Rachit Talwar
Student ID- 44162901
ACST840 Quantitative Research Methods 2
Thursday, 7 September 2017
The Satellite Insurance Market and Underwriting Cycles
Piotr Malinowski and Mary A. Weiss
The Geneva Risk and Insurance Review, 2013, 38, (148–182)
© 2013 The International Association for the Study of Insurance Economics
Abstract
This study examines whether underwriting cycles are present in an important but often overlooked line of insurance, satellite insurance. The motive of the study is to determine whether components like rate on lines and loss ratios determine the applicability of cycles. Regression analysis using the second order autoregressive model –AR (2) is carried out on the rate-on-line and capacity variables, and the regression results are used to support the rational expectations/institutional intervention hypothesis (Cummins and Outreville,1987) and the capacity constraint (capital shock)
hypothesis(Winter,1994).
Though sometimes criticized it is a highly successful model.
The Geneva Risk and Insurance Review (2013) 38.
Keywords: satellite insurance; underwriting cycle; rational expectations/institutional
intervention hypothesis; capacity constraint (capital shock) hypothesis.
Introduction
. The underwriting cycle refers to a repeating series of phases that insurance markets go through (Niehaus and Terry 1993; Harrington and Danzon 1994). The sequence of hard and soft markets may be observed in prices, profitability, and supply (capacity) for insurance. Most of the research documents that would talk about the existence of cycles would rely on the time series behavior of published underwriting information on loss ratio. The paper has used two prominent underwriting cycle theories, the rational expectations/institutional intervention hypothesis and the capacity constraint theory which are tested with satellite insurance industry.
The existence of underwriting cycles depend on how the insurance product is priced.. Volatility and cyclicality are emphasized in this satellite insurance research paper..
Historically the market has turned when underwriters start to experience a net cash outflow – cash flow underwriting, though actuarially displeasing, is common practice .The difficulties involved in cycle management lie in diagnosing the market situation and in implementing the strategy.
Diagnosis for implementing a strategy requires a future-oriented market analysis to recognize changes in prices. Implementation depends on underwriting expertise only refined underwriting skills make it possible to distinguish good from less good business There is no universal method of underwriting cycles determination, but the most commonly used is a second-order autoregressive model (AR(2)) proposed by Venezian (1985) which has been used in the research paper in order to test the parameters for testing for the existence of the underwriting cycle. The determination of the overall underwriting cycle for the entire insurance market could be difficult as, in practice there is rather bunch of cycles, which show variations of only one selected indicator (e.g. loss ratio). The research paper has accurately described the relation between parameters which is really important as it allows for a distinction of three types of indicators: coincident (at approximately the same time as the conditions they signify), leading (signaling future events) and lagging (following an event).
The data set used in this study consists of time series data from 1968 to 2010, and how these data comprises of the entire history of the satellite industry. It states clearly how in the mid 90s the market was soft with low rates and excess capacity but as new insurers emerged in the market it began to harden after suffering losses. The last hard market lasted from 2000 to 2004 but it softened again between 2005 and 2007. By the middle of 2008, launch rates stabilized and the rates started to fall downwards until 2013. Thus the satellite insurance market appears to go through perpetual sequences of “hard” and “soft” markets, forming the basis of a cycle.
THE SATELLITE INSURANCE MARKET
This research is important, also, because of the importance of the satellite industry. Satellites perform a vast variety of function like remote sensing ( observing and measuring our environment from a distance), used in the field of oceanography by marine scientists, for its ability to see the weather on a global scale, to take photographs and satellite imagine like google earth . The author talks about the development of the insurance market from its inception in the mid 1960s to 2013.
Until the mid 1960s most satellites were launched related to their military needs and the risk was taken by the government, in 1962 The American Communication Satellite Corporation was the first company to deal with satellite insurance. By the use of histogram, the author shows the number of failures were higher in the early years. In the recent years there’s only been 3-4 failed launches annually. Using the same method, the study shows the number of insured launches had grown since the late 1980s. It then gives relative information on graphical basis using histogram depicting the period of premium growth since 1970 to 2010 when there was a sudden boom post 1998.
The loss ratio is the total losses paid by an insurance company in the form of claims. The losses are added to adjustment expenses and then divided by total earned premiums
The ratio of premium paid to loss recoverable in a reinsurance contract. Rate on line (ROL) represents the amount charged per a $1000 of coverage proper depiction of the loss ratio over time showed the claims had outstripped premiums ( Satellite insurance loss ratio: 1968–2010.)The author takes into consideration the minimum rate-on-line , the average rate of line and the capacity to indicate wide variation in rates on line and capacity, but these factors also appear to be cyclical as the capacity when compared to ROL fell in 1980s then reached its peak in 1999 , the shrunk and rose again in 2005. In addition to justify the idea we found that the correlation of coefficient is -.066 showing inverse relationship between the two. It can be seen that the satellite market is a highly unpredictable market which is cyclic in nature. It is not known whether developments in the satellite insurance can have an impact on costs for services to the buyers of the insurance , hence I feel the risks associated depends highly on the markets cycle, whether it’s at its peak or on a low.
Hypothesis
The basic idea of the hypothesis is to demonstrate a relation between satellite insurance rate on lines with past losses and to the capacity or coverage availability to justify the analysis studied previously.
I feel the author could also have tested the profitability when they are talking about the ROL and the tendencies of different indices during the hard and soft markets which shows its behavior in a changing(cyclic) scenario.
The first hypothesis was Rate-on-line is positively associated with past losses, one way of doing it is to increase the ROL hence the prices would rise. There is a following hypothesis to which it states “The maximum amount of coverage available for a new satellite launch is negatively related to past losses”
The rational expectations theory is an economic idea that the people make choices based on their rational outlook, available information and past experiences hence an increase in ROL or decrease in coverage availability will result in increasing premiums during hard markets. owers, (M. R. and Shubik, M., 2006)According to the capacity constraint hypothesis if ever a capital shock occurs the price will be inversely related to capacity (coverage availability) which leads to the second hypothesis
“Satellite insurance rates are inversely related to the amount of satellite insurance coverage available for a new satellite launch”
DATA
The data for the analysis covered the period from 1968-2010 which includes the entire history of the satellite insurance. I believe that it was important to take a huge amount of data for the analysis to support the hypothesis which had to be performed in accordance with past losses and the changes in the market cycle including the capital shocks which supports the constraint hypothesis.
The data was obtained from insurance brokers, underwriters and big companies hence was full and representative of the entire population. Macroeconomic data had also been used from official publications.
Methodology
There was a two-step methodology which was followed:
• Underwriting cycle determination
This is determined by using the second order autoregressive method by Venezian where the variable Pt is the subject to a cycle and along with it several dependent variables are tested like
1. Satellite insurance rate
2. Average insurance rate
3. Insurance capacity
4. Loss ratio
Given by the autocorrelation function Pt =a0 +a1Pt-1 +a2Pt-2 +ωt including the random error term.
A cycle is present if a1>0, a2<0 since a0and a1 are the solutions to the equation furthermore the periodicity is decided by the model coefficients, result is based on a 1% critical value for the test statistic.
The AR processes have a “long” memory as the current value of the series is correlated with all previous ones hence is good when we take a huge data for testing.
Whereas the MA process has a very short memory hence not suitable for the analysis
The author could choose ARMA process which has the property of both but as we just had to check for the signs of the coefficients it is not viable to use this method.
• Analysis of insurance rates and coverage/capacity
To test hypothesis 1-3 regression models had been used. Two regression models had been used to test the hypotheses. In the first regression model, satellite insurance rate was assumed to be a function of capacity/ coverage, past losses(main variable to be tested) and control variables consisting of the discount rate and demand . In the second regression model, capacity was assumed to be a function of the satellite insurance rate, past loss ratios and control variables like the condition of the overall industry . To check if variables were stationary AR2 model was applied , if they’re stationary unit root test were used using the ADF test (Dickey- Fuller test). This was important for the following reasons
• Persistence of shocks will be infinite for non-stationary series
• Spurious regressions
• T ratios will not follow a t distribution so no information on regression parameters could be obtained
They could have used The Phillips – Perron Test which gives the same conclusions as the ADF tests , but calculations are more complex.
Satellite insurance rate model:
1) The satellite insurance rate model is specified as:
ΔRatet = α + β1ΔLoss ratiot – 1 + β2ΔLoss ratiot – 2 + β3ΔLoss ratiot – 3
+β4ΔCapacityt +β5ΔDemandt +β6ΔInterest ratet + β7Trend+εt
Change in real minimum rate-on-line was used as the dependent variable and capacity available for a new test was used to test the capacity constraint. Under the capacity constraint theory we have come to fact that the coefficient for this variable should be negative. Demand, interest rates and trends are the other control variables used in this model. Demand is defined by the number of total launches in a given year. Previous studies have given evidence that the insurance prices are inversely related to discount rates.60 A trend variable is put in the model to take into consideration for the increasing knowledge about the industry as the insurers charge according to the previous performance this industry over time.
The satellite insurance capacity regression model is specified as:
2) Capacity= α + δ1ΔLoss ratiot – 1 + δ2ΔLoss ratiot – 2 + δ3ΔLoss ratio t- 3 + δ4ΔRatet + δ5ΔNew Satellite Valuet + δ6Δ Premiums Written=Surplus
+ δ7ΔTotal Launch Failed % t + δ8Trend + νt
The coefficient for capacity variable is expected to be negative as capacity is expected to be negatively related to higher failure ratios.
The expected signs of the loss ratios are meant to be negative as premium has direct correspondence to the capacity available and premiums decrease when the underwriting performance goes below standards.
In both the equations the change in rate and change in capacity variables appear both as dependent and independent variables. Therefore, the two equations were estimated as a system of equations using 3SLS
The error term εt is assumed to be N(0, σ2R) and υt is assumed to be N(0, σ2C).
I feel the OLS estimator is biased and inconsistent for simultaneous equations whereas the 2SLS is used for single equation estimation
Three-stage least squares (3SLS) is used for the whole system estimation hence it’s the right choice, as the author wanted to to test cross-equation restrictions which would otherwise be impossible to do using 2SLS.
RESULTS
Results from the analysis determined that MEAN of the minimum rate-on-line is 0.1074, while capacity MEAN is approximately $384 mil , these results were used to determine whether or not the underwriting cycles exist in satellite insurance
The cycle periods linked to minimum and average rate-on-line were 12.48 and 13.95, respectively, when trend was included as a variable. These periods were relatively long compared to 6 year average cycle commonly cited in some studies. ( Lamm-Tennant and Weiss(Cummins and Osterville)found cycle lengths as long as 11 years in their study. (Chen et al) found a cycle length of approximately 14 years in some of their results
Loss ratio did not have any cycle detected. There was no reason given for it in the article
The given table consists of the hypothesis test. The hypotheses were based on the rational expectations hypothesis and the capacity constraint.
In the first hypothesis we expected the minimum rate-on-line to be positively and significantly related to lagged past losses This hypothesis is partly supported, the coefficient for the change in the first lagged loss ratio is significant while the coefficient for the second lagged loss ratio is positive with a t-statistic of approximately 1.5
From the second hypothesis it was expected that the capacity (i.e., the maximum amount of coverage available for launch insurance) should be negatively related to past losses. The results from this test did not support the hypothesis.
Conclusion
In the words of Rudolf Flicker, the Member of the Board of Management responsible for space insurance at Munich Re, “It is only possible to do business in space insurance with an underwriting policy that is based on long-term goals” hence I chose this article for my report as it explains clearly the analysis for predicting the future of satellite insurance market.
The main motive of the article was to come to a conclusion whether underwriting cycles exist and what causes it, and it concluded that they do in in the minimum rate-on-line, average rate-on- line and capacity. The corresponding cycle periods are relatively long (13–25 years) compared to other commonly cited cycle periods averaging 6-7 years. The analysis of changes in the minimum rate-on-line provided some support for the rational expectations/institutional intervention hypothesis. The minimum rate-on-line is negatively related to capacity (coverage availability), as predicted by the capacity constraint theory. They could also have chosen the cycle deviation method for checking for profitability as it is the ultimate goal of the insurer
References
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Cummins, J.D. and Outreville, F. (1987) An international analysis of underwriting cycles, The Journal of Risk and Insurance 54(2): 246–262.
Dickey, D.A. and Fuller, W.A. (1979) Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association 74(366a): 427–431.
Lamm-Tennant, J. and Weiss, M.A. (1997) International insurance cycles: Rational expectations/ institutional intervention, The Journal of Risk and Insurance 64(3): 415–439
Lamm-Tennant, J. and Weiss, M.A. (1997) International insurance cycles: Rational expectations/ institutional intervention, The Journal of Risk and Insurance 64(3): 415–439
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