The Impact of Reinsurance on Cost of Capital of Insurers

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THE IMPACT OF REINSURANCE ON COST OF CAPITAL OF INSURERS

The Impact of Reinsurance on Cost of Capital of Insurers

Abstract

The primary objective of this study is to test a theoretical framework relating the purchase of reinsurance with the cost of equity capital and cost of debt capital in the insurance industry. Company stock betas are selected as indicators of cost of equity capital and besides retention ratios, several company performance attributes are selected as factors that affect stock betas. A regression analysis is used for analyzing their relationships with company stock betas. For cost of debt equity, credit ratings are selected as its indicators and a descriptive analysis is used for analyzing its relationship with reinsurance purchases. 1. Introduction

Insurers have to maintain a sufficient amount of capital to support their promise to policyholders, which is a unique feature of insurance industry. Even though insurers can solely depend on capital from premiums paid by policyholders but in order to expand and remain solvent, insurers may also raise capital from investors such as stockholders and creditors. Additional capital from stock and bond markets can serve as buffers against unexpected claims or financial losses. However, this kind of capital is often associated with a cost, which is the fair rate of return insurers have to pay for the use of the capital. If an insurance company does not pay the required rate of return, there will be risk that its stock prices drop to such an extent that the company becomes a takeover target for competitors and risk that rating agencies downgrade its credit ratings to such an extent that the company becomes under regulatory supervision.

Since holding a strong capital base and paying the demanded rate of return is such an important consideration for insurers, examining what decisions and factors may affect cost of capital becomes an interesting and significative topic. Among the various decisions and factors that may impact cost of capital, the purchase of reinsurance stands out as one of the most commonly used risk funding tools of insurers. Even though reinsurance can be very beneficial as it helps to increase the underwriting capacity, reduce the risk of insolvency, and stabilize the earnings level of the direct insurer, it exposes the direct insurer to more credit risk, reinsurance market fluctuations, and unreasonably high rates.

Previous research concentrated on examining diverse corporate finance mechanisms associated with cost of capital while few of them addressed the issue of industrial variation. The allocation of different types of capital may vary largely among different industries. Insurance companies, as unique leveraged investment vehicles, have corporate structures that are completely disparate from other industries. Consequently, the results presented in the former studies, though supported by elaborate statistical analyses and persuasive reasoning, may not apply to insurance industry at all.

The purpose of this current study is therefore to empirically investigate the impacts of reinsurance and some company performance attributes on cost of capital in the insurance industry. There are two major reasons why insurance industry is selected as the subject for this study. The first is that insurers operate under strict regulatory constraints on how they run their businesses and how much capital they need to set aside to keep operating. The second is that accounting rules for recording earnings and asset value at insurers are at variance with accounting rules for the rest of the market.

The remainder of the paper is organized as follows. Section 2 reviews prior studies on the relationship between reinsurance purchase and its impact on insurance companies. Section 3 develops the hypotheses and describes the study sample. Section 4 tests the hypotheses and analyzes the results. Section 5 makes the final conclusion and discusses the major limitations.

2. Literature Review

Past literature breaks into two streams with one concentrating on the productive and positive effects of reinsurance and the other on the negative impacts of reinsurance. Several empirical studies found a strong connection between reinsurance purchase and good firm performance. Zeng (2005) indicated that shareholders can utilize reinsurance to manage their risk well for the ultimate goal of maximizing the firm value as well as maintaining longer term sustainability. Meier & Outreville (2006) proposed that with reinsurance, an insurance company is able to sustain a larger volume of premiums than it would with other types of risk financing techniques. Cummins et al. (2008) argued that the purchase of excess of loss reinsurance improves loss experience by lowering claim payments caused by huge losses and catastrophes, and reduces the potential liability of an insurance company on some specified risks. Cummins, Feng, & Weiss (2011) discovered that reinsurance utilization can bring insurers with better performance but concentration in reinsurance counterparties can adversely impact insurers’ performance. Swiss (2002) and Cummins & Trainar (2009)emphasized that reinsurers' superior knowledge and wide-ranging proficiency in product pricing, underwriting and risk management can help the cedents increase their economic value significantly.

However, as stated, there is the other stream which focuses on discussing negative effects brought by reinsurance, among which the most frequently mentioned were its associated credit risk and high costs. Froot (2001) stated that since the cost of reinsurance is always greater than the actuarial price of the transferred risk, the apparent disadvantage of this practice is that it is so expensive that it could probably cause the primary insurer to provide insurance at a higher cost. Meier & Outreville (2006) explained why reinsurance with decreased price could indeed increase the loss ratios of insurance companies. When reinsurance becomes more and more affordable, it is highly likely that most insurers will rush to purchase reinsurance in order to enhance their underwriting capacity. However, this will increase the competition among insurers, driving the price low and hence the loss ratios high. Cummins & Doherty (2002) mentioned the concepts of hard and soft market to illustrate how reinsurance may lead to inadequate management towards certain systematic risk. In the soft market, prices are low and coverage is widely available while in the hard market, prices are high and necessary coverage may be unavailable. If there is a reinsurance market cycle shifting from soft market to hard market, without timely risk management, insurers may suffer huge losses.

3. Hypothesis Development and Data Collection

Hypothesis Development

Since under CAPM Model, cost of equity capital is calculated as risk free rate plus stock betas multiplied by the difference between market risk premium and risk free rate, a direct impact on stock betas will result in a direct impact on cost of equity capital. In addition, under the assumption that firm specific features such as firm size, operating ratios and retention ratios do not impact risk free rate and market risk premium, the relationship between selected attributes and stock betas should be in the same direction as the relationship between selected attributes and cost of equity capital.

3.1.1. Firm size and cost of equity capital

Aval & Ohadi (2011) stated that since companies with smaller firm sizes usually grow and expand faster, they often perform better than the average market when the overall economy is boosted and that since larger firms often have stronger capital bases and better corporate structures than smaller firms, they remain less volatile to adverse market conditions. Combining those two points, we can assume that there is a high probability that insurers with larger firm size will have lower stock betas. The following hypothesis is thus formulated:

H1: Firm size is negatively related to stock betas and thus negatively related to cost of equity capital.

3.1.2. Operating ratio and cost of equity capital

Operating ratios are essential in the insurance industry. They indicate the profitability of the business that the company is involving in and a low operating ratio is often associate with a high ROE. Maintaining profitable will help the stock insurance company increase its policyholder’s surplus and offer more dividends if possible. The result is apparent that stock prices will keep stable or go up so that they hold less volatility. Hence, it is hypothesized that:

H2: Operating ratio is positively related to stock betas and thus positively related to cost of equity capital.

3.1.3. Retention ratio and cost of equity capital

Iqbal & Rehman (2014) stated that by enabling the primary insurer to issue more policies, the purchase of reinsurance helps the company gain stable growth, and acquire more value because of the stable growth. If there is less risk associted with devaluation or insolvency of the company, its stock betas should usually be lower compared with its competitors. Since rentention ratio is how much percentage the insurer retains, namely net written premium over gross written premium, it is has a perfect negative correlation with reinsurance percentage. Hence, it is hypothesized that:

H3: Retention ratio is positively related to stock betas and thus positively related to cost of equity capital.

3.1.4. Retention ratio and cost of debt capital

Iqbal & Rehman (2014) also argued that reinsurance can stabilize an insurer’s capcacity, reduce its overall exposures and help insurance companies incur less financial distress cost, which they support with their empirical results. Again, retention ratio stands as a ratio that’s perfectly negatively correlated with reinsurance percentage. Hence, it is hypothesized that:

H4: Retention ratio is positively related to credit ratings and thus negatively related to the cost of debt capital.

Data Collection

Quantify Credit Ratings

Since few papers have discussed how to quantify credit ratings, this paper is presented with great challenges. The reasoning developed here is that the change in credit ratings can be divided into two categories based on the below table: 1. does not change according to the satisfaction. 2. jumps to a higher rating 3. drops to a lower rating. Any large jump or drop in credit ratings is excluded, because it is highly unlikely that the percentage of reinsurance alone can be the cause; other casual factors may create too much noise. Therefore, the change in credit ratings is presented as a dummy variable.

Long-Term Debt Ratings

Investment Grade

Non-Investment Grade

aaa(Exceptional)

bb(Speculative)

aa(Very Strong)

b(Very Speculative)

a(Strong)

ccc, cc, c(Extremely Speculative)

bbb(Adequate)

d(In Default)

Sample

The sample for this paper is drawn from companies listed in the category of Finance Industry – Life and A&H Insurance/ Property-Casualty Insurers from http://www.nasdaq.com/screening/companies-by-industry.aspx?industry=Finance. One out of every two companies was randomly selected and then some were removed because of lack of access to their specific information. The sample for the study finally stands at 100.

Regression Model and Variables

The ordinary least squares (OLS) regression is employed to examine the relationship between explanatory variables and stock betas and the following regression equation is estimated:

SBT = β0 + β1SIZE + β2OPR + β3RR+ ε

Where:

SBT = stock beta

SIZE = log value of total assets

OPR = operating ratio

RR = retention ratio

Data on stock betas was obtained directly from WRDS. Data on operating ratios and retention ratios was obtained from ISIS – insurance companies worldwide. A ternary scheme is used to denote the change in credit ratings. “1” is assigned for companies of which credit ratings jumped, “0” for companies of which credit ratings remained constant, and “-1” for companies of which credit ratings dropped. Data on credit ratings was obtained from AM Best.

4. Hypothesis Tests and Discussion

Results of Descriptive Statistics and Correlation Analysis

Table 2 shows the descriptive statistics for all variables. The average stock beta of the sample companies was 1.33, with a range of 0.79 to 2.01 and a standard deviation of 0.48. Thus, there were large variations in stock betas among the sample companies. Table 2 also shows that average retention ratio of the sample companies is 83.08%, with a range of 67.96% to 92.19% and a small standard deviation of 7.80%. This indicates that there are small variations in retention ratios among the sample companies.

Table 3 shows the descriptive statistics of retention ratios classified by credit ratings. The average retention ratios of the sample companies of which credit ratings dropped are lower than the average retention ratios of the sample companies remained the same or improved. There is also large difference in standard deviations and extreme values between the sample classes. This perhaps indicates that reinsurance purchase could have some negative influence on credit ratings because of its associated credit risk.

It is encouraging that the bivariate analysis in Table 4 suggests no serious multicollinearity among the independent variables. Moreover, from Table 4 it can be observed that operating ratios and firm sizes are both negatively correlated with stock betas, same as the expected relationships. Thus the bivariate findings may provide a basis for interpreting the results of the multivariate analysis later.

Table 2: Descriptive Statistics for All Variables

Variables

N

Mean

Std. Deviation

Minimum

Maximum

Dependent variable

SBT

100

1.33

0.48

0.79

2.01

Independent variables

SIZE

100

5.23

1.21

8.06

1.12

OPR

100

0.93

0.11

0.76

1.38

RR

100

0.83

0.08

0.68

0.92

Data:

SBT: stock beta

SIZE: log value of total assets

OPR: operating ratio

RR: retention ratio

Table 3: Descriptive Analysis

CR

N

Mean

Std. Deviation

Minimum

Maximum

Dummy values

RR

1

4

0.86

0.11

0.80

0.92

0

90

0.83

0.07

0.69

0.90

-1

6

0.76

0.14

0.68

0.85

Data:

CR: Credit Ratings

RR: retention ratio

Table 4: Correlation Analysis

Variable

SBT

SIZE

OPR

RR

SBT

1.0000

SIZE

-0.7100

1.0000

OPR

-0.8285

0.2014

1.0000

RR

-0.3064

0.1246

0.2766

1.0000

Data:

SBT: stock beta

SIZE: log value of total assets

OPR: operating ratio

RR: retention ratio

Results of hypotheses testing

Table 5 presents the R2, F value, beta coefficients and t-statistics for the model and summarizes the multiple regression results. The table indicates R2 of 0.42 (F = 23.08, p = 0.000), which shows that a moderate percentage (42%) of the variation can be explained by variations in the whole set of independent variables (adjusted R2 = 0.40). Only firm size enters the equation with a regression coefficient that is significant at the 0.05 level in the regression model. On the other hand, operating ratios and retention ratios are insignificant.

Hypothesis 1 states that firm size is negatively related to the stock betas and thus the cost of equity capital. The hypothesized direction is correct and the hypothesis is supported at the 5% significance level. A large firm size is thus viewed as an important factor that affect the stock betas of insurers as well as a possible indicator of lower demanded rate of return on the stock of insurers.

Hypothesis 2 states that operating ratio is positively related to the stock betas and thus the cost of equity capital. The hypothesized direction is not correct and the hypothesis is not supported at the 5% significance level or at the 10% significance level. The two most possible reasons are: 1. Low operating ratio of a single year does not mean the company has a relative stable operating ratio for years. 2. Since stock betas are associated with the market conditions, high profitability associated with low operating ratios could increase stock betas under a stagnant market condition.

Hypothesis 3 states that firms with lower retention ratios have lower stock betas. The hypothesized direction is correct and the hypothesis is supported at the 10% significance level. This result is consistent with that of the first stream of literature which states that reinsurance can help insurers gain stability.

Table 5

OLS Regression Results

SBT = β0 + β1SIZE + β2OPR + β3RR+ ε

Variable

E(Sign)

Coefficient

Std Err

Beta t-values

Significance

Intercept

--

62.419

13.316

-1.68

0.0955

SIZE

-

-11.266

1.402

8.04

<.0001

OPR

+

-0.0768

0.161

-0.48

0.6353

RR

+

7.469

2.130

1.79

0.0766

Data:

SBT: stock beta

SIZE: log value of total assets

OPR: operating ratio

RR: retention ratio

Root MSE = 8.952

R2 = 0.4191

Adjusted R2 = 0.4009

F value = 23.08

F Significance = 0.000

Durbin-Watson Test = 1.984

Number of significant coefficients = 1

N = 100

5. Summary and Conclusion

The results of this paper conclude that reinsurance may help insurers stabilize their stock performance and reduce cost of equity capital but rely on reinsurance imposes insurers to more credit risk and thus lower the credit ratings of the insurers if defaults happen, which in turn increases the cost debt capital. However, since the quantification of credit ratings is not a very reliable method of measuring cost of debt capital and data pool in this paper is not large enough, further research needs to be done in explaining the relationship between reinsurance purchase and cost of capital of insurers.

References

Aval, Z. G., & Ohadi, F. P. (2011). Investigation of the Relation between the Beta, Firm Size, Liquidity and Idiosyncratic Volatility with Stock Return in Tehran Stock Market. Interdisciplinary Journal of Contemporary Research In Business: 1085-1092.

CumminsJ., & DohertyN.A. (2002). Can Insurers Pay for the "Big One"? Measuring the Capacity of an Insurance Market to respond to Catastrophic Losses.

CumminsJ., & TrainarP. (2009). Securitization, Insurance and Reinsurance. The Journal of Risk and Insurance, 76(3), pp. 463-492.

CumminsJ., DionneG., & NouiraG.R. (2008). The Cost and Benefits of Reinsurance.

CumminsJ.D., FengZ., & WeissM.A. (2011). The Impact of Reinsurance on Ceding Insurers' Efficiency in the Property-Liability Insurance Industry: Affiliation and Domicile Effects.

FrootK.A. (2001). The Market for Catastrophe Risk: A Clinical Examination . Journal of Financial Economics, 60(2-3), pp. 529-571.

IqbalTahooraHafiza, & RehmanUr.Mobeen. (2014). Reinsurance analysis with respect to its impact on the performance: evidence from non-life insurers in Pakistan. Aestimatio 8: 90-113.

MeierU.B., & OutrevilleJ. (2006). Business Cycle in Insurance and Reinsurance: The Case of France, Germany and Switzerland. The Journal of Risk and Finance, 7 (2), pp. 160-173.

SwissR. (2002). Introduction to Reinsurance, Swiss Reinsurance Company, Technical Training and Chief Underwriting Officer.

ZengL. (2005). Enhancing reinsurance efficiency using index-based instruments. The Journal of Risk Finance, 6(1), 6-16.

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