formula and firm size and organizational form are used as predictors.
Grace, Harrington, and Klein (GHK) (1998) and Cummins, Grace, and Phillips (CGP) (1999)
compare the accuracy of the NAIC’s Financial Analysis and Solvency Tracking (FAST) system with
RBC. FAST is a system of audit ratios that are used by the NAIC as an early warning system for
insurer insolvencies. GHK use data from 1989-1991 to predict insolvencies over a three-year
prediction horizon, and CGP use data from 1990-1992, also with a three-year prediction horizon.
GHK find that RBC ratios are less effective than FAST scores in identifying financially troubled
property-liability insurers. However, they find that RBC does add a small amount of explanatory
power to the predictions of the FAST scores alone. CGP find that RBC ratios provide very low
explanatory power is predicting insurer insolvencies, that the predictive power of the FAST scores
is significantly greater than for RBC, and that the RBC variables tend to be statistically
30
insignificant when included in predictive models along with the FAST variables. CGP also
investigate cash flow simulation models and find that simulation model results add significant
discriminatory power to the solvency prediction models.
Pottier and Sommer (PS) (2002) investigate the accuracy of four predictive measures of
insurer insolvency – the FAST scores, the A.M. Best Company’s Capital Adequacy Relativity
ratios, A.M. Best financial ratings, and the NAIC RBC ratios. They use property-casualty data
from 1995 to predict insolvencies over the period 1996-1998. They find that the private sector risk
measures have superior predictive accuracy in comparison with FAST and RBC. They also find
that the predictive accuracy of the RBC ratios improves significantly if RBC rankings are used
rather than the ratios themselves. Thus, the conclusions of GHK, CGP, and PS are that RBC is not
an accurate predictor of insolvencies, that the NAIC solvency measures are inferior to private
market risk measures, and that cash flow simulation improves predictive accuracy. These findings
provide powerful arguments for the NAIC to move from a static, factor-based approach to a more
dynamic model-based approach.
In the light of the findings about RBC system accuracy, it is also revealing to look at the
RBC ratios for firms that recently became insolvent versus those that remained solvent. I.e., we
calculate the RBC ratios for insolvent firms in the year prior to insolvency and for firms in the
same year that did not become insolvent in the following year. We conducted this analysis for
property-casualty insurers. Calculating the ratios for the period 1997-2006, we find that the ratio of
adjusted capital-to-RBC in the year prior to insolvency events was 230% for insolvent propertycasualty insurers and 710% for insurers that remained solvent. Thus, insolvent firms clearly had
much lower ratios, but on average did not fall below the Company Action Level. This provides
further evidence that RBC is somewhat sensitive to insolvency risk but not as accurate as would
perhaps be desirable.
31
A final point to be made about RBC concerns a rather strange argument made by some
experts who were involved in the original design of the system. When informed that RBC is not
an accurate solvency predictor, some of these individuals argue that it was never intended to be a
predictor of insolvency, and therefore it does not matter whether it is accurate or not. Rather, it
was merely intended to give regulators stronger authority to discipline financially weak insurers. It
seems obvious that this argument misses the point, and as well it ignores the costs that can be
imposed on insurers to the extent the system is inaccurate. The system is called risk-based capital,
and thus it should relate to risk. Presumably, given that it is a regulatory solvency system, the type
of risk the system should be measuring is insolvency risk. Whether that was or was not the
primary original goal of the system is irrelevant at this point in time. In addition, if the system is
inaccurate, it may identify financially sound firms as being in need of regulatory attention or fail to
identify firms that pass the RBC threshold but in fact have abnormally high insolvency risk.
Therefore, it seems imperative that the objective should be to devise a system that measures
insolvency risk as accurately as possible.
5. Conclusions
This paper reviews the historical insurance insolvency experience in the U.S., compares the
U.S. risk-based capital (RBC) system with the European Union’s Solvency II system and the Swiss
Solvency Test (SST), and evaluates the U.S. RBC system. The insolvency review shows generally
favorable solvency experience in the U.S. life and property-casualty insurance industries. The
insolvency rates, numbers of insolvencies, and costs in terms of guaranty funds assessments have
been quite low, particularly during the most recent decade. Thus, the insurance industry generally
appears to be prudently managed, and insurance regulation appears to be effective. Nevertheless,
there are areas where regulation could be improved.
The comparative analysis of RBC, Solvency II, and the SST shows that the U.S. system is
32
out-of-date. Solvency II and the SST are principles-based systems that utilize market values to
measure solvency, whereas RBC is a rules-based system that utilizes statutory accounting values.
The U.S. system is static and ratio-based, whereas the European systems are dynamic and modelbased. RBC is “one size fits all,” contrary to Solvency II and the SST, which can be geared to
individual insurer characteristics. The U.S. system ignores important risks such as operational risk
and catastrophe risk and overlooks important qualitative criteria such as risk management systems
and corporate governance. Given the limitations of the U.S. system, it is not surprising that
research has shown that RBC is not an accurate predictor of insurer insolvency.
The U.S. should move to a principles-based system including dynamic models that are
geared to market values of assets and liabilities. Internal models should be permitted; but to
reduce modeling risks and mitigate managerial moral hazard, there should also be a standard
model that is run for every firm in the industry. Capital lower than recommended by the standard
model should be permitted only if thoroughly justified. The NAIC needs to establish a Model
Evaluation Office to build expertise in reviewing and vetting internal models. The U.S. system also
needs to systematically incorporate qualitative factors, provide incentives for improved risk
management, and introduce an own-risk and solvency assessment (ORSA) process.
Further
analysis also needs to be conducted of systemic risk in the insurance industry to avoid future
problems similar to the AIG debacle.
33
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Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS), 2008a,
CEIOPS’ Report on its Fourth Quantitative Impact Study (QIS4) for Solvency II (Frankfurt,
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Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS), 2008b,
CEIOPS’ Report on its Fourth Quantitative Impact Study (QIS4) for Solvency II: Annex of
Selected Tables (Frankfurt, Germany).
Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS), 2008c,
Quantitative Impact Study 4: Summary Results and Main Messages (Frankfurt, Germany).
Cummins, J. David, 1988, “Risk-Based Premiums for Insurance Guaranty Funds,” Journal of
Finance 43: 823-839.
34
Cummins, J. David, Martin F. Grace, and Richard D. Phillips, 1999, “Regulatory Solvency
Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow
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Cummins, J. David, Scott E. Harrington, and Robert W. Klein, 1995, “Insolvency Experience,
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Elderfield, Matthew, 2009, “Solvency II: Setting the Pace for Regulatory Change,” The Geneva
Papers 34: 35-41.
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35
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36
Figure 1: Property-Casualty Impairment Rate and Combined Ratio: 1969-2008
120
2
Combined Ratio after Dividends
P/C Impairment Rate (%)
1.8
115
1.6
1.4
105
1
0.8
Impairment Rate (%)
1.2
100
0.6
0.4
95
0.2
90
0
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Combined Ratio
110
Source: A.M. Best Company (2009a, 2009f).
37
19
7
19 7
7
19 8
7
19 9
8
19 0
8
19 1
8
19 2
8
19 3
8
19 4
8
19 5
8
19 6
8
19 7
8
19 8
8
19 9
9
19 0
9
19 1
9
19 2
9
19 3
9
19 4
9
19 5
9
19 6
9
19 7
9
19 8
9
20 9
0
20 0
0
20 1
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
08
Impairment Rate (%)
P/C FIF
Cat Points in Combined Ratio
1.8
1.6
1.4
10
1.2
1
8
0.8
6
0.6
4
0.2
0
CAT Points In Combined Ratio (%)
Figure 2
Property-Casualty Impairment Rate & Catastrophe Points in Combined Ratio
2
14
12
0.4
2
0
Source: A.M. Best Company (2009f).
38
19
7
19 6
7
19 7
7
19 8
7
19 9
8
19 0
8
19 1
8
19 2
8
19 3
8
19 4
8
19 5
8
19 6
8
19 7
8
19 8
8
19 9
9
19 0
9
19 1
9
19 2
9
19 3
9
19 4
9
19 5
9
19 6
9
19 7
9
19 8
9
20 9
0
20 0
0
20 1
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
08
Impairment Rate (%)
3.5
L/H Impairment Rate
3
2.5
3
2
2
1.5
1
1
0.5
0
After-Tax Profit Margin (%)
Figure 3: Life-Health Impairment Rate and Profit Margin
5
Profit Margin
4
0
-1
Source: A.M. Best Company (2009c).
39
Figure 4
Property-Casualty Impairments: Triggering Events
Significant Change In
Business
4%
Investment
Problems/Overstated
Assets
7%
Catastrophe Losses
8%
Reinsurance Failure
4%
Deficient Loss
Reserves/Inadequate
Pricing
38%
Impairment of an
Affiliate
8%
Alleged Fraud
8%
Miscellaneous
9%
Rapid Growth
14%
Source: A.M. Best Company (2009f).
40
Figure 5
Life-Health Impairments: Triggering Events
Significant Change in
Business
5%
Alleged Fraud
9%
Reinsurance Failure
2%
Inadequate Pricing
27%
Miscellaneous
9%
Investment Problems
15%
Affiliate Problems
18%
Rapid Growth
15%
Source: A.M. Best Company (2009c).
41
Figure 6: Property-Casualty Guaranty Fund Assessments By Year
0.50%
1,400
GF Assessments
% of NPW
0.45%
1,200
1,000
0.35%
0.25%
600
0.20%
0.15%
400
0.10%
200
0.05%
0.00%
-
Source: National Conference of Insurance Guaranty Funds, A.M. Best Company (2009a).
42
% of NPW
0.30%
800
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Assessments ($millions)
0.40%
43
F ig u r e 8
In s u r a n c e In s o lv e n c y : C a u s a l C h a in
C r e a tio n o f U n d e r ly in g C a u s e s o r P r e c o n d itio n s
In c u b a tio n P e r io d : A r r iv a l o f In te r m e d ia te C a u s e s
P r e c o n d itio n s a n d In te r m e d ia te C a u s e s C o m b in e
to R e a c h C r itic a l M a s s
T r ig g e r in g E v e n t
F in a n c ia l L o s s to In s u r e r
C o s ts Im p o s e d o n P o lic y h o ld e r s & G u a r a n ty F u n d s
44
F ig u r e 9 : R is k M a p - T h e P a t h T o In s o lv e n c y
U n d e r ly in g C a u s e s :
In t e r n a l
M a n a g e m e n t,
governance,
o w n e r s h ip
U n d e r l y in g o r T r i g g e r C a u s e s : E x t e r n a l
G e n e r a l E c o n o m i c F lu c t u a t i o n s , L o c a l i z e d S h o c k s t o I n s u r a n c e
In d u s tr y
O p e r a t io n a l R i s k :
In a d e q u a te o r
f a i l e d in t e r n a l
pro cesses,
p e o p le , o r
s y s te m s
In a p p r o p r ia t e
R is k D e c is io n s :
P r i c in g ,
u n d e r w r itin g ,
in v e s t m e n ts ,
ALM
r e in s u r a n c e
R is k A p p e t it e D e c is io n
Source: Sharma (2002) and the authors.
F i n a n c ia l
O u tc o m e s
M a r k e t r is k ,
C r e d i t r is k ,
C la im s r is k ,
R e s e r v in g r is k
R e p u t a t io n r is k
L o s s e s to
P o l ic y h o l d e r s ,
In v e s t o r s ,
G u a ra n ty F u n d s
E r r o n e o u s In t e r p r e t a t io n
o r R e a c t io n t o F in a n c i a l
O u tco m e s
45
46
47
Figure 12: Value at Risk (VaR) and Tail Value at Risk (Tail VaR)
-r
Note: The figure graphs the random variable S(1) = e [A(1)-L(1)] - [A(0)-L(0)],
where r = the risk free rate, A(t) and L(t) are market consistent values of
assets and liabilities at time t, and S(1) is the shortfall at time 1.
VaR 1%
Expected Shortfall = Tail VaR(α)
Density: h(x)
0
S(1) = exp(-r)[A(1) - L(1)] - [A(0) - L(0)]
48
49
50
51
Figure 16
Equity Capital to Asset Ratios -- Banks, PC Insurers, and Life Insurers
0.45
Banks
Life Insurers
PC Insurers
0.4
0.35
0.25
0.2
0.15
0.1
0.05
5
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
0
19
8
Equity/Assets
0.3
Source: Federal Reserve Flow of Funds Accounts; American Council of Life Insurance.
52
Figure 17
Premiums-to-Surplus Ratios: Life-Health and Property-Casualty Insurers
3.0
PC Insurers
LH Insurers
2.0
1.5
1.0
0.5
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
Premiums-to-Surplus
2.5
Source: A.M. Best Company (2009a, 2009d), American Council of Life Insurance.
53
Table 1: Comparison of Solvency II, the SST and U.S. Risk-Based Capital Requirements
Solvency II Framework (SII)
Stochastic Approach Applies VAR at confidence level of 99.5%.
Swiss Test (SST)
Expected shortfall (Tail VaR) at 99% VaR.
U.S. Risk-Based Capital (RBC)
Non-stochastic except for some types of
annuities.
Introduces a quantitative approach for
operational risk: the charge is a % of the
Basic Solvency Capital Requirement.
Qualitative approach to operational risk
within the SST report.
Operational risk not explicitly considered.
Catastrophe risk falls within underwriting risk.
Covers catastrophe risk via predefined
scenarios.
Catastrophe risk not explicitly considered.
Economic Values
Under Pillar I, capital requirements are
calculated based on an economic balance
sheet, using market-consistent values of
assets. (SII is intended to be aligned with
International Financial Reporting Standards
(IFRS), although inconsistencies and
challenges can be experienced in practice.)
SST also uses market-consistent values of
assets and liabilities. Market consistent
RBC is based on statutory accounting
liabilities equal the discounted best estimate
values, which are not necessarily good
plus the market value margin (MVM). The
proxies for market values
minimum capital ratio (MCR) is based on the
statutory balance sheet.
Risk Management
Pillar II encourages insurers to implement
enhanced risk management practises.
Insurers required to conduct Own Risk and
Solvency Assessment (ORSA).
A complex standard formula was introduced
with an explicit target to ensure adequate risk No risk management provisions.
management capabilities of all insurers.
Operational Risk &
Catastrophe Risk
Considered under Pillar II. Also, under the
Corporate Governance,
general governance principles, the “fit and
Assessment of
proper” standard for persons in the key
Management
corporate functions.
Public Disclosure
Internal Models
Fosters market transparency by requiring a
wide range of public disclosures (including
information on the insurer’s solvency and
financial condition) under Pillar III.
Internal models are encouraged.
Does not cover corporate governance, but
fulfilled in part by the “Swiss Quality
Assessment,” and insurance licenses are
granted only if certain management positions
are filled by qualified persons.
Corporate governance is not part of RBC
but is considered by some commissioners
and sometimes the NAIC's Financial
Analysis Working Group.
Does not require public disclosure.
RBC is confidential when filed, but results
are disclosed in the annual statement; thus,
effectively, RBC score is public.
Internal models are the default. Standard
Static, factor-base system except for some
model can be used if it accurately represents
types of annuities.
a firm's risk.
Principles-based with technical rules.
Rules-based
Principes vs. Rules Principles-based with technical rules.
Source: The authors and A.M. Best Company (2009b).
Note: The NAIC currently has a Solvency Modernization Initiative, which may lead to changes in the RBC system.
54
Table 2
Risk-Based Capital Ratios: Property-Casualty and Life Insurers, 1997-2007
Panel 1: Property-Casualty Insurers
Number of companies
Adjusted
Capital/RBC
≥ 200%
175-199%
150-174%
125-149%
100-124%
< 100%
Total
Average Ratio
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1,965 1,978 1,906 1,861 1,876 1,829 1,856 1,879 1,878 1,957 1,914
18
14
11
22
11
15
10
16
10
4
13
13
7
11
9
10
24
15
11
13
17
7
7
5
9
7
15
7
5
12
7
8
8
7
6
6
13
10
16
6
13
8
4
5
13
12
16
17
32
18
27
21
19
17
16
2,023 2,022 1,959 1,929 1,954 1,909 1,919 1,952 1,935 2,007 1,963
1225% 1220% 1222% 1344% 1316% 1215% 1167% 1111% 1199% 1277% 1271%
Percentage of companies
Adjusted
Capital/RBC
≥ 200%
175-199%
150-174%
125-149%
100-124%
< 100%
Total
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
97.1% 97.8% 97.3% 96.5% 96.0% 95.8% 96.7% 96.3% 97.1% 97.5% 97.5%
0.9%
0.7%
0.6%
1.1%
0.6%
0.8%
0.5%
0.8%
0.5%
0.2%
0.7%
0.6%
0.3%
0.6%
0.5%
0.5%
1.3%
0.8%
0.6%
0.7%
0.8%
0.4%
0.3%
0.2%
0.5%
0.4%
0.8%
0.4%
0.3%
0.6%
0.4%
0.4%
0.4%
0.3%
0.3%
0.3%
0.7%
0.5%
0.8%
0.3%
0.7%
0.4%
0.2%
0.3%
0.6%
0.6%
0.8%
0.9%
1.6%
0.9%
1.4%
1.1%
1.0%
0.8%
0.8%
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
Panel 2: Life Insurers
Number of companies
Adjusted
Capital/RBC
≥ 200%
175-199%
150-174%
125-149%
100-124%
< 100%
Total
Average Ratio
1997
1,210
44
42
46
16
26
1,384
290%
1998
1,198
50
32
31
16
22
1,349
286%
1999
1,125
37
39
32
18
27
1,278
283%
2000
1,061
44
28
31
19
15
1,198
287%
2001
1,046
37
29
31
14
21
1,178
346%
2002
1,002
31
25
30
13
25
1,126
325%
2003
1,051
30
24
30
18
22
1,175
357%
2004
1026
18
21
25
13
16
1119
390%
2005
997
19
16
15
10
14
1,071
409%
2006
948
19
22
21
5
14
1,029
411%
2007
892
23
11
13
5
16
960
406%
Percentage of companies
Adjusted
Capital/RBC
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
≥ 200%
87.4% 88.8% 88.0% 88.6% 88.8% 89.0% 89.4% 91.7% 93.1% 92.1% 92.9%
175-199%
3.2%
3.7%
2.9%
3.7%
3.1%
2.8%
2.6%
1.6%
1.8%
1.8%
2.4%
150-174%
3.0%
2.4%
3.1%
2.3%
2.5%
2.2%
2.0%
1.9%
1.5%
2.1%
1.1%
125-149%
3.3%
2.3%
2.5%
2.6%
2.6%
2.7%
2.6%
2.2%
1.4%
2.0%
1.4%
100-124%
1.2%
1.2%
1.4%
1.6%
1.2%
1.2%
1.5%
1.2%
0.9%
0.5%
0.5%
< 100%
1.9%
1.6%
2.1%
1.3%
1.8%
2.2%
1.9%
1.4%
1.3%
1.4%
1.7%
Total
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
Source: NAIC annual statement data and American Council of Life Insurance.
55