Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
Risk Management of Natural Disasters on Insurance Stock
Market
T. Bouaricha
The impact of worst natural disasters is analysed in terms of insured losses which
happened between 2010 and 2014 on S&P insurance index. Event study analysis is used
to test whether natural disasters impact insurance index stock market price. There is no
negative impact on insurance stock market price around the disasters event. To analyse
the reaction of insurance stock market, Normal Returns (NR), Abnormal Returns (AR),
Cumulative Abnormal Returns (CAR), Cumulative Average Abnormal Returns (CAAR) and
a parametric test on AR and on CAR are used.
Keywords: Study Event, Natural Disasters, Insurance, Reinsurance, Stock Market
1. Introduction
The world faces unpredictable natural disasters. Due to various effects such as climatic
changes, catastrophes number is increasing. As insurance market is depending on it,
this leads to an increasing damage costs. Natural disasters impact the economy of a
country, tourism, stock market and insurance companies.
Over the last decade, the world and especially Insurance Companies had to face
massive uncontrollable natural disasters such as tsunami, floods, and earthquake which
are taken into account by insurers. To evaluate their impact the 5 most costly disasters
(in terms of insured damages) between 2010 and 2014 are examined. By using an event
study, the impact of a disaster on insurance industry is analysed by calculating various
financial indicators.
From 2010 to 2015, according to Swiss Re Sigma 2014 report, the most costly disasters
are presented as follow.
In March 2011, a massive earthquake hit Japan; it was most catastrophic earthquake in
Japan, and represented 37 B$ insured damaged. In October 2012, Sandy hurricane hit
US and Caribbean Island which lead to 36 B$ total insured damaged. In February 2012,
New Zealand earthquake costs 17 B$ insured damaged. In July 2011, Thailand floods
represented 16 B$ (insured damaged).
In July 2012, US Drought costs 11 B$ (insured damaged).
Section 2 provides the study context. Section 3 presents the data used in this study and
Section 4 presents methodology and results which have been used while Section 5
concludes this study.
2. Context
Natural disasters impact many areas such as tourism, economy, infrastructure,
manufacturing business, health and can cause physical damage by destruction and kill
people. Natural disasters (floods, hurricane, earthquake, tsunamis, storms, drought)
have immediate and long term effects on the population and the economy because it can
take
___________________________________________________________________
*Tarah Bouaricha, Undergraduate student, ECE Paris School of Engineering, France,
Email: b.tarah@gmail.com
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
time to rebuild infrastructure and to provide local food supply and medical healthcare.
The damaged losses from natural disasters have heavy cost. To manage it, insurers
cede a portion of the business to one or more reinsurer companies which help them to
mitigate losses from natural disasters by providing capital to economy and to reduce
their solvency risk. Here emphasis is put on the five most costly natural disasters from
2010 to 2014. Study before 2010 can biased the results due to 2007-2008 World
financial crisis. The value of insurance company should be impacted by natural disasters
event in particular due to high insured losses.
3. Data
The daily stock market prices have been taken from S&P Insurance index which
represents the largest insurance companies worldwide and the benchmark MSCI world
for the period of 2010 to 2015 from Yahoofinance.com.
To analyse the insurance and reinsurance areas, Standard & Poor’s Insurance historical
stock market price is used. S&P Insurance index is composed of 49 US Insurance/
Reinsurance firms. The study focuses on the 5 most costly damages occurred between
2010 and 2014. This period is chosen not to be affected by 2008-2009 financial crisis
period. Data are composed by collecting stock prices in the time interval around natural
disasters event. All data have been uploaded in Excel to be analysed by calculating
statistical indicators.
4. Results and Discussion
4.1 Objective of the Study:
The purpose of this study is to determine whether natural disasters impact insurer stock
prices. The 5 most insured damaging disasters between 2010 and 2014 are examined
by using an event study, and the insurance market response is compared to these
disasters. When the disasters occurred on a non-trading day, the event date is chosen
as the next trading day. The impact is analysed around the event date.
The event study has been analysed over 1 year scale. The daily return has been
calculated from closing data USD price of S&P Insurance index and MSCI World index
(market benchmark).
4.2 Methodology:
The methodology used to achieve the objective of this study is presented as follows.
The event analysis has been used to study whether natural disasters affect significantly
the value of insurance market.
Firstly, performance has been calculated. The time is settled for the event study.
Secondly, the abnormal return (AR) has been calculated for S&P Insurance index. Then
the Cumulative abnormal return (CAR) is derivate from the AR calculus.
The t-test test has been used to test whether cumulative abnormal returns of S&P
Insurance index is equal to zero.
A. Analysis S&P Insurance Index Performance
The behavioural evolution of S&P Insurance index has been compared to its market
benchmark MSCI world calculating the normalized price of each index on a daily basis:
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
(1)
Where:
- Si,t is the normalized price (basis 100),
- Pi,t is the stock market price at time t,
- Pn is the first index price.
Figure 1: S&P Insurance Index and MSCI world Stock Market Evolutions (20092015)
B. Event Definition
The first step is to define the event date, which corresponds to the day when the natural
disasters occurred. The timeline of event study is subdivided in 3 periods. The first one is
the event period which is composed of the natural disaster event day (t=0) and 10 days
before and after this event (t1,t2).
The second one is the gap period (T2,t1) which corresponds to a time period between
the event period and the estimation period.
The last one is the estimation period (T1,T2) before event and gap periods, with a
certain number of observed trading days depending on availability of historical data. The
longer is this period, the better will be the estimation of normal return to be the most
efficient as possible (no further impact which can affect this estimation).
Figure 2: Event Study Timeline
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
The 5 analysed natural disasters analysed in the event study are presented in Table 1.
Natural
Disasters
Japan
earthquake
Date
Event Period
Gap Period
11/03/2011
25/02 - 25/03
2 months
Sandy hurricane
New Zealand
earthquake
24/10/2012
10/10 - 09/11
2 months
22/02/2011
07/02 - 08/03
2 months
Thailand floods
27/01/2011
12/01 - 10/02
2 months
15/07/2012 *
29/06 - 30/07
2 months
US Corn Belt
Estimation
Period
22/12/2010 22/12/2009
07/08/2012 05/08/2011
02/12/2010 03/12/2009
10/11/2010 10/11/2009
25/05/2012 25/05/2011
Table 1: 5 Most Costly Natural Disasters in 2010-2014 Period
(*next trading day)
C. Return
The return has been calculated for S&P Insurance index and for reference market
benchmark as follows:
(2)
Where Ri,t is the return, Pi,t is the index price at time t.
D. Normal Return
The normal return of S&P Insurance index stock market is approximated by using Market
Adjusted Returns method which takes into account the market reference to have the
same behaviour. To judge the impact of returns, actual returns (the returns affected by
the natural disaster event) need to be compared with normal returns (the normal return is
the period when nothing happens which can impact significantly the return).
The model used to analyse the data of estimation period is CAPM which takes the risk
and the return of stock market. From Brown (1985),
E(Ri) = i +iRm,t
(3)
Where E(Ri) is the expected return (Normal Return), Ri is the daily return, Rm,t is the
daily market return (MSCI world) at time t, i is the intercept and βi is the slope from the
CAPM. Regression results indicate the normal return of S&P Insurance index. Display of
the results is summarized on Table 1 below.
Event E(Ri)
Japan Earthquake
i
3.104
i
1.0917
Hurricane Sandy
4.104
1.1237
New
Zealand
Earthquake
Thailand Flood
3.104
1.0945
2.104
1.0884
US
Corn
Drought
4.104
1.1176
Belt
Results
Table
F1
Table
F2
Table
F3
Table
F4
Table
F5
Table I. Display of Results for Considered Disasters
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
E. Abnormal return
The abnormal return is defined by:
The calculation is
(5)
F. T-test statistical
After calculation of the AR, the null hypothesis H0 : AR=0 is tested. It corresponds to the
situation where the natural disaster event has no impact on Insurance market price. On
the opposite H1: AR≠0 would mean that the natural disaster event has an impact on
Insurance market price.
The T-test method uses statistical general expression:
̅
(6)
√
Where s is the sample standard deviation, n is the sample size, µ is the overall average
value (AR), and ̅ is the sampling average value (AR). Parameter s is calculated as
follow:
√(
) ∑
(7)
Steyx in Excel is used as regression standard error to calculate the significance of
abnormal returns.
The test statistic for the abnormal return is:
(8)
It is assumed that regression residuals are normally distributed, and that AR is significant
at 95 percent level.
F1. Japan Earthquake Event
NR_S&P
AR_S&P T_test_S&P
DATE
Insurance Insurance Insurance
25/03/2011
0,2%
0,3%
0,36
24/03/2011
1,1%
-0,8%
-1,05
23/03/2011
0,1%
-0,3%
-0,36
22/03/2011
0,3%
-0,9%
-1,13
21/03/2011
1,8%
0,2%
0,23
18/03/2011
0,7%
0,1%
0,08
17/03/2011
1,7%
-1,2%
-1,65
16/03/2011
-1,0%
-0,2%
-0,28
15/03/2011
-2,4%
1,2%
1,65
14/03/2011
-1,1%
0,4%
0,48
Event Date
11/03/2011
0,2%
0,1%
0,12
10/03/2011
-2,1%
0,3%
0,45
09/03/2011
-0,1%
-0,1%
-0,14
08/03/2011
0,2%
1,0%
1,30
07/03/2011
-0,7%
0,1%
0,12
04/03/2011
-0,3%
-0,4%
-0,54
03/03/2011
1,2%
0,7%
0,91
02/03/2011
-0,2%
-0,9%
-1,23
01/03/2011
-0,8%
-0,7%
-0,98
28/02/2011
0,9%
-0,3%
-0,41
25/02/2011
1,2%
0,0%
0,06
Table F1: Results of NR(eq.3), AR(eq.5) and
T-test(eq.8) around Japan Earthquake Event
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
F2. Hurricane Sandy Event
Event Date
NR_S&P
AR_S&P T_test_S&P
DATE
Insurance Insurance Insurance
09/11/2012
0,0%
0,3%
0,42
08/11/2012
-1,0%
0,5%
0,54
07/11/2012
-1,9%
-0,7%
-0,79
06/11/2012
0,7%
0,5%
0,54
05/11/2012
-0,2%
0,4%
0,45
02/11/2012
-0,6%
-1,1%
-1,35
01/11/2012
1,0%
-0,4%
-0,51
31/10/2012
0,1%
-0,2%
-0,21
26/10/2012
-0,2%
-0,7%
-0,82
25/10/2012
0,4%
0,0%
0,04
24/10/2012
-0,1%
0,2%
0,20
23/10/2012
-1,7%
0,5%
0,63
22/10/2012
-0,1%
0,4%
0,42
19/10/2012
-1,4%
-0,1%
-0,10
18/10/2012
0,0%
0,4%
0,51
17/10/2012
0,9%
0,3%
0,32
16/10/2012
1,5%
-0,1%
-0,08
15/10/2012
0,6%
0,3%
0,35
12/10/2012
-0,2%
-0,6%
-0,75
11/10/2012
0,4%
0,2%
0,28
10/10/2012
-0,7%
0,8%
0,95
Table F2: Results of NR(eq.3), AR(eq.5) and T-test(eq.8) around Hurricane Sandy Event
F3. New Zealand Earthquake Event
NR_S&P
AR_S&P T_test_S&P
DATE
Insurance Insurance Insurance
08/03/2011
0,2%
1,0%
1,28
07/03/2011
-0,7%
0,1%
0,12
04/03/2011
-0,3%
-0,4%
-0,53
03/03/2011
1,2%
0,7%
0,89
02/03/2011
-0,2%
-0,9%
-1,21
01/03/2011
-0,8%
-0,7%
-0,96
28/02/2011
0,9%
-0,3%
-0,41
25/02/2011
1,2%
0,0%
0,06
24/02/2011
-0,2%
-0,3%
-0,43
23/02/2011
-0,6%
-0,4%
-0,50
Event Date
22/02/2011
-1,9%
-0,4%
-0,54
18/02/2011
0,3%
0,5%
0,71
17/02/2011
0,7%
0,1%
0,17
16/02/2011
0,7%
0,3%
0,34
15/02/2011
0,0%
0,4%
0,49
14/02/2011
0,3%
-0,9%
-1,21
11/02/2011
0,3%
0,6%
0,75
10/02/2011
-0,3%
0,0%
0,03
09/02/2011
-0,4%
0,1%
0,09
08/02/2011
0,7%
-0,4%
-0,51
07/02/2011
0,7%
0,3%
0,38
Table F3: Results of NR, AR and T-test around New Zealand Earthquake Event
F4. Thailand Floods Event
NR_S&P
AR_S&P T_test_S&P
DATE
Insurance Insurance Insurance
10/02/2011
-0,3%
0,0%
0,04
09/02/2011
-0,4%
0,1%
0,10
08/02/2011
0,7%
-0,4%
-0,48
07/02/2011
0,7%
0,3%
0,40
04/02/2011
0,2%
0,7%
0,85
03/02/2011
-0,2%
0,5%
0,62
02/02/2011
0,2%
-1,1%
-1,42
01/02/2011
1,9%
0,0%
-0,03
31/01/2011
0,5%
-0,4%
-0,57
28/01/2011
-1,5%
-0,6%
-0,82
Event Date
27/01/2011
0,3%
0,5%
0,66
26/01/2011
0,6%
-0,4%
-0,53
25/01/2011
-0,1%
0,7%
0,95
24/01/2011
0,8%
-0,3%
-0,45
21/01/2011
0,6%
-0,6%
-0,71
20/01/2011
-1,1%
1,2%
1,59
19/01/2011
-0,6%
-0,8%
-1,01
18/01/2011
0,6%
-0,5%
-0,69
14/01/2011
0,3%
0,3%
0,44
13/01/2011
0,6%
-1,0%
-1,23
12/01/2011
1,4%
-0,8%
-1,01
Table F4: Results of NR(eq.3), AR(eq.5) and T-test(eq.8) around Thailand Flood Event
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
F5. U.S Corn Belt Drought
Event Date
NR_S&P
AR_S&P T_test_S&P
DATE
Insurance Insurance
Insurance
30/07/2012
0,4%
-0,1%
-0,14
27/07/2012
2,1%
-0,3%
-0,32
26/07/2012
2,5%
-1,7%
-1,86
25/07/2012
-0,1%
0,4%
0,38
24/07/2012
-0,8%
-0,3%
-0,37
23/07/2012
-1,7%
1,0%
1,03
20/07/2012
-1,4%
-0,3%
-0,36
19/07/2012
0,8%
-1,2%
-1,28
18/07/2012
0,9%
-0,7%
-0,79
17/07/2012
0,4%
0,3%
0,28
16/07/2012
0,0%
-0,8%
-0,82
13/07/2012
1,7%
0,1%
0,06
12/07/2012
-1,0%
0,3%
0,36
11/07/2012
0,0%
0,0%
-0,01
10/07/2012
-0,4%
-0,2%
-0,23
09/07/2012
-0,4%
-0,3%
-0,32
06/07/2012
-1,1%
0,4%
0,43
05/07/2012
-0,8%
-0,2%
-0,18
03/07/2012
1,0%
0,0%
0,00
02/07/2012
0,5%
-0,1%
-0,08
29/06/2012
3,4%
-1,2%
-1,26
Table F5: Results of NR(eq.3), AR(eq.5) and T-test(eq.8) around U.S Corn Belt Drought Event
The significance level of the T-test is 5 percent level and 20 degrees of freedom.
According to T table, the t0.05;20 value is 1.725.
As seen in Tables F1 to F5, for the 5 natural disasters event, t ≠0, so the null hypothesis
should be rejected. Moreover, t value is never less than -1.725 and greater than 1.725
which means that studied natural disasters did not impact S&P Insurance stock market
price. A further statistical T-test will be performed with the CAR in the following part to
confirm this result.
G. Cumulative Abnormal Returns
The cumulative abnormal returns calculated for the event period (t1,t2) is given by:
∑
(9)
Using the CAR value, a test is performed to identify the CAR on the stock.
H0: CAR=0 corresponding to the case where the natural disaster event has no impact on
Insurance market price.
H1: CAR≠0 where the natural disaster event has an impact on Insurance market price.
The statistic t-test is calculated with:
(10)
Where SD is the standard deviation of AR of the stock, and N is the number of trading
days (in the event period)
The significance level of the T-test is 5 percent level and 20 degrees of freedom.
According to T-table, the t0.05;20 value is 1.725. If t ≠0, the null hypothesis is rejected. If
t is less than -1.725 or greater than 1.725, a significant impact on insurance stock market
is due to the natural disaster event.
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
G1. Japan Earthquake
Figure 1: Cumulative Abnormal returns of S&P Insurance index around Japan Earthquake Event
Event Date
CAR_S&P T_test_S&P
DATE
Insurance
Insurance
25/03/2011
-1,5%
-0,54
24/03/2011
-1,8%
-0,64
23/03/2011
-1,0%
-0,36
22/03/2011
-0,7%
-0,26
21/03/2011
0,1%
0,05
18/03/2011
0,0%
-0,02
17/03/2011
-0,1%
-0,04
16/03/2011
1,1%
0,40
15/03/2011
1,3%
0,48
14/03/2011
0,1%
0,04
11/03/2011
-0,3%
-0,09
10/03/2011
-0,3%
-0,12
09/03/2011
-0,7%
-0,24
08/03/2011
-0,6%
-0,20
07/03/2011
-1,6%
-0,55
04/03/2011
-1,6%
-0,58
03/03/2011
-1,2%
-0,44
02/03/2011
-1,9%
-0,68
01/03/2011
-1,0%
-0,36
28/02/2011
-0,3%
-0,09
25/02/2011
0,0%
0,02
Table G1: Results of Cumulative Normal Return and T-test
G2. Hurricane Sandy Event
Figure 2: Cumulative Abnormal returns of S&P Insurance Index around Hurricane Sandy Event
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
CAR_S&P T_test_S&P
DATE
Insurance Insurance
09/11/2012
0,9%
0,41
08/11/2012
0,6%
0,25
07/11/2012
0,1%
0,04
06/11/2012
0,8%
0,34
05/11/2012
0,3%
0,14
02/11/2012
-0,1%
-0,03
01/11/2012
1,1%
0,48
31/10/2012
1,5%
0,67
26/10/2012
1,7%
0,75
25/10/2012
2,4%
1,06
Event Date
24/10/2012
2,3%
1,04
23/10/2012
2,2%
0,97
22/10/2012
1,6%
0,73
19/10/2012
1,3%
0,57
18/10/2012
1,4%
0,61
17/10/2012
0,9%
0,41
16/10/2012
0,6%
0,29
15/10/2012
0,7%
0,32
12/10/2012
0,4%
0,19
11/10/2012
1,0%
0,47
10/10/2012
0,8%
0,36
Table G2: Results of Cumulative Normal Return and T-test
G3. New Zealand Earthquake Event
Figure 3: Cumulative Abnormal returns of S&P Insurance index around New Zealand Earthquake Event.
CAR_S&P T_test_S&P
DATE
Insurance Insurance
08/03/2011
-0,8%
-0,33
07/03/2011
-1,7%
-0,75
04/03/2011
-1,8%
-0,79
03/03/2011
-1,4%
-0,61
02/03/2011
-2,1%
-0,90
01/03/2011
-1,2%
-0,51
28/02/2011
-0,4%
-0,19
25/02/2011
-0,1%
-0,06
24/02/2011
-0,2%
-0,08
23/02/2011
0,1%
0,06
Event Date
22/02/2011
0,5%
0,23
18/02/2011
0,9%
0,40
17/02/2011
0,4%
0,17
16/02/2011
0,3%
0,12
15/02/2011
0,0%
0,00
14/02/2011
-0,4%
-0,16
11/02/2011
0,6%
0,24
10/02/2011
0,0%
0,00
09/02/2011
0,0%
-0,01
08/02/2011
-0,1%
-0,04
07/02/2011
0,3%
0,13
Table G3: Results of Cumulative Normal Return and T-test
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
G4. Thailand Floods Event
Figure 4: Cumulative Abnormal Returns of S&P Insurance index around Thailand Flood Event
CAR_S&P T_test_S&P
DATE
Insurance Insurance
10/02/2011
-2,6%
-0,91
09/02/2011
-2,6%
-0,93
08/02/2011
-2,7%
-0,95
07/02/2011
-2,3%
-0,82
04/02/2011
-2,6%
-0,93
03/02/2011
-3,3%
-1,17
02/02/2011
-3,8%
-1,34
01/02/2011
-2,7%
-0,95
31/01/2011
-2,6%
-0,94
28/01/2011
-2,2%
-0,78
Event Date
27/01/2011
-1,6%
-0,56
26/01/2011
-2,1%
-0,74
25/01/2011
-1,7%
-0,59
24/01/2011
-2,4%
-0,85
21/01/2011
-2,1%
-0,73
20/01/2011
-1,5%
-0,53
19/01/2011
-2,7%
-0,97
18/01/2011
-1,9%
-0,69
14/01/2011
-1,4%
-0,50
13/01/2011
-1,7%
-0,62
12/01/2011
-0,8%
-0,28
Table G4: Results of Cumulative Normal Return and T-test
G5. U.S Corn Belt Drought
Figure 5: Cumulative Abnormal Returns of S&P Insurance Index around U.S Corn Belt Drought Event
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CAR_S&P T_test_S&P
DATE
Insurance Insurance
30/07/2012
-3,7%
-1,34
27/07/2012
-3,6%
-1,29
26/07/2012
-3,3%
-1,18
25/07/2012
-1,5%
-0,55
24/07/2012
-1,9%
-0,68
23/07/2012
-1,5%
-0,56
20/07/2012
-2,5%
-0,91
19/07/2012
-2,2%
-0,78
18/07/2012
-1,0%
-0,35
17/07/2012
-0,2%
-0,08
16/07/2012
-0,5%
-0,18
13/07/2012
0,3%
0,10
12/07/2012
0,2%
0,08
11/07/2012
-0,1%
-0,04
10/07/2012
-0,1%
-0,04
09/07/2012
0,1%
0,04
06/07/2012
0,4%
0,15
05/07/2012
0,2%
0,09
03/07/2012
0,2%
0,09
02/07/2012
0,2%
0,06
29/06/2012
-1,0%
-0,37
Table G5: Results of Cumulative Normal Return and T-test
Table G1 to G5 shows that I can’t reject the null hypothesis because none of the t test
value is less than -1.725 or greater than 1.725. That means that none of any natural
disasters studied affects the S&P Insurance stock market.
As seen in Figures 1 to 5, the CAR of S&P Insurance index did not react to any of
studied natural disaster events. It means that none of them had an impact on S&P
insurance stock market price. Further analysis will prove this hypothesis.
H. Cumulative Average Abnormal Returns
In this part, the cumulative average abnormal returns (CAAR) of S&P Insurance index is
also calculated. The expression is:
( ) ∑
(10)
Event
Japan earthquake
Sandy hurricane
New Zealand earthquake
Thailand floods
US Corn Belt
CAAR_S&P
Insurance
-12%
22%
-7%
-47%
-21%
Table 2: CAAR of S&P Insurance Index around Natural Disasters Event.
As seen in Table 2, CAAR of S&P Insurance index is negative for Japan earthquake,
New Zealand earthquake, Thailand floods, US Corn Belt event that means that those
events have a negative effect S&P Insurance stock market but not a significant one as
seen on T-test results. Meanwhile Sandy hurricane has a positive effect on S&P
Insurance stock market as prove on T-test results.
5. Conclusion
This paper examines the effect of five worst natural disasters occurred between 2010
and 2014 on S&P insurance stock market price. The results show that there was no
impact on S&P Insurance. To perform a test on AR and CAR results a statistic T-test has
been used. The AR and CAR do not show any impact from the 5 considered
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
catastrophes. This is explained by the high enough level of reinsurance coverage
insurance companies have up to now been globally subscribing to face the occurrence of
possible disastrous events, and is a strong element in favour of seriousness of their risk
management. However further studies are necessary to study in more detail the stock
market price of each insurance and reinsurance company from various countries.
Acknowledgments
The author is very much indebted to ECE Paris School of Engineering to have provided
the environment where the work has been developed and Pr M. Cotsaftis for discussion
and help in preparation of the manuscript.
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