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THE SATELLITE INSURANCE MARKET AND UNDERWRITING CYCLES
Piotr Manikowski, Poznan University of Economics, Poland
Mary Weiss, Temple University
ARIA Annual Meeting
Quebec City, August 5-8, 2007
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Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
Agenda
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Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
Agenda
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The Underwriting Cycle
Source: Kunstadter, 2005
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Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
Agenda
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Aims
Determine whether underwriting cycles exist in satellite insurance, and the length of the cycle if relevant.
Determine whether premium components (rates-on-line and annual industry-wide coverage availability or both) are cyclic.
Test two prominent underwriting cycle theories, the rational expectations/ institutional intervention hypothesis (Cummins and Outreville, 1987) and the capacity constraint theory (Winter, 1994) with satellite insurance industry data.
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Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
Agenda
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Figure 5Capacity versus Rate-on-line: 1968-2005
0%
5%
10%
15%
20%
25%
year
0
200
400
600
800
1000
1200
1400
million dollars
Capacity
Minimum Rate
Average Rate
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Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
Agenda
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Hypotheses
Hypothesis 1a: Rate-on-line is positively associated with past losses.
Hypothesis 1b: The maximum amount of coverage available is negatively related to past losses.
Hypothesis 2: Satellite insurance rates are inversely related to the amount of satellite insurance coverage available.
Hypothesis 3: Satellite insurance rates and maximum available coverage are determined simultaneously.
Rational Expectations/ Institutional Intervention Hypothesis
Capacity Constraint
Theory
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Agenda
Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
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Data
Period: 1968 – 2005
Type of data:– Satellite insurance underwriting data: claims,
premiums, loss ratios, rates (minimum, maximum,
average) and capacity (the sum of the maximum
amounts that each underwriter is willing to provide
on one satellite for launch and in-orbit insurance);
number of launches; average satellite value
– Reinsurance data: number of reinsurers, surplus
– Macroeconomic data: interest rates, stock prices
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Introduction Aims The satellite insurance industry Hypotheses Data Methodology Results Conclusions
Agenda
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Underwriting Cycle Determination
– existence of the underwriting cycle: a second-order autoregressive model proposed by
Venezian (1985):
Pt = a0 + a1 Pt-1 + a2 Pt-2 + ωt,
tested variables: the minimum and the average rate, capacity, and loss ratio
A cycle is present if a1 > 0, a2 < 0 and (a1)2 + 4a2 < 0
– cycle periods:
2
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2cos
2
a
aT
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Hypothesis Testing
Regression models:
– Satellite insurance rate model:
– Satellite insurance capacity model:
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1 1 2 2 3 3
4 6
7 8
t t
t t t t
t
t t
Demand
Trend
Rate Loss ratio Loss ratio Loss ratio
Capacity Interest rate
New satellite value
1 1 2 2 3 3
4 5 6
7 8
Re
Re
t t tt
t t t
t t
Loss ratio Loss ratio Loss ratio
Rate
Capacity
Stock price Number insurers
insurers Surplus Trend
(+)
(-)(+)
(-)
(+/-)
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Introduction Aims The satellite insurance industry Hypothesis Data Methodology Results Conclusions
Agenda
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Cycle Test Results for Satellite Insurance (1968-2005)
Without trenda With trendb
Variable Cycle Period Cycle Period
Loss Ratio No N/A No N/A
Minimum Rate-on-Line Yes 13.73 Yes 12.36
Average Rate-on-Line No N/A Yes 17.26
Capacity Yes 25.85 Yes 10.84
aThe OLS equation estimated is Vt=a + a1Vt-1 + a2Vt-2 + et
bThe OLS equation estimated is Vt=a + a1Vt-1 + a2Vt-2 + a3Trend + et
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∆Min. Rate Regression Results (1972-2005)
OLS Results 3SLS ResultsCoeff. t-stat Coeff. z-stat
Intercept 0.0039 0.98 -0.0202 -2.27 **
∆Loss Ratio1 0.0086 1.98 * 0.0078 2.02 **
∆Loss Ratio2 0.0126 2.24 ** 0.0138 2.35 **
∆Loss Ratio3 0.0059 1.5 0.0072 1.69 *
∆Capacity -0.0002 -3.66 *** -0.0001 -1.93 **
∆Discount rate -0.0085 -2.46 ** -0.0086 -3.2 ***
∆No. of Launches 0.0004 0.9 0.0003 0.46
∆New Sat. Value -0.0001 -0.67 0.0001 0.62
Trend 0.0005 1.14 0.0008 1.44
R-squared 0.52 0.46
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∆ Capacity Regression Results (1972-2005)
OLS Results 3SLS ResultsCoeff. t-stat Coeff. z-stat
Intercept 27.03 0.97 25.96 1.08
∆Loss Ratio1 2.02 0.28 2.08 0.19
∆Loss Ratio2 -2.26 -0.21 -1.99 -0.14
∆Loss Ratio3 -1.49 -0.19 -1.42 -0.14
∆Minimum rate -1377.29 -3.57 *** -1465.60 -2.15 ***
∆Share Price 0.56 5.37 *** 0.57 4.51 ***
∆No. of Reinsurers -0.19 -0.09 -0.13 -0.11
∆Reinsurer Surp. -0.01 -0.39 0.00 -0.71
Trend -2.82 -1.64 -2.63 -1.76 *
R-Squared 0.64 0.64
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Introduction Aims The satellite insurance industry Hypothesis Data Methodology Results Conclusions
Agenda
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Confirmation of the existence of an underwriting cycle in satellite insurance market (for minimum rate-on-line, average rate-on-line, and capacity).
Cycle periods are relatively long (10 to 25 years) compared to the average six year cycle commonly cited in other studies.
Conclusions (1)
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Confirmation of the rational expectations/ institutional intervention hypothesis - a positive and significant relationship between the minimum-rate-on line and lagged loss ratios.
Confirmation of the capacity constraint theory - the minimum rate-on-line is negatively related to capacity (coverage availability).
Conclusions (2)
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Because of the unique data used in this study we are able to determine that the maximum coverage available in the satellite insurance industry and the rate-on-line are determined simultaneously.
Further, our results suggests, that changes in capacity appear to be relatively more responsive to changes in the minimum rate than the other way around.
Conclusions (3)
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THANK YOU FOR THANK YOU FOR
YOUR ATTENTIONYOUR ATTENTION
Contact:Contact:
mary.mary.weiss@temple.eduweiss@temple.edu
piotr.manikowski@ae.poznan.plpiotr.manikowski@ae.poznan.pl