The Effect of Brexit on Exchange Rates and Stock Prices: An RD approach

A thesis presented to the Faculty of Economics and Business – University of Amsterdam

In partial fulfilment of the requirements of the degree of the Bachelor of Science in

Economics and Business – Finance specialisation

Written by

James Heaton

Student I.D.

10630309

Thesis Supervisor

Dr. Liang Zou

Netherlands

January 2017

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Table of Contents Abstract .............................................................................................................................................. 3

Introduction ........................................................................................................................................ 3

Domestic Social and Political Background .......................................................................................... 3

International Background ................................................................................................................... 4

Theoretical Background ...................................................................................................................... 5

Previous Research Methods ............................................................................................................... 6

Data .................................................................................................................................................... 6

Methodology ...................................................................................................................................... 6

Test Results ......................................................................................................................................... 8

Discussion ......................................................................................................................................... 10

Appendix ........................................................................................................................................... 13

References ............................................................................................................................................ 17

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Abstract Brexit was one of the major global phenomena of 2016 with implications for

the future of trade in Europe and around the world. This paper discusses the

implications of Brexit on the British stock market and exchange rate, it does

so in the full context of the events leading up to and following the vote. The

event study methodology used in this paper is a regression discontinuity

design with the occurrence of Brexit is used as the treatment variable. The

use of this methodology is appropriate over that of a more complicated time

series analysis due to the magnitude of the impact on the markets and the

relatively short period of time which has passed since. Focusing on the

FTSE100 market index and GBP per SDR, market reactions are estimated and

a chain of causality between the variables is established using theories of

exchange rate determination. This paper makes frequent use of non-

academic sources for the purposes of capturing the causes, both political and

economic, behind the vote and, to establish why the market reacted with

such volatility to the outcome. We find that, after initial panic in the market,

the exchange rate depreciation lead to an appreciation in British stock prices

due to the perceived increased competitiveness of exports.

Introduction

Domestic Social and Political Background On the 23rd of June 2016 the United Kingdom (UK) voted to leave the European

Union (EU), this vote has been coined Brexit and stands for British exit from the EU. The

implication of this vote is that there will likely be a systematic change to the way the UK

interacts with the rest of the EU, focusing on issues central to the union such as immigration

and trade. The decision to leave, for the most part, came as a shock to the market; to

understand how divided public opinion was immediately before Brexit this we can look to

several non-academic sources.

Firstly, we found the odds on several popular betting sties on the day of the vote

where a leave vote was given at best a three to one shot, but in most instances around six to

one (New Statesman, 2016), this infers that in the last hours the general population

expected there to be a vote to remain. Days before the election George Soros, a prominent

billionaire investor, wrote an opinion article for the guardian titled ‘The Brexit crash will

make all of you poorer – be warned’ (2016). In this he predicted a currency market crash of a

similar magnitude to the crash of “Black Wednesday” where, on September 16th 1992, Soros

and others participated in massive speculative attacks against the pound which caused the

British Government to devalue the sterling leading to a drop in exchange rates against the

Dollar and Deutschemark of six and five percent respectively from the 15th to the 17th of

September 1992. He (Soros) stated that there would likely be a crash of equal or greater

magnitude than in 1992 due to the current economic environment and that of the past ten

years. In part because the interest rate set by the Bank Of England (BOE) was already at 0.5%

(BOE, 2016), the lowest level compatible with properly functioning British banks (Soros,

2016). Mr Soros also pointed out that the IMF, BOE and, the Institute for Fiscal Studies had

made public their assessments of the economic consequences of Brexit on the average

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household, with estimated losses ranging from £3000 to £5000 annually. The Prime Minister

David Cameron and opposition leader Jeremy Corbyn both campaigned for the remain vote

as did a long list of foreign diplomats and economic experts including President Barack

Obama, who stated in an address to the British people that they would be at the “back of

the queue” when it comes to future trade deals. Poll data from the days before the election

also seemed to show a rally towards remain (Kennedy, 2016).

On the other side of the aisle were those in favour of Britain leaving the EU, the so

called ‘Brexiteers’, many of whom were equally as passionate that leaving was the only way

to avoid catastrophe (Maddox, 2016). British politicians such as Former UKIP leader Nigel

Farage and Former London mayor Boris Johnson lead campaigns for the leave vote. They

were sometimes criticised for perceived misinformation; examples of this would be the now

infamous Brexit bus, (Hughes, 2016) which Boris endorsed, suggesting that all the money

spent on maintaining Britain’s place in the EU would instead go to the National Health

Service (NHS), this was found to be untrue the day after the vote in an interview with Nigel

Farage. Fears were raised over state pensions and the ability of the NHS to cope with

immigration, since Britain has an all-inclusive single payer health care system. This exact

premise was debated by famous economist Milton Friedman, whom discussed the issues

with having open immigration policy and a welfare state, suggesting it leads to a steady

decline in overall welfare until equilibrium is reached with the countries able to migrate.

Since the UK is seen by many of citizens as having a high standard of living and level of

welfare compared to other EU member states (Sippitt, 2015), many wished for an end to the

freedom of movement which is available to every citizen of an EU constituent country. As

well as arguments on the basis of welfare and immigration there are also those who

presented arguments based on economic outcomes. A London economist rebuked Soros’

statements that a catastrophic depreciation of the pound would be damaging to the British

economy, but could instead lead to a “virtuous wage price spiral” to avoid what he describes

as a “deflationary abyss” (Elliott, 2016).

Separately from these campaigns has been the proliferation in the United Kingdom

of right wing political groups such as the English Defence League (EDL) and Britain First who

have been calling for a nationalist agenda. Whilst these groups posed no threat to the big

three political parties, Conservative, Labour and, Liberal Democrats, they have been

harbouring and spreading anti-Muslim, anti-immigrant sentiment for a number of years.

These groups have participated in mosque raids and protests against immigration. A look at

Britain First’s Facebook page, their primary mode of communication, reveals over 1.5million

followers and a stream of posts focused on crimes committed by migrants and how UK aid is

spent abroad, the truth of such stories is questionable and many claim it is ‘fake news’. We

should note the long term influence of David Cameron’s previous political campaign for

office in 2010 in which he has been criticised for using anti-European language to garner

votes (Hope, 2010).

International Background The Economist published several issues of their magazine describing the global rise

of nationalism (Anonymous, 2016), with the leaders of notable world powers such as the

United States of America, India, Russia, Turkey, France and now Britain moving along the

political spectrum towards nationalism. This has been put down to several key causes, firstly

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many of these nations have seen a large increase in their foreign born population in recent

years, graph from The Economist in appendix 2, with Britain facing one of the highest

percentage increases. This then is an extension of the welfare problem mentioned

previously, supported by an article for Forbes magazine describing “unequal national welfare

states, free immigration and a stable EU” as “an impossible trinity” (Bernstam, 2016).

Secondly, fears of national security have been expressed by the British people (Schindler,

2016). With the rise of motivated radical groups such as ISIS, who have recently planned and

executed terrorist attacks against civilian populations as close to Britain as Paris and Berlin,

tensions have been wound tightly in recent years. The decision of Angela Merkel to allow an

estimated 800,000 migrants into Germany, in 2015 alone (Hall, 2015), exacerbated the

feeling that some had about national security given the EU’s open border policy.

The uncertainty caused by these complex conditions goes some way to explaining

how the markets got their predictions wrong and why, when the results of the referendum

were made public, there was such a large reaction in the foreign exchange and stock

markets. We seek to determine how Brexit affected two markets and how these markets

affected each other.

Theoretical Background The literature supporting the hypothesis that there exists a relationship between

exchange rates and stock prices fall into two main categories: flow-oriented models and,

stock-oriented models, we will begin by discussing flow-oriented models.

Prior to the publication of Exchange Rates and the Current Account by Dornbusch

and Fischer in 1980, exchange rate theory had developed to assert that the point exchange

rate is determined by the asset market, the bond market, and is affected by policy such as

monetary expansion (Calvo and Rodriguez, 1977; Dornbusch, 1976). Whereas the long term

trend of the exchange rate is driven by changes in the current account, which affects net

asset positions and by extension the asset markets. Flow-oriented models such as

Dornbusch and Fischer (1980) built on this definition of exchange rate theory and on

research by the likes of Robert Mundell and Marcus Fleming to form a model of exchange

rate determination. They (Dornbusch & Fischer, 1980) extended on previous literature in

two ways: firstly their model assumed that there are determinants of the exchange rate

other than equilibrium real money demand as posited by purchasing price parity. Secondly,

they considered how anticipated future disturbances affected the current economic climate.

Importantly from this we note that the theory suggests that, through the current account,

there exists some long term correlation between asset markets, approximated by either the

bond market or later the stock market, and the exchange rate.

Stock-oriented models such as the portfolio balance model developed by Branson et

al. (1977) make the assumption that the prime determinant of the exchange rate is the

capital account. They state that an increase in stock prices leads to a rise in the domestic

interest rate which in turn increases demand for domestic currency which affects the

exchange rate. By this mechanism movements in the stock market affect the exchange rate.

Monetary models such as Gavin (1989) are similar to the Mundell-Fleming model; however

the stock market rather than the bond market is used to determine aggregate demand.

Gavin found that including stock prices in the model could dampen and even reverse the

conventional effect of a monetary expansion; a monetary expansion could lead to an

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appreciation of the real exchange rate. Whilst he does not formulate a direct link between

stock market movements and exchange rates there are common factors influencing both

markets in his model.

Previous Research Methods There have been numerous empirical analyses that have sought to describe, either

directly or indirectly – through macroeconomic variables – the relationship between

exchange rates and stock prices. In the majority of cases the authors find evidence of

cointegration between their market proxy and exchange rate (Fang, 2002; Gang Tian & Ma,

2010). These, in general, rely on either an autoregressive model or an error correction model

to provide evidence for a relationship, the main reason for this is their analysis periods lack a

large exogenous shock, such as Brexit, and must instead perform complicated statistical

procedures to yield reliable results. We avoid this problem of complexity due to the nature

of Brexit, because the event came as a surprise to many it was not priced in before hand,

infact there was evidence to suggest a vote to remain had been priced in. We therefore see

a large, exogeneous and instantaneous effect on the markets, this makes Brexit an exellent

opportunity to use regression discontinuity. Due to the magnitude of this effect we make the

assumption that other exogenous factors are neglegible, this allows us to run a simple

ordinary least squares (OLS) linear regression as evidence for this study. Finally as a model

for the layout of the event study we looked a papers describing financial crises (Miyajima &

Yafeh, 2007; Taylor, 2009). These provided a good platform due to some of the similarities

between our events; these papers also discussed recent financial events with implications in

both the foreign exchange and stock markets. Brexit was smaller in terms of its global effect

than was the global financial crisis of the late 2000s discussed in one of the papers, and

therefore we have included comparitively more domestic, social and, political influences and

less of the global interactions.

Data The data used to estimate the stock market is the FTSE100; daily close values are

taken from the London stock exchange website (London Stock Exchange, 2017). The

exchange rate chosen is British Pounds (GBP) per Special Drawing Right (SDR); data taken

from the International Monetary Fund (IMF) website. SDR is a weighted value of a number of

different currencies and is chosen because Britain trades with many countries and therefore

no single currency pair would give an adequate representation of how trade will be affected,

we expect the SDR will give a better estimate of the real effect of a revaluation of the

currency on the market.

Note that important graphical representations of the variables are available in

appendix 1, these graphs clearly shows the changes we are looking for. The vertical gaps

between the trend lines show the approximate effects of Brexit. “Pre” and “Post” trend lines

in appendix 1 are calculated using separate ordinary least square (OLS) regressions.

Methodology The method we use in this paper is a regression discontinuity (RD) event study based

on the work of Lee and Lemieux (2010) titled Regression Discontinuity Designs in Economics.

The RD method was first used by Thistlethwaite & Campbell (1960) in their analysis of how a

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positive signal of students receiving a merit award affected a student’s future grades and

probability of receiving a scholarship. The RD design differs from a regular OLS regression in

that it must include a dummy variable which represents a change in conditions or

Treatment. In the study of Thistlethwaite & Campbell the treatment variable was whether a

student received an academic merit award, it is useful that the event may be categorised as

either happened or not happened with ceratainty, this is called a sharp RD. There is also the

fuzzy RD in which the probability of the event occuring changes but where there is not

certainty, for example a 0.2 probability increases to 0.8. Our data exhibits several properties

which enable us to use the RD method, we cover these below.

The properties which make an RD design favourable will be now listed and

explained. Firstly, the effect can be isolated to the exact data point in which it occurs; this

provides a more efficient coefficient estimate as the entire effect of the leave vote can be

placed in the correct half of the regression. Secondly, the probability of Brexit affecting a

value is either 0 or 1; this means we have a sharp regression continuity design. Thirdly, it is

preferable to have observations as close to the treatment cut-off point as possible – for

reasons described later in this paper –we meet this criterion because the data used spans a

relatively short time period. Finally, we have a disturbance caused by the event that is large

enough that we may assume it accounts for all the variation that occurs at that time, this

means that although we may not have an exhaustive list of causal factors, the missing

factors account for a small enough proportion of the change that excluding them makes little

difference to the output of the regressions.

As we pointed out in the data section, appendix 1 provides a graphical

representation of the variables, showing the linear trends before and after the vote. One can

make a number of interesting observations from these graphs. The first observation is that in

each graph the market’s reaction to the event is readily apparent. Secondly the trend lines

move in opposite directions. Finally, we note that stocks fall before rising again past the

original level a few days after the event; this is suggestive that multiple phenomena are

occurring with differing lags – we will discuss this more later on.

The ratio of the Brexit-induced effect will be our estimate of how stock prices move

relative to the exchange rate. Causality will be determined by the sign of this coefficient. If

the sign is negative we will assume flow theories of Dornbusch and Fischer (1980) to be

dominant; that causality leads from exchange rates to stocks, through increased

competitiveness. Conversely, if the sign of the coefficient is positive we will assume the

portfolio-balance model of Branson et. al. (1977) to be dominant; that causality runs from

stocks to the exchange rate because a relatively low rate of returns caused an outflow of

capital and a thus depreciation of the exchange rate.

Lee and Lemieux (2010) describe a linear discontinuity equation of the form of (0).

( )

Y represents future outcomes, D is a dummy variable and X is the independent variable.

Assuming an otherwise linear relationship this model can be used to estimate how an event

affects future outcomes. This is the same model as was used by Thistlethwaite and Campbell

(1960) and we use the model to estimate our parameters. We go one step further than

previous research (Thistlethwaite & Campbell, 1960; Lee & Lemieux, 2010) in that we use

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two RD analyses to form an approximation for how two variables react in relation to each

other in response to the same treatment.

The model we are going to produce, given by equation (1), will add rigor to the

observations we made about the graphs in appendix 1. It infers that we will predict the level

of the stock market based on a constant plus some coefficient multiplied by the exchange

rate, with an allowance for a variance with time – due to the appearance of time dependant

trends in the graphs of appendix 1. We are interested in how Brexit affected the relationship

between the markets and not in predicting the market; it is therefore enough for this study

to calculate and evaluate the β value used in equation (1).

( ) 1 2

To form an approximation of the value of β in equation (1) we will use a 2 step

process, firstly we will perform an ordinary least squares (OLS) estimation on each variable,

as given by equations (2), and (3). B in equations (2) and (3) represents a dummy variable

which is equal to zero in the period leading up to and including the 23rd of June and equal to

one thereafter.

( )

( )

Once we have estimates for the coefficients and we will estimate value β in

equation (1) using the function in equation (4).

( )

Equation (4) is an estimate for the ratio of how the stock markets move in relation to

the foreign exchange markets due to Brexit. This value could be an estimator of how the

markets will react due to other similarly large macroeconomic changes. We state large

macroeconomic changes and do not generalise to all movements in the market because of

the assumption we made that other exogenous variables have a negligible impact on the

results. Therefore when analysing smaller events the β value we generate in this study

would be a poor estimator of the relative changes.

Test Results First we present the results of the OLS regressions from in equations (2) and (3);

these are available in tables [1] and [2]. We get an estimated mean value for the data

(Constants) and an estimate for the variable change due to Brexit (β coefficients). We also

included a variable, t, to account for time variant change.

1 SM refers to the level of the stock market, as approximated by the FTSE100 stock index.

2 ER refers to the level of the foreign exchange market, as approximated by GBP per SDR.

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Table [1]

Ftse100 Coefficient Std. Error 95% C.I. 351.07553 36.49102 279.2106 422.9403 t 2.02527 .1718423 1.686846 2.363694 Constant 5915.315 20.23593 5875.462 5955.167

Table [2]

gbpsdr Coefficient Std. Error 95% C.I. -.06758294 .0040508 -.0755615 -.0596043 t -.000176 .0000192 -.0002138 -.0001382 Constant 1.016663 .0022641 1.038506 1.047426

The coefficients in tables [1] and [2] for the β values represent our RD estimate for

the change in the corresponding variable caused by the Brexit vote. These statistics support

what can be seen in the graphs of appendix 1. Due to the clear step-like behaviour exhibited

we have a strong indication that Brexit is in fact the cause of the variation (Thistlethwaite &

Campbell, 1960); this conclusion is supported by our analysis of the social and

macroeconomic environment. In table [1] we see that after Brexit the mean level of the

FTSE100 index was 351 points, about 5.5%, higher than before and the coefficients are

significant from zero to a high degree. In table [2] we see that the exchange rate is 6.8 basis

points, about 6.8%, lower following Brexit than in the time before. Again the coefficients are

significant from zero to a high degree. The β values are approximately equal to the average

gap between the pre and post trend lines in the graphs of appendix 1 if the lines were to

extend in the way shown in appendix 4. This method is limited in that the estimates are of

the average treatment effect and so the calculated coefficients are not useful for predicting

where a specific data point would have been placed if the treatment had been opposite (Lee

& Lemieux, 2010).

We now examine how these coefficients relate to each other. The coefficients in

these estimates give us our Brexit-induced effect ratio, β, described in equation (4). We

calculate the relative change in the stock market compared to a unit exchange rate change

with results in equations (4.1) and (4.2) to 7 significant figures.

( )

( )

This β value represents the Brexit-induced ratio of change, the approximate change

in the stock market for every increase of one in the exchange rate market, the negative sign

shows that the market value increases with a depreciation of the domestic currency. The

logic of why we generated this variable and why it is suitable to introduce into equation (1)

can be explained mathematically. Multiplying ER by the ratio of the Brexit-induced ratio of

3 This is our estimated value for in equation (4).

4 This is our estimated value for in equation (4).

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change gives the approximation for the correlation of movements in the stock markets and

foreign exchange markets due to a phenomenon. The β value calculated here could be used

to approximate how the markets will act in response to each other in the future.

The literature upon which we based our RD analysis (Lee & Lemieux, 2010;

Thistlethwaite & Campbell, 1960) does not provide a method for calculating the significance

of our generated β variable, this is because we use the coefficients in a way which they do

not. Our manipulation, given by equation (4), is valid because it makes use of the definition

of a coefficient in an OLS linear regression5 – the change in the dependent variable per unit

change in the independent variable, which is exactly what equation (4) computes with

respect to the variables in equation (1). We briefly discuss possibilities for evaluating the

significance of our variable β. An empirical test of the significance could take the form of a

recreation of this study on a similar event, such as Black Wednesday, and comparing the β

values from each study for similarities in sign and magnitude6. A mathematical possibility

would be to estimate the variance of the β test statistic and calculate a t statistic. However,

this requires the underlying populations of and to be normally distributed (Mood,

1950), as can be seen in appendix 6 we have bimodal distribution7. These graphs show

multimodal distributions. The coefficient β is a function of two coefficients for which we

have confidence intervals, therefore we can, using the probability law ( ) ( )

( ), generate a confidence interval (C.I) for β; . The reason we have

a C.I of at least 90.25% is that there will be values outside each confidence interval which

multiply to make a value within our generated interval. Also in this case the interval cannot

be of a higher confidence degree than the C.Is on which it is estimated. The bounds for the

confidence interval are given by equations (5.1) and (5.2).

( )

( )

( ) ( )

What can be inferred from equation (5.2) is that the 90.25% CI for β is within the

parameters of the inequality. Whilst we realise this is an imperfect estimate, it is still useful

and allows us to conclude that that the β value generated in equation (4.2) is significant to at

least 90.25%.

Discussion The analysis we performed showed that the event had a number of effects on our

variables. The fact that the changes to the variables are relatively large over the event is

evidence that the markets had not priced a leave vote in, taking efficient markets hypothesis

to be true (Fama, 1970), this means that it was unexpected, or at the very least the markets

were highly uncertain about the outcome. The signs of the coefficients shown in tables [1]

and [2] tell us a lot about how the market reacted in the short run. The signs of the

5 Exceptions are the coefficients of residuals and constants.

6 Although we do not perform this second event study, a visual analysis of data from the period seems

promising (appendix 5 & the depreciation mentioned in the introduction). 7 This is due to the Brexit induced effects on each variable.

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coefficients are opposite, we can infer causality from this using the two the prevailing

theories, the flow-oriented model (Dornbusch & Fischer, 1980) and the portfolio-balance

model (Branson, Halttunen, & Masson, 1977). We determine the causality to run from

exchange rates to the stock market, a description of the chain of events as we interpret it

follows. The vote to leave the EU, due to macroeconomic implications which are not entirely

covered by the scope of this paper, caused an oversupply of GBP, this lead to a depreciation

of the pound relative to numerous other currencies, as estimated by SDR. Due to the

comparatively low price of GBP, prices of exports from Britain became relatively less

expensive and therefore more competitive; markets saw this and anticipated that these

stocks would therefore receive higher returns in the future, and so increased investment

into British companies. Since investors have a home bias they prefer to invest in domestic

stocks (Coval & Moskowitz, 1999), this makes British stocks seem relatively more appealing

to international investors, whom likely had a large proportion of their wealth not already

invested in Britain, the depreciation then would then make British stocks look even more

appealing than if they already held GBP. This inflow of capital caused the market which had

originally fallen to quickly and dramatically recover, to a level 5% higher than it was

previously. Since this initial effect due to investor behaviour we see that the British economy

has grown faster than was anticipated (Office for National Statistics, 2017), validating

investor belief in British stocks. It must be noted that at this point in time Britain is

essentially having its cake and eating it too, by this we mean that the depreciation has made

exports more competitive and at the same time nothing, yet, has changed in terms of

Britain’s access to the single market and tariff free trade.

There can be seen graphically in appendix 1 an immediate fall in stock prices after

the Brexit vote, this does not fit in with what we have already stated as the chain of

causality. However, we do not believe this phenomenon is unexpected due to the nature of

the event and our interpretation of why this occurred follows. There was a lot of anxiety

surrounding Brexit, as was mentioned in the introduction, the vote was making waves

around the world and evoked emotional responses from those on both sides. European

leaders and institutions such as the IMF had come out publicly in favour of remaining, stating

that a bleak economic outlook could follow (Allen, 2016). Because of this, when the results

came out that Britain had voted to leave, there was panic in the markets, which will have

affected both variables as investors moved money out of British stocks. After the immediate

shock had passed, the market saw an investment opportunity and acted to take advantage

of this – which is why we see the prices of British stocks rise back up quickly and stay up.

It appears as if there have been only positive repercussions for the British economy.

However, we must note that some of the arguments used by those opposing Brexit reflected

negative long term externalities which would not have been expected to come to pass by

this time. As of the time this paper is written the UK has not yet begun the negotiating

process to leave the EU. During this study the British stock market has continued to perform

well, the table in appendix 3 (London Stock Exchange, 2016) shows that all FTSE metrics are

up in the six months since Brexit, notably to our study the FTSE100 is up 11.7% in this time.

As well as positivity in the stock markets Donald Trump, the newly inaugurated president of

the USA, has come out publicly stating that he believes Britain are doing well and that he

wants a trade deal between the UK and the US ready to sign by the time Britain leaves the

EU (Cowburn, 2017); this is exactly the opposite tone set by previous president Barrack

12

Obama. Although the EU foreign minister Federica Mogherini has stated in an address that

the UK may not participate in even informal bilateral trade meetings as long as they are a

member state of the European Union. Such talks could put the EU in an unfavourable

position according to Hungarian foreign minister (Barigazzi, 2017), although since the UK are

on the way out it is uncertain whether they will continue to adhere to such EU rules. Whilst

so far it would appear that Brexit has provided some much needed growth to the British

economy it will be interesting to see how the markets react as information leaks out over

the specific terms of Brexit. The British pound depreciated further recently, against the USD

and Euro, as Theresa May, current PM, set a course for a “Hard Brexit”, which refers to an

exit from the single market, if the pattern we found in our study prevails we expect this to

further strengthen the British economy.

There is, however, trouble on the horizon; in the last few days investment banks

HSBC and UBS have warned that they may be moving 1000 high paying jobs abroad each

upon Brexit, and Goldman Sachs is considering halving its London workforce to 3000 (CNBC,

2017). With the services industry accounting for around 80% of GDP in the UK, financial

services being an important part of this, the country may yet face some economic

challenges. There have been a number of resignations from members of the Shadow Cabinet

over pressures to vote to trigger article 50 of the Treaty of Lisbon, which would begin the

process for the UK to leave the EU. This suggests that the referendum was not the end of the

matter, and the country still appears to be divided on the issue. Not only will it be of

economic relevance to keep a close eye on Brexit as matters develop further, it will also be

an interesting experiment – one which EU sceptics and isolationists, from many countries,

will be watching avidly.

13

Appendix Appendix 1

Appendix 2

14

Graph from Anonymous. (2016, Nov 19). Trump's World: The new nationalism.

Appendix 3

Table from London Stock Exchange. (2016, Dec 30). FTSE 100 Index.

15

Appendix 4

Image from Lee & Lemieux (2010, pp. 290). Regression Discontinuity Designs in Economics.

Appendix 5

Graph from Yahoo Finance. (1992) FTSE100 Historical Returns.

16

01

02

03

04

05

0

Fre

que

ncy

.9 .95 1 1.05 1.1GBP/SDR

01

02

03

04

0

Fre

que

ncy

5500 6000 6500 7000FTSE100

Appendix 6

Frequency plot – observing FTSE100 for normality.

Frequency plot – observing gbpsdr for normality.

17

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