Martin Kostov
S2275244
The impact of hurricane Katrina on
insurance companies and their abnormal
returns. Do natural disasters cause any
distortion of prices, insurance companies
charge and urge for the demand of higher
premiums due to risk compensation?
Bachelor Thesis
Economics and Business Economics
University of Groningen, The Netherlands
Martin Kostov (s2275244)
Abstract
The main objective of this paper is to research the impact of natural hazards, such as Hurricane
Katrina on the abnormal returns of insurers and their market prices. Overall, I examine the
returns of multiple US firms situated in the infected area by the event and test, whether they
have been affected by the hurricane storm significantly. Furthermore, I have included 110
insurance companies all of which located in the US area and established an overview of their
abnormal returns to see the implications of Hurricane Katrina on their stocks. All the relevant
data has been collected from Datastream. Based on the results found, there seem to appear
statistically significant positive abnormal returns during the day of the disaster. From this we
can infer that Hurricane Katrina caused insurance prices to increase significantly that time and
that insurance companies began to demand higher rate premiums in order to be compensated
for the risks.
Keywords: Hurricane, abnormal returns, insurance companies, Event study methodology
University of Groningen – FEB – Bachelor Thesis for E&BE, BE track – Supervised by Dr. V. Purice
Martin Kostov
S2275244
1. Introduction
Hurricane Katrina was the largest and third strongest hurricane in the US. Costs in the aftermath
reached $81 billion in property damages, however it has been estimated that the total damage
in Mississippi and Louisiana may exceed $150 billion, which makes it the costliest hurricane
in the US history. The disaster took place about 322km southeast of the Bahamas on August
23, 2005. It was rated as a Category 5 hurricane, the size of it was 6 meters high and the intensity
of its wind speed was up to 175 mph. The hurricane took the life of approximately 1836 people,
with more than half of these victims being senior citizens. It was rated 3rd in the deadliest
hurricanes in the US history with the first being Galveston (1900) with 8000-12000 deaths and
Okeechobee (1928) with above 2500 deaths being second. The range of the affected area by the
disaster was 90000 square miles. A tremendous amount of infrastructure was destroyed, and
whole New Orleans was under water after the hurricane made landfall. Furthermore, it affected
a considerable amount of 15 million people in ways such as having to evacuate their homes,
skyrocketing gas prices due to destroyed major refineries in the Gulf coast, and staggering the
economy. The event caused hundreds of thousands of local residents to become unemployed
and affected at least 1 million non-farm jobs. In other words, mass unemployment become a
huge factor. In the end more than 70 countries helped out with monetary donations or other
kind of assistance after the disaster. One of the largest single donation was made by Kuwait and
the amount was $500 million. Countries such as India Bangladesh Qatar and Pakistan also
contributed with considerable donations.
There have been many studies conducted about hurricanes and natural disasters for that matter.
However, in this research I would like to particularly concentrate the focus on the impact of
Hurricane Katrina on the abnormal returns of insurance companies and the effect of it on the
prices charged by insurers after the shocks in the inflicted area. More particularly, I want to
analyze the return fluctuations prior to the event and examine if there is any interconnection of
scientific and media information on the market value of insurance firms by the use event study
methodology. The reason to investigate this kind of event is to observe its impact on insurers
and the direct and indirect effect on firms affected by the hurricane. I find that there is indeed
impact on the insurance companies that I have encountered. Such a catastrophic event caused
insurers to demand higher prices for their services due to the increase of risks insurance
companies are to bear and the increased occurrence of such events recently.
The paper is structure accordingly, with section 2 being the literature review, directly followed
by section 3 representing the hypothesis of my research. Section 4 is composed of the
methodology for the event study. The data collection is provided in section 5. Last but not least,
follows section 6 in which situated are the empirical results of the research paper. Finally, the
conclusion is taking place in the last part of the paper, namely section 7.
Martin Kostov
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2. Literature review
There has been done a lot of research on how natural disasters impact stock volatility of
organizations and generally how catastrophes affect the market. However, the issue of hurricane
Katrina and its consequences on insurance companies have not been analyzed to a full extent
yet. If previous studies were to be considered, one would expect stock prices of insurance
companies after the disaster to spike. Undoubtedly, throughout the years natural disasters have
raised attention to the eyes of many researchers to conduct analysis and scrutinize the effects
of them on the stock market. An article written by Christian Thomann (2013) concerns the idea
of natural catastrophes and their impact on insurer stock volatility and the correlation of
insurance stock with the market. The author found out that natural catastrophes tend to increase
the volatility of insurance stocks and reduce the correlation of insurance stocks with the market.
A research has been conducted by Bradley T. Ewing, Scott E. Hein & Jamie Brown Kruse
(2006) about insurance firm’s price responses to Hurricane Floyd. The authors shed light on the
question by doing an event study analysis using Storm characteristics. More importantly, the
paper researches the effect of Hurricane Floyd and the interconnected scientific and media
information on the market value of insurance companies by using event study methodology.
The paper distinguishes itself by specifying the development of the disaster over the time and
space is also included to explain how the market reacts to changing news about the event’s
characteristics. The study shows that there was a negative effect on insurance firm’s stock price
changes in the life cycle of the storm. On the other hand, the effect was not the same and was
not at all times negative on every day of the cycle. The results state that significant market
reaction to the news about the path and strength of the natural disaster before the landfall took
place.
Another study of Patrica H. Born & Barbara Klimaszewski-Blettner (2013) concentrates on the
main factors that drive insurers’ willingness to provide coverage in catastrophe-prone property
insurance lines. Furthermore, the authors analyze the supply decisions in commercial and
personal lines, with regard to insurers’ responses in the aftermath of natural disasters. The
results of the study, suggest that important policy implications concerning the improvement of
the availability of insurance against catastrophic threats is a must. Regarding the implications
of regulatory constrains the study offers empirical evidence on the subject that certain
regulatory responses may involuntary decrease insurers’ willingness to offer coverage against
natural disasters.
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What is more, an article by Andrew Worthington and Abbas Valadkhani (2004) measures the
impact of natural disasters on capital markets by conducting an empirical application via
intervention analysis. The study uses data from 42 events of storms, cyclones, earthquakes,
floods and bushfires and examines 4958 observations between the period of December 1982
and January 2002 to survey daily prices and accumulation returns for the All Ordinaries Index
(AOI). The authors have used an Autoregressive moving average (ARMA) model in order to
examine the returns and the incorporation of news arrival regarding natural disasters and
intervention analysis is used as specification. The findings disclose that cyclones, earthquakes
and bushfires have a severe effect on market returns, while floods and storms do not show such
an impact on the stock market.
Furthermore, a research made by Bjoern Hagendorff, Jens Hagendorff and Kevin Keasey
(2015) investigated the impact of Mega-Catastrophes on Insurers. More importantly, the
authors conducted an exposure-based analysis of the U.S. homeowners’ insurance market.
Moreover, due to the increase in the recent occurrence of natural disasters, this study aims to
examine the performance effects of mega-catastrophes for U.S. insurance companies by using
market expectation measures. In particular, the authors examine the share price losses of
insurance organizations in response to natural disaster events in order to provide evidence
whether mega-catastrophes significantly damage the performance of insurance firms and
whether different types of mega-catastrophes have distinct impacts. Overall, the results show
that the impact of mega-catastrophes on insurers is not that devastating. Whereas, the exact
effect of natural disasters depends on the type of the event and the certain degree of competition
within the given insurance market, by bearing in mind that less competition allows insurance
firms to recoup losses through charging appropriate premiums. The findings of the study prove
that U.S. insurers can adaptively cope and manage with risk and costs regarding megacatastrophes. Indeed, results present evidence that insurance provides a robust means of sharing
natural disaster losses to help and weaken the financial consequences of a catastrophe event.
3. Hypotheses
Based on the understandings of hurricane catastrophes and the previously conducted studies
concerning such hazards, this article forms the following hypotheses:
Null Hypothesis 1: There was no impact of abnormal returns on insurance companies in the
US market caused by the Hurricane Katrina.
Alternative Hypothesis 1: There was an impact of abnormal returns on insurance companies
in the US market caused by the Hurricane Katrina.
Null Hypothesis 2: Due to the storm insurance companies did not adjusted higher premiums
for the risks of their services.
Alternative Hypothesis 2: Due to the storm insurance companies did adjusted higher
premiums for the risks of their services.
Martin Kostov
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4. Data and descriptive statistics
For my event study analysis, I have collected data for 110 insurance companies, all of which
situated in the United States. The source from which I download the data is “Datastream”. All
of the firms are listed on the NASDAQ. I compare the abnormal returns of all the companies
with the total market return index in order get a broad and clear look at the effect of the storm
on the returns of all the firms concerned from the hurricane shock. The research includes the
returns of insurance firms in the US area for a 252 day period prior to the storm and 5 days after
it has made landfall (23rd September 2005).
After gathering all the necessary data for the event purposes, the following formula is used to
calculate the returns in question
∆𝑅𝐼 =
(𝑅𝐼¹ − 𝑅𝐼°)
𝑅𝐼°
The purpose of this formula is to find the difference between the return in day 1 and day 0 and
divide it by the return of day 0 in order to calculate the percentage change of the total return
index in the span of the two days.
In general, the data collected included 172 insurance companies in total, but due to some
necessary limitations in order to make the research less biased and more concrete I had to
exclude some companies that were delisted before the hurricane or became listed less than six
months prior to the storm. Therefore, after omitting for several observations and clearing out
the unnecessary outliers, the total adjusted number of insurance companies located in the US
for the analysis became 110.
5. Methodology
Generally the market responses are represented by examining whether the companies have
recorded any significant or substantial abnormal returns (AR) on the day of the event taking
place. What is more, a period prior to the hazard happening is being examined and the volatility
of returns is being evaluated and compared to the market total return index to observe if there
is substantial movements of the stock returns in the given period. Respectfully, if shareholders
of insurance companies react favorably to the event, we would expect abnormal returns to be
positive over the specified period and accordingly negative abnormal returns would be expected
Martin Kostov
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if otherwise. Particularly in this paper, abnormal returns serve the purpose of assessing the
financial market’s reaction to hurricane Katrina.
In this research I will try to measure an event’s economic impact by using market returns viewed
in a specific time period. In actuality, the methodology will use financial market data to decide
the abnormal return for a stock and isolate the effect of the hazard by adjusting for when the
disaster occurred. Generally, a normal return could be viewed as capturing the whole
quantifiable risk in the value of the stock. To be precise, abnormal performance is defined by
returns that are viewed as statistical outliers to the distribution. Therefore, normal actual
performance can be compared to abnormal performance driven by the hazard in question.
(Campbell et al. 1997) Undoubtedly, by using the event study methodology, the results has
shown that the market value of insurers has been affected by natural disasters, such as a
hurricane. In a study Lamb (1998) has discovered that in the year 1992 when the south of
Louisiana and Florida were hit by Hurricane Andrew, the companies with exposure in these
areas experienced a decrease in their stock returns. However, this does not come as a shock due
to the mass destruction such natural disasters cause and the following insured losses.
The use of daily returns of the companies included in the sample is used for the purpose of the
market model. In this analysis I have set an estimation period of one year and an event window
including the day of the event and five days after the event day. What is more, the interpretation
of the relationship between the returns of a stock j, Rjt and the return of the market, Rmt is
structured in the following way:
Rjt = α + β*Rmt + et, E(et) = 0, Var(et) = σ2e
α = daily return (not a result of movements of the market)
β = proportion of the return that is caused by market movements
e = portion of daily return not caused by market movements or firm’s daily return
In the estimation period concerning the event, namely the Hurricane Katrina, I estimate the
deviation between the daily return of given insurers and their expected daily return, all derived
from the equation above, which gives the abnormal returns for each company. The abnormal
returns are calculated with the following formula:
𝐴𝑅 = 𝑅̂𝑖 − 𝛼̂𝑖 − 𝛽̂𝑖 𝑅𝑀
𝑅̂𝑖 = Estimated return on the security
𝛼̂𝑖 = Estimated intercept
𝛽̂𝑖 = Estimated slope
𝑅𝑀 = Return on the market
Furthermore, next step is to calculate the average abnormal returns (AAR), which is done by
estimating the average of the abnormal returns of each security. Moreover, to get a clear
conclusion for the event, the average abnormal returns (AAR) are all combined in order to
estimate the cumulative average abnormal returns (CAAR). Consequently, the given
cumulative average abnormal returns (CAAR) is to be tested within the appropriate confidence
Martin Kostov
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intervals and evaluated whether the average abnormal returns have a significant difference from
zero on the day the event has made its landfall by executing a t-test. The formula used for a ttest is the following:
AAR: 𝑡 − 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 =
CAAR: 𝑇 − 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 =
𝐴𝐴𝑅𝑖𝑡
𝜎(𝐴𝑅)𝑖
𝐶𝐴𝐴𝑅𝑖
√𝑁∗𝜎 (𝐴𝑅)𝑖
A red flag in the estimation of the AAR and CAAR is the precise day of the event happening,
in other words the reaction of the stocks might arise a day or two after the landfall of the
hurricane Katrina, which calls for setting an event window of a range 20 days [-10;+10] around
the exact day the storm has marked its occurrence.
The following step is to decide whether the abnormal returns are significantly different from
zero. In order to form a valid understanding we use the t-statistic test by dividing the AAR and
CAAR by their standard deviations. If the estimated values are significant at 1% confidence
interval (2.576), 5% confidence interval (1.96) or 10% confidence interval (1.645) then the
given null hypothesis is rejected and we can concur that there was an impact of abnormal returns
on insurance companies in the US market caused by the Hurricane Katrina and also insurance
companies did adjusted higher premiums for the risks of their services because of the raging
storm.
6. Results
From the results we can see that on the day of the striking event we observe a positive expected
return and the average abnormal return shows a value of 4,73%. Furthermore, by performing
an AR t-test, the value of -2.03 is displayed in the result section. What is more, by testing for
whether it is significant or not with a 5% confidence interval (1.96), the results show that indeed
it is significant. Therefore the null hypothesis is rejected and the statement that, there was an
impact of abnormal returns on insurance companies in the US market caused by the Hurricane
Katrina, is true.
Additionally we examine the cumulative abnormal returns in a 5-day interval after the storm to
capture a more clear result of the effect, because it might take a day or two for the stocks of the
insurers and reinsurers to adjust to the news of the new information. In this case we observe
that the cumulative abnormal returns increase steadily in the following days, which means that
insurance companies are trying to decrease their exposure. Moreover, if we look at the returns
for 15-Day CAR of Table 1, we observe that the returns 10 days prior the event day show the
gradual movement of the cumulative abnormal returns in the industry. What is more, on the
Martin Kostov
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chart of Graph 1, there is a drastic decrease of the average abnormal returns (AAR) value
(0.0473) for the industry on the day of the storm, and for the abnormal returns (AR) it is
(0.0294).
Table 1.
AR
9.9.2005
12.9.2005
13.9.2005
14.9.2005
15.9.2005
16.9.2005
19.9.2005
20.9.2005
21.9.2005
22.9.2005
23.9.2005
26.9.2005
27.9.2005
28.9.2005
29.9.2005
30.9.2005
3.10.2005
4.10.2005
5.10.2005
6.10.2005
7.10.2005
AAR
AR t-test Significant?
4,47%
4,47%
3,09 No
1,41%
5,87%
0,97 No
-0,58%
5,29%
-0,40 Yes
-1,92%
3,38%
-1,33 Yes
0,68%
4,06%
0,47 No
-0,20%
3,86%
-0,14 Yes
1,53%
5,39%
1,06 No
-0,83%
4,56%
-0,57 Yes
3,23%
7,79%
2,24 No
-0,12%
7,67%
-0,08 Yes
-2,94%
4,73%
-2,03 Yes
-0,81%
3,92%
-0,56 Yes
1,47%
5,39%
1,02 No
0,93%
6,32%
0,65 No
-0,12%
6,20%
-0,09 Yes
-0,27%
5,93%
-0,18 Yes
-1,19%
4,74%
-0,82 Yes
1,99%
6,73%
1,37 No
-0,75%
5,98%
-0,52 Yes
-1,70%
4,28%
-1,18 Yes
-0,51%
3,77%
-0,35 Yes
5-day
15 day
CAR
CAR
CAR
CAAR
3,77%
3,77%
3,77%
-0,69%
3,08%
3,43%
-2,10%
0,98%
2,61%
-1,52%
-0,54%
1,82%
0,40%
-0,15%
1,43%
-0,28%
-0,43%
1,12%
-0,08%
-0,52%
0,88%
-1,62%
-2,13%
0,51%
-0,79%
-2,92%
0,13%
-4,02%
-6,94%
-0,58%
-3,90%
-3,90%
-10,84%
-1,51%
-0,96%
-4,86%
-11,80%
-2,37%
-0,15%
-5,01%
-11,95%
-3,11%
-1,61%
-6,62%
-13,56%
-3,85%
-2,55%
-9,17%
-16,11%
-4,67%
-2,42% -11,59%
-18,53%
-5,54%
-2,16%
-0,97%
-2,96%
-2,21%
-0,51%
Martin Kostov
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Graph 1.
Название диаграммы
0,04
0,02
0
-0,02
-0,04
-0,06
-0,08
-0,1
-0,12
-0,14
(E) r
AR
AAR
7. Conclusions
Generally, based on the analysis performed, we can concur that there is the implied effect of
rising prices for insurance in the United States after Hurricane Katrina. In conjunction with
Martin Kostov
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the previous Hurricane Rita that has struck the year before Katrina makes the effect even
bigger. It is important to note that some of the market impact is immediate and some occurs
gradually because the models are adjusted to take into account both of the hurricanes.
Understandably, the catastrophe causes commercial insurance terms and conditions to be
more regulated because of the fact that insurance companies wish to minimize their exposure.
More to that, there is the chance of availability constraints due to the fact that insurers and
reinsurers readjust their exposures to risks. Furthermore, insurers of insurance companies will
also be affected by hurricane Katrina.
. The reinsurance markets will also be affected. We expect that the property per-risk and property
catastrophe reinsurance market will harden, and capacity may be reduced somewhat. To the extent
that reinsurers rely on retrocessional protection, they will experience higher prices and less capacity
to support them. Like the associated primary market, the marine and energy reinsurance market will
be particularly problematic as the players reevaluate their appetite for this sector.
8. References
Andrew Worthington &Abbas Valadkhani (2004). Measuring the impact of natural disasters
on capital markets: an empirical application using intervention analysis. Applied Economics,
Vol 36, Issue 19, pages 2177-2186
Martin Kostov
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Bjoern Hagendorff, Jens Hagendorff & Kevin Keasey (2015), The impact of MegaCatastrophes on Insurers: An Exposure-based Analysis of the U.S. Homeowners’ Insurance
Market. Risk Analysis, Vol 31, Issue 1, pages 157-173
Bradley T. Ewing, Scott E. Hein & Jamie Brown Kruse (2006), Insurer Stock Price Responses
to Hurricane Floyed: An event Study Analysis using Storm Characteristics, American
Meteorological Society, Vol 21, Issue 3, p. 395
Christian Thomann (2013). The impact of Catastrophes on insurer stock volatility, Journal of
Risk and Insurance, Vol.80, Issue 1, pp. 65-94
Lazuras A. Angbazo & Ranga Narayanan (1996). Catastrophic shock in the Property-Liability
insurance industry: Evidence on Regulatory and Contagion Effects, The journal of risk and
insurance, Vol 63, No.4, pp. 619-637
Patrica H. Born & Barbara Klimaszewski-Blettner (2013), Should I stay or should I go? The
impact on natural disasters and regulation on U.S. property insurers’ supply decisions. The
journal of risk and insurance, Vol 21, Issue 1, pages 1-36
Impact of Hurricane Katrina on the insurance industry, Towers Watson, n.d. Web. October
2005.< https://www.towerswatson.com/en/Insights/IC-Types/Ad-hoc-Point-ofView/Perspectives/2005/impact-of-hurricane-katrina-on-the-insurance-industry>
<https://www.dosomething.org/us/facts/11-facts-about-hurricane-katrina>
http://www.livescience.com/22522-hurricane-katrina-facts.html
Martin Kostov
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http://americanhurricanes.weebly.com/analysis.html
9. Appendix
10.
Appendix
11. Note: 10%, 5% and 1% significance levels are represented by, respectively, 1), 2) and 3)
Flooding in New Orleans after Hurricane Katrina.
Credit: NWS/Lieut. Commander Mark Moran, NOAA Corps, NMAO/AOC- See more at:
http://www.livescience.com/22522-hurricane-katrina-facts.html#sthash.TsqI39AR.dpuf