THE IMPACT OF NATURAL DISASTER ON STOCK MARKETS

Author: Kevin Brosseau

A Master Research Project Submitted to Ipag Business School, Paris, France in Partial

Fulfillment of the Requirements for the Degree of Master

Written for Master in Finance & Markets under the direction of Khaled Guesmi

April 2016, Paris, France

Acknowledgements

I would like to thank Pr. Khaled Guesmi, Professor of Finance at Ipag Business School for

wisely advised me throughout this project, as to the relevant areas to be developed on the

subject of memory and for helping me on issues related to methodology. I also would like to

express my appreciation to Pr. Duc Khuong Nguyen, Deputy Director for Research and

Director of the Master in Finance and Markets at Ipag Business School, for his implication in

my studies.

My thanks also go to the 5th year of coordination team, our program managers, to monitor

them throughout the year.

Finally, I thank more generally Ipag Business School that allowed me during this year to

diversify my knowledge in corporate finance and financial markets, throughout a research

program.

I would also like to introduce this subject by two citations of a Sociologist and Philosopher,

Edgar Morin, whose work on the complex thought, conduct my reflections and my work.

These following raise questions related to the topic of this memory.

“Une prévision statistique avant la naissance de l'univers aurait considéré celui-ci comme

quasi impossible.”

“Il y a moins de désordre dans la nature que dans l'humanité.”

Edgar Morin

i

THE IMPACT OF NATURAL DISASTER ON FINANCIAL MARKETS

Kevin Brosseau

Abstract

April, 2016

Extreme events represent a threat to financial markets. Since the 2008 financial crisis, issues

of regulation and risk prevention become unavoidable. To be expected and managed

correctly, the behavior of impacts of these extreme events must be described and measured.

Growth in number and power of natural disasters today brings a new type of risk to be taken

into consideration on the financial market. In this study, we focus on impact of natural

disaster on financial markets all over the world. An investigation of the actual statement of the

literature review is engaged to understand how theses phenomenon are measured and by

which econometrical means. Daily average abnormal return, cumulative abnormal return and

average cumulative abnormal return are calculated to perform the test and analyze the reaction

of the stock markets. Consistent with the literature, this paper finds that a negative shock

brought by this catastrophic natural disaster exists. But this impact is surprisingly small.

Under the statistic t-test, the shock on all of the six stock markets is statistically insignificant.

But for some individual stocks, this earthquake shows a significantly impact. The impact is

either positive or negative. The direction of impact will depend on the industries that the

company is involved in.

Key words and phrases: Natural Disaster, Stock Market, Efficiency, Event Study

Methodology, Intervention Analysis, GARCH

ii

Table of Contents

Acknowledgements......................................................................................................................i

Abstract.......................................................................................................................................ii

Table of Contents......................................................................................................................iii

Chapter 1 Introduction...............................................................................................................1

1. Background.....................................................................................................................1

2. Objectives.......................................................................................................................2

3. Chapter organization.......................................................................................................2

Chapter 2 Literature Review......................................................................................................4

1. Efficient Market Hypothesis...........................................................................................4

2. The case of natural disaster & the evolution of financial literature trough the time......5

3. Return adjustment studies...............................................................................................7

4. Return and Volatility adjustment studies......................................................................12

Chapter 3 Conclusion...............................................................................................................15

References..................................................................................................................................iv

iii

Chapter 1

Introduction

1. Background

A natural disaster is a natural event, suffered and brutal, which causing major upheavals that

could cause high human and material damage. The French national statistic institution INSEE

define it as “an event characterized by the abnormal intensity of a natural agent (flood,

mudslide, earthquake, flood, drought ...) when the usual measures to prevent such damage

could not prevent their occurrence or haven’t been taken”. A French ministerial decree notes

the state of natural disaster.

While natural disasters are caused by meteorological, seismic or other causes over which man

has no control, their balance sheets are heavily dependent on the human factor. Indeed, the

implantation of populations, infrastructures or activities in areas subject to natural disasters

affects the economic and human consequences of disasters.

In an actual ecological context, especially with the COP 21, climate change causes an increase

of natural disaster and damaged associated. Cyclones, hurricanes, drought, heat waves,

torrential rains, floods, storms have had their number and intensity grow significantly since

the 1980s, and this increase is a direct consequence of global warming, in the opinion of the

Group of climatologists Intergovernmental Panel on Climate Change (IPCC).

Moreover, population growth and GDP in regions vulnerable as well. Therefore, the

frequency and severity of the economic impact of natural disasters have intensified in recent

decades. The economic stakes are high. Some researches show that major natural disasters

have a negative impact on economic conditions. For example, a study of insurance board of

Canada in 2014 shows that a typical disaster will reduce economic growth by about one

percentage point to GDP of roughly 2%, while major disasters can have more adverse

consequences. For example, the earthquake that occurred in Kobe in 1995 long-term reduces

GDP by 13% per capita.

More specifically with a financial analysis, with international capital market more and more

interconnected, inadequate risk transfers and financial risks can also transform the growth

engine of economic interconnection as a threat to financial stability. Catastrophic losses may

1

spread along different sectors of the economy through a systemic domino effect. For example,

a mega earthquake can devastate the housing stock of a region. If the affected properties are

not insured, mortgage holders may find themselves with a property loss and be strongly

discouraged to repay their debt. Such a scenario could have disastrous consequences for the

banking sector, as demonstrated by the recent 2008 subprime financial crisis in the United

States.

Financial markets are described as “nervous” or “jittery” by people which create theses

features themselves. The market is one day a peaceful and the next day is a storm. These

descriptions are explained by the fact that the market itself is simply a massive compilation of

investors’ buy and sell based on the most part on fundamental analysis such as news or events

and expectation on underlying part. For a market to be considered efficient, share prices must

respond quickly to new information.

Natural event and financial market have some similar properties, such as unpredictability and

uncontrollability by humans and result in huge damage to personal property when extremes

events occur. Then issues of growing unpredictable natural disasters join those from

interconnected financial market. It becomes in our current context a new important kind of

risk to be taken into account next to those historically study in the financial literature such as

market or liquidity risk. This statement is reflected for example in the risk management

landscape in full evolution and the appearance of Cat Bonds. In this way, it is therefore

important to measure the evident effect of theses kind of disasters on financial market in order

to understand, expect, and manage it and may be a good test of market efficiency theory.

2. Objectives

This study focus on the reaction of the financial market to the happening of extreme events

such as natural disasters and how to measure this effect with econometrical tools. Statistical

methods are used to find out the relationship between them, determine the size of the reaction

and when it occurs. The specific methodology is crucial because it directly affects the results

of a test of market efficiency.

3. Chapter organization

2

The paper is organized as follows. Chapter 2 focus on the literature review which addresses

the topic. Different known approaches used to measure the impact of natural disasters on

financial markets for both return and volatility are explained. These models are presented in

increasing order of complexity and throughout time. The final Chapter 3 summarizes and

concludes the paper.

3

Chapter 2

Literature Review

There are four parts in this chapter. The first part introduces the efficient market hypothesis,

an essential base to study econometrical measuring. The second part the logical evolution of

subject treated in literature, especially the case of insurer stock price behavior after natural

disasters. And following parts describe how return and volatility are measured in financial

literature.

1. Efficient Market Hypothesis

First of all, to study the effect of natural disasters on Financial Market, we have to admit that

these markets can reflect this information and in which forms.

Fama in “The Behavior of Stock Market Prices” paper (1965) has introduced for the first time

the notion of efficient market hypothesis. Indeed, before the 1960’s, economists don’t focus

on financial markets because they though that it was not a serious study.

For Fama, financial markets are efficient and drive all information in market price. In this

way, prices follow a random walk making its evolution unpredictable. This theory induced

that if the market is efficient, no investor can succeed in obtaining an abnormal profit on the

market for a given level of risk. On the long run, “to beat the market” is thus impossible. The

price of a security is thus equal to its theoretical value. The overvaluation or undervaluation is

thus impossible in an efficient market.

More specifically in 1970, Fama distinguishes 3 forms of efficiency, classified according to

the capacity of the agents to get information on the market. introduce the event study

methodology. The weak form follows the first study and states that the whole of past

information is already taken into account by the current price of a stock. The semi strong

efficiency implied that prices adjust very fast to new public information (thus it is impossible

to speculate on it with fundamental or technical analysis). And the strong efficiency which

take into account public and private information.

4

To test and validate the assumption of semi strong hypothesis when the price of a security

fluctuates instantaneously with the advertisement of a public information, Fama introduces in

1969 with Fisher, Jensen and Roll the now classic event study methodology. They analyze

data of different kind of companies which make split in a specific period (1927-1959). The

main object of this study is to find the obvious impact of an event on stock market but the

magnitude and the timing as well.

This assumption, developed at the time of the application of probabilistic mathematics

(stochastic) to finance, was the support of important advanced financial modeling, and

involved in its turn the fast development of new tools of financial markets. In the same way,

large studies on impact of different kind of information on financial markets was studied

based on this model such as merger, acquisition, non economic event, change in economic

policies and in our case natural disaster.

2. The case of natural disaster and the evolution of financial literature trough the

time

There is not much literature which speak about statistical tools to measure the effect of natural

disasters on financial markets. Indeed, most of subjects were addressed in 90s years, when the

significant growth of natural disasters and their damage beginning to awaken the spirits of the

possible economic and financial impact. Thoughts have naturally turned to the first key

industries and began to focus on insurer stock price behavior after natural disasters. Indeed,

this industry are affected by catastrophic events in more complex ways than most non-

insurance firms. It is in this limited financial context that two big hypotheses were studied.

Firstly, some researchers assume that there is a benefit for insurance industry after a disaster

because it involves an increase in demand for their products through an increase in both

required coverage and additional premium earnings. It was studied by Shelor et al. (1992) and

Aiuppa et al. (1993), it was found that insurer stock values increased and had a 2%

cumulative abnormal return over the three weeks following the 1989 California’s Loma Prieta

earthquake because high earthquake insurance rates and low perceived risk meant many

property owners were uncovered at the time.

5

The second obviously hypothesis state that we can think that large premiums paid to contract

holders in case of natural disaster involve large losses for insurance industry. Effectively, a

large part of them are hedged by reinsurance but for the market, the expectation of the impact

involves panic behavior and a decline of insurer stocks price. This first focus was studied in

this way by Sprecher and Pertl (1983) and Davidson, Chandy and Cross (1987) who find that

large loss due to acts of nature and airline disasters are incorporated into stock prices with

significant negative returns.

Lamb (1995) and Angbazo & Narayanan (1996) who found large negative effect of Florida

and Louisiana’s Hurricane Andrew, and an increase in premium insufficient to cover losses.

Moreover, they showed evidence of a contagion effect to insurers with no claims exposure in

the hurricane affected states. Lastly, Cagle (1996) concluded that South Carolina’s Hurricane

Hugo involved a high negative price reaction for insurers with high exposure and unaffected

for those with low exposure.

Moreover, it is important to note that issues of catastrophe risk involved the emergence of

new hedging products for insurance and reinsurance industry such as options and bonds (Cat

Bonds…) which enables insurance companies to hedge their exposure by transferring risk to

investors, who take positions on the occurrence and cost of catastrophes. These products are

relatively new, but they have already established an important link between the insurance

industry and capital markets. It is discussed at length in Borden and Sarker (1996). Implied an

additional impact on FM.

The first studies on the effect of natural disasters on stock market as a whole appears in

2000’s and begin with Worthington and Valadkhani (2004) who study the abnormal return

after a natural disaster in Australia. It is important to note that researches are divided in two

categories. Authors focus on return adjustment or volatility adjustment. These two parameters

are two related concepts in finance. Indeed, in the case of an investment in a risky financial

asset an investor will require a higher return in exchange for the risk. Conversely, an investor

who wants to improve the performance of its portfolio must agree to take more risks. Thus it

is important to study these two different parameters which are measured by different

statistical tools.

6

3. Return adjustment studies

In financial area, the return measure the gain or the loss of a security. In the case of a stock,

there is two types of income: the dividend corresponding to the share of income distributed

annually received by the shareholder, and gains or losses realized upon resale of shares.

There are two techniques to calculate profitability. The first of these is arithmetic. There is a

different value of the asset at the beginning and end of the period, it is added to income earned

during this period and is reported everything to the starting value. That seems obvious but

once observed several times, you can not add them directly. To do so, use logarithm return.

Accordingly, some researchers try to find the effect of financial disaster on stock market

return.

There is two best known methods in event studies. Indeed, the specific methodology

employed is very important because it affects the result of a test for market efficiency in our

case.

- Intervention analysis & ARMA Model

Worthington and Valadkhani (2004) examined the impact of natural disasters on the

Australian equity market.

The paper used the daily price and accumulation returns over the period 31 December 1982 to

1 January 2002 for the All Ordinaries Index (comprised of over common shares of over 300

companies from the Australian Stock Exchange with the record of 42 natural disasters to

examine the impact. The log of the price is computed for the closing prices to produce a time

series of continuously compounded daily returns supposed stationary. In this study period,

authors use Emergency Management Australia records to identify dates of events.

- Methodology:

Intervention analysis (first proposed by Box and Tiao in 1975 who applicate it to economic

and environmental issues) was used to estimate the effect of each natural disaster on the time

series. Intervention mean a change in something which modify the value of a series,

represented here by disasters. In this way, we want to estimate how much these interventions

7

had changed the series. ARMA was used to model returns before and after interventions, and

a variable was added to represent the intervention effect to returns after events.

a. The following ARMA process of order (k,q) specified in terms of the lag operator L

with no intervention is supposed like this:

y t−μ=Θq ( L )Φk ( L )

E t

Φk ( L ) represents a k-order polynomial lag operatory is the market return in price or accumulation formμ is a constantΘq ( L ) denotes a q-order polynomial lag operatorε is a white noise processk is the number of autoregressive (AR) terms, q is the number of moving-average (MA) terms

The Autoregressive process regress the return on itself on k terms, and the Moving Average

regress the error term on itself on q terms, autocorrelation pattern can be removed from a

stationarized series by adding enough autoregressive terms.

The autocorrelation (AC) and partial autocorrelation (PAC) functions can be used to

determine accurate value of q and k. The magnitude and sign of the estimated coefficients on

these variables indicates the mean effect of each natural disaster category on market return.

b. Now to introduce the intervention effect, formula may be written as:

y t−μ=Θq ( L )Φk ( L )

E t+βD¿

D¿ are intervention variables (referred to natural disasters during our period)β are intervention parameters and all other variables are as previously defined

Patterns for intervention effect can be constant, partial, gradually increasing or decreasing…

And can be modeled as well.

c. To best gauge the impact of natural disasters, it is important to take into account

several feature of stock markets.

First, seasonality is a classic pattern in finance, and usually causes the series to be non

stationary. To capture any possible systematic underlying time series patterns in the data, we

can remove trends by seasonal differencing of the autoregressive term.

8

Secondly, exogenous variable can be added. It is important because it can isolate any other

macroeconomic intervention parameters which have an impact on returns:

y t−μ=Θq (L )

Φk ( L )(1−ϕ Lr)E t+βDt+δ Dug , t

δ are exogenous variables, Du are exogenous parametersϕ is a seasonal parameter, r is the seasonal lag term

- Results:

Obviously, the conclusion of the study was that shocks provided by natural events and

disasters have an immediate influence on market returns, but it is important to note that

different kind of natural disaster had a mixed impact on market return. Indeed, cyclones and

bushfires are generally the most significant.

- Limits:

The principal critic that we can make is that authors focus on a single country and on a

smaller, less liquid Australia market and don’t compare across national market or try to find if

natural disasters affect a biggest economy such as United States.

Statistically, authors miss to take into account control variable which can have an impact on

the return of Australian Stock Exchange, such as exchange rate or other stock return. Indeed,

in a globalized world all stock markets are more or less correlated, especially with United

States Stock Market.

- Event study methodology & Market model

Siqiwen Li. (2012) employs the event study methodology to evaluate the impact on several

natural disasters which occurred in the State of Queensland during 2005 to 2011 on the

Australian Equity market. But the Author doesn’t take an index as a whole but divided the

examination across seven industries (agriculture, banking, insurance, mining, construction,

retailing and transportation). Indeed, Schwert (1981) suggested that firms stock price data are

more powerful because they incorporate all relevant information when they are available. The

log of the price is computed for the closing prices to produce a time series of continuously

compounded daily returns supposed stationary.

9

Author use event study methodology, which analyses differentiate between the returns that

would have been expected if the analyzed event would not have taken place (normal returns)

and the returns that were caused by the respective event (abnormal returns).

- Methodology:

Authors identify a study period for each disasters and use event study methodology to analyze

the difference between the return that would have been expected if the event wasn’t here

(normal return) which became the reference market and the return that was caused by the

respective event (abnormal return). This analysis is in two step:

a. Estimating the parameters of the market model based on data of a prior period to the

event, author use Market model:

Ri , t=αi+β i Rm,t+εi ,t

Ri , tis the estimated daily equity return of firm i at day tRm , t is the return of the market portfolio (AOI)α i , β i are respectively the estimated market model intercept independent of the market performance and slope parameterε i ,t is the error term which is assumed to be normally distributed and seriallyindependent

Parameters of this linear regression was estimated by Ordinary Least Squared methodology.

b. Analyzing the residual after applying this model to a time period which include the

announcement date:

ARi ,t=Ri ,t−Ri , t

Ri , t is the actual daily return of firm i for day t;ARi ,t is the abnormal return for firm i for day t.and all other variables are as previously defined

c. Thus, the sum of abnormal return of all companies give the Average Abnormal Return:

AARt=1n∑j=0

n

ARi , t

n is the number of firms in the sampleand all other variables are as previously defined

d. To measure the total impact of the natural disasters over a particular period of time

10

(event window), add up average abnormal returns of sample firms to create a Cumulative

Abnormal Return:

CAR(t 1 , t 2)=∑t=t 1

t 2

AARt

CAR(t 1 , t 2) is the cumulative abnormal of sample firms on day t in the event window t1 is the earliest date in the event windowt2 is the later date in the event windowand all other variables are as previously defined

- Results:

The study obviously shows abnormal returns in all firms both negative for the most part and

positive which provide evidence that the disasters generated new information to the market

(The sign, magnitude and statistical significance of the excess returns indicate whether there

is a market response to new information). Firms of different sector had different reaction.

Insurance firms had negative reactions (maybe because of insured losses), while construction

industry are obviously less vulnerable. Surprisingly, banking, mining, and agriculture industry

appeared no significant reaction.

Author also show that negative returns appeared some days before disasters, it suggests that

the market may have anticipated these events because of media revelations and

meteorological forecasting. Abnormal returns appeared some days after disasters as well,

maybe because media hadn’t all information immediately, and it is difficult to evaluate all

losses in a short time.

Generally, Australia equity market seemed efficient and absorbed information correctly and

immediately for these disasters and the direction of impact will depend on the industries of

that specific company.

Nannan Luo (2012) studied the impact of the Japanese earthquake 2011 on six stock markets

all over the world with event study methodology as well.

Authors funded that Japanese stock market was negatively impacted, and other stock markets

was smoothly impacted and re adjusted immediately, but insignificant statistically for the

most part. It is showed that as previous study, the direction of impact of the earthquake will

depend on the industries of that specific company, with negative and positive impact, which

explain the overall no significant impact of each global stock markets. The reaction also

appeared a little bit later du to delay of information caused by the earthquake.

11

- Limits of these two study:

These authors based their study on the most famous and simple methodology by Fama, but

they miss the issue of autocorrelation which is obviously the case with financial data. Indeed,

autocorrelation violate the Ordinary Least Squared assumption that the error terms are

uncorrelated. With this false hypothesis in this case, estimation of parameters was biased.

To model the autocorrelation in regression analysis using time series data, we have to use an

Autoregressive model, Moving Average model, or a combined of Autoregressive Moving

Average model used by Worthington and Valadkhani in in the first study above.

Secondly, the β parameter in the linear market model used in the event study methodology

represent for a financial data the systematic risk, which are the excess of volatility on a market

portfolio. Indeed, it is a key parameter of the one factor Capital Asset Pricing model. For a

study of an index, the systematic risk is equal to 1, but it isn’t the case for a firm. For

example, a traded company has its own beta, but this beta is not stable and changes when an

announcement has an effect on it. In our case, we can expect a change in beta when a natural

disaster appears.

Then the CAR methodology is susceptible to produce biased results. This issue can be take

into account with Intervention analysis by adding more exogenous variables which represent

the variation of the systematic risk (pre and post event) what Worthington and Valadkhani

don’t take account as well.

But in reality, financial time series data are not linear and are heteroskedastic, variance are not

constant, it changes over time. In this way, beta or the slope coefficient of the market model is

conditional to the time. Thus financial disasters can impact return and volatility of stock

markets, we have to capture this on our model in order to have the best estimation.

4. Return and Volatility adjustment studies

Considered in finance as the basis for measuring risk, volatility is defined as a measurement

of the amplitudes of changes in financial assets. This quantification indeed uses the standard

deviation of changes in profitability, it is calculated by finding the squared root of the

variance (average of the squared deviations from the mean rate of return). For example, after

12

an important public news, during a stock market crash or a sharp fall in markets, volatility

increases sharply. Although the realized volatility of an asset may know "peaks", it also tends

to revert to its mean.

Thus, the higher the volatility of an asset is higher and investment in this asset is considered

risky and therefore more expected gain (or risk of loss) will be important.

Worthington (2008) returns to his previous study which he made with Valadkhani in 2004 on

the impact of natural disasters on the Australian stock market in order to take account of the

impact of them on the return and volatility.

He used the daily stock market return from 1980 to 2003 from the All Ordinary Index and

natural disasters from Emergency Management Australia. The log of the price is computed for

the closing prices to produce a time series of continuously compounded daily returns

supposed stationary.

- Methodology:

To model the return and take account of the volatility, he used GARCH model in the mean.

GARCH model the variance of the return. In the return equation under heteroskedastic

feature, the error term is not constant and its variance is modeled by GARCH model.

GARCH is well known to capture long term memory and volatility clustering, which is more

adapted to financial data and to measure the magnitude and persistence of shocks. Indeed,

statistically ARCH is based on a Moving Average model to capture short term volatility

behavior, and GARCH add an Autoregressive feature to capture long term memory.

GARCH-M only introduce the conditional variance in the mean equation as an explanatory

variable, thus the return is in relationship with the risk as the CAPM model.

a. GARCH-m add a heteroskedastic term in the mean equation supposed like this:

y t=α i D¿+γ σ t2+εt

y is the market return in priceD¿ are exogenous parameters (i natural disasters)Du are exogenous parametersε is the error term which is normally distributed with zero mean and a variance of σ t

2

b. The variance of return is modeled by the GARCH process of order (p,q) supposed like

13

this:

σ t2=β0+ βi∑

i=1

p

ε t−i2 +γ j∑

j=1

q

σ t− j2

σ t2 is the return volatility or risk of the market

ε is the error term which is normally distributed with zero mean and a variance of σ t2

β0 is the time invariant component of the varianceβ i is the ARCH parameter and γ j is the GARCH parameter

- Results:

The results indicate significant negative ARCH and GARCH parameters. Thus volatility

shocks in the Australian market was strong and persist. But no significant negative variance

parameter in the mean equation. Thus natural disasters involve no systematic risk for the

Australian Stock Market. That can be explained by the well diversified index AOI. However,

specifics firms and areas as impacted.

- Limits:

This study presents some limits. First, the author still missed to introduce more exogenous

variable which have an impact on stock market returns (as we seen above for his first study).

Secondly, financial data behavior could be more accounted. Indeed, stock return could be

modeled by ARMA process to remove autocorrelation features with intervention and

exogenous variables.

But these variable may also affect the volatility. Thus, the variance could be modeled by an

GARCH-X model which allow the conditional variance to depend on the intervention

variables and additional explanatory variables as the conditional mean. Thus, variables in the

mean equation measures the abnormal returns due to natural disasters, while variables in the

variance equation measures the impact on stock market volatility.

Moreover, we can consider an Exponential GARCH introduce by Nelson in 1991 to capture

the leverage effect of equity returns. This model is the most successful to model indices, Dima

14

Alberga, Haim Shalita, and Rami Yosef studied in 2008 the best asymmetric GARCH models

to estimate stock markets volatility.

Chapter 3

Conclusion

Resumer principaux resultats

Indiquer comment le travail pourrait etre completer par autre etude

Detailler les analyse des auteurs, les donnes utilize les resultats les methods, faire des beaux

tableaux. Comparer tout ca et faire une conclusion qui repondrait a la problematique par

rapport a la revue de literature.

15

References

Aiuppa, T., Carney, R. J., & Krueger T. M. (1993) An examination of insurer stock prices

following the 1989 Loma Prieta earthquake, Journal of Insurance Issues.

Angbazo, L.A. and Narayanan, R. (1996) Catastrophic shocks in the property-liability

insurance industry: Evidence on regulatory and contagion effects, Journal of Risk and

Insurance.

Borden, S. and Sarker, A. (1996) Securitising property catastrophe risk, Current Issues in

Economics and Finance.

Box, G.E.P. and Tiao, G.C. (1975) Intervention analysis with applications to economic and

environmental problems, Journal of the American Statistical Association.

Bureau d’Assurance du Canada (2014), Reduisons l’impact financier et economique des

catastrophes.

Cagle, J.A.B. (1996) Natural disasters, insurer stock prices and market discrimination: The

case of Hurricane Hugo, Journal of Insurance Issues.

Davidson, W., Chandy, P. and Cross, M. (1987) Large losses, risk management and stock

returns in the airline industry, Journal of Risk and Insurance.

Dima Alberga, Haim Shalita, and Rami Yosef (2008) Estimating stock market volatility

using asymmetric GARCH models, Applied Financial Economics.

Fama, E. F. (1965) The Behavior of Stock Market Prices, Journal of Business.

Fama, E. F., Fisher, L., Jensen, M. C. & Roll, R. (1969) The Adjustment of Stock Prices to

New Information, International Economic Review.

Fama, E. F. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work,

Journal of Finance.

Groupe d’experts intergouvernemental sur l’evolution du climat (2011), Rapport special sur

l’evolution du climat et la gestion des risques de catastrophes et de phenomenes extremes

pour les besoins de l’adaptation au changement climatique (SREX).

Lamb, R.P. (1995) An exposure-based analysis of property-liability insurer stock values

around hurricane Andrew, Journal of Risk and Insurance.

Nannan Luo (2012), The Impact of Natural Disasters on Global Stock Market: the Case of

the Japanese 2011 Earthquake, Saint Mary’s University, Halifax, Nova Scotia.

iv

Schwert (1981) Using Financial Data To Measure Effects of Regulation, Journal of Law

and Economics.

Shelor, R., Anderson, D. and Cross, M. (1992) Gaining from loss: Property-liability insurer

stock values in the aftermath of the 1989 California earthquake, Journal of Risk and

Insurance.

Siqiwen Li. (2012) The Australian Capital Market Reaction to Natural Catastrophes: an

empirical analysis using event study method, Global Accounting, Finance and Economics

Conference, 20-21 February 2012, Melbourne

Sprecher, C. and Pertl, M. (1983) Large losses, risk management and stock prices, Journal

of Risk and Insurance.

Worthington, A. C. & Valadkhani, A. (2004). Measuring the impact of natural disasters on

capital markets: an empirical application using intervention analysis, Applied Economics.

Worthington (2008). The impact of natural events and disasters on the Australian stock

market: A GARCH-M analysis of storms, floods, cyclones, earthquakes and bushfires, Global

Business and Economics Review.

v