Do policy events affect market uncertainty : evidence from the option market

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Do policy events affect market uncertainty: evidence from the option

market.

Name: Brijan Baboeram

Student ID: 10578986

Study: Economics and Business

Specialization: Finance and Organisation

E-mail: Brijanb@gmail.com

Subject area: Asset Pricing

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Hierbij verklaar ik, [Brijan Baboeram], dat ik deze scriptie zelf

geschreven heb en dat ik de volledige verantwoordelijkheid op me

neem voor de inhoud ervan.

Ik bevestig dat de tekst en het werk dat in deze scriptie

gepresenteerd wordt origineel is en dat ik geen gebruik heb gemaakt

van andere bronnen dan die welke in de tekst en in de referenties

worden genoemd.

De Faculteit Economie en Bedrijfskunde is alleen verantwoordelijk

voor de begeleiding tot het inleveren van de scriptie, niet voor de

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Abstract

The purpose of this study is to examine whether policy events have an effect on market

uncertainty. To measure market uncertainty, this study uses the VIX as an proxy. The VIX is

a forward-looking index that measures implied volatility in the market. The index is

constructed by using the weighted prices of put and call options with different strike prices

(Dumas et al., 2004). The results indicate a positive, but not significant relation, between

fiscal policy events and the VIX. Moreover, there is a negative, but not significant relation,

between monetary policy and the VIX. Lastly, there is a negative significant relation between

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1.0 Introduction

The purpose of this study is to examine whether policy events affect market uncertainty. As a

proxy for policy events, this study uses legislations that are considered and passed by

Congress and finally signed by the president. Previous studies have conducted a similar

research, but differ in some way from this study. Pastor and Veronesi, (2012) examine the

effects of political uncertainty on stock prices. The authors divide political uncertainty in two

categories. On the one hand, they examine the effects of uncertainty on stock prices due to a

change in fiscal policy. on the other hand, they examine the impact- effect of a policy change

on stock prices. this study partly extends their study by also using fiscal policy events, but

uses them to test whether there is an effect on market uncertainty, which is measured by the

VIX. Furthermore, fiscal policy events are defined in this study as proposals and adoptions of

regulatory acts. The main reason is to find out whether market participants consider fiscal

policy events as a form of uncertainty. Furthermore, this study also examines the effect of

monetary policy events.

In a study by Kelly et al. (2014), the authors examine the effects of political

uncertainty on implied volatility. In their study, they use elections and global summits as a

proxy for political uncertainty. Furthermore, they examine whether political uncertainty affect

the implied volatility on firm-level whereas my study focuses on the effects of policy

uncertainty on index-level. Changes in policy variables and their effect on implied volatility

have also been studied by Baker et al. (2013). The authors constructed an Economic Policy

Uncertainty index (hereafter: EPU) to measure daily policy uncertainty. This study partly

extends their study by using the EPU to predict the effect on the VIX. This study uses this

index to examine whether policy events affect market uncertainty and their index incorporates

both types of policy events, therefore it makes sense to use it as a proxy. Secondly, this study

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the effects of the events on implied volatility of firms. There are two reasons why I am

examining the market’s expectations on index level. Firstly, I do this because of data availability. Secondly, this study uses legislations which do not particularly target a sector or

industry, but the financial market as a whole.

This study use the VIX as a proxy for market’s expectations of risk. This is because

the VIX is a forward-looking index and incorporates the market’s sentiment. If there is an

announcement of a policy event, the future expectations of the market can change. The

efficient market hypothesis implies that testing for anticipated or existing regulation is

useless. This is because it will already be incorporated in the stock price (Schwert, 2013, pp.

121 - 159). However, if a policy event is unexpected, this change in expectations might imply

a change in the VIX. To test whether fiscal policy events and monetary policy events affect

the VIX, this study uses five dummy-variables. The first regression will show whether the

announcement of a policy event in general is associated with a change in the VIX. This

change could be due to both types of events and it is not specified what exactly is happening.

The Act could have either been proposed, passed Congress or being adopted by the president

on the event date. Because of the probability effect, this study divides the Event variable in

four different dummy categories.

This study will find out if announcement of the act has an effect on the VIX.

Secondly, I test what the effect is on market uncertainty when the act passes Congress.

Lastly, I test what the effect is of the act being adopted on the VIX. The reason why this

second test is conducted, is because there might be a need to incorporate the probability of an

event.

This study also tests if monetary policy events affect market uncertainty. A monetary

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rate. As a proxy for monetary uncertainty, the index of Baker et al. (2013) can be used

because the authors also include monetary policy events.

Veronesi et al. (2012) examine whether political uncertainty affect the stock market. They

divide uncertainty into two categories which are changes in policy and the impact of a policy.

Furthermore, they divide the effects of impact in two ways; on the one hand they separate the

policies that have a negative impact on the stock prices and on the other hand changes that

have a positive impact on stock prices. this study partly extends their study but uses fiscal

policy events to examine whether they effect uncertainty on the short run. The short run is a

more interesting period because of the ‘fads hypothesis’. This means that stock markets might

overreact to relevant news. Such overreactions lead to momentum over the short time

horizons. Subsequent correction of the overreaction leads to poor performance following a

good performance and vice versa (Bodie et al. 2014, pp.364 - 365).

In another study, Veronesi et al. (2014). Examine the effects of political uncertainty on

option prices. in this study, they define political uncertainty as the uncertainty of global

summits and presidential elections. Furthermore, they test whether political uncertainty affect

the VIX. Also they examine whether the historical variance is smaller than the

implied-variance so that there is tail-risk associated with these events. I extend their study by looking

at the effects of policy uncertainty on the VIX. However, I define political uncertainty in a

different way by looking at legislations and monetary policy.

This study is restricted to the US equity market. This is because of two reasons. The

monetary and policy events take place in the United States. Secondly, because of data

availability. The United States Government keeps a well-organized archive of all the acts

proposed and implemented. This makes the data easier to obtain and to understand. This study

is restricted to the period 2001-2014, because most of the important legislations are

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Consumer Protection Act of 2008 and the Sarbanes-Oxley Act. The results of the regression

analysis are not entirely consistent with my predictions. I find a positive, but not significant

relation between fiscal policy and the VIX Furthermore, there is a positive but not significant

relation between monetary policy and The VIX . However there is a negative and strong

relation between monetary policy and the VIX.

This thesis is structured in the following way. In the next section the literature review

will be covered. The third section will cover the empirical design of the model and the setup

of the OLS regression models. The fourth section will cover the data description. The fifth

section will cover the results. The last section will be about the discussion and further

research.

2.0 literature review

The Black-Scholes option pricing model was first introduced by Fischer Black and Myron

Scholes in their famous article in 1973. In this article, the authors argue that the price of an

option depends on the time to maturity, the stock price and other variables which are assumed

to be approximately constant during the lifetime of the option. A drawback of their valuation

model is that they assume that the variance rate of the return on the stock is constant. Because

volatility levels increase during a regression (Veronesi, P, 1999, pp.975- 1007). the

assumption that volatility is constant in the model is violated, thus making the predictions of

the model less reliable. Authors Christensen and Prabhala (1997) argue that the implied

volatility is widely regarded as the option’s market forecast of future return volatility. Implied volatility is the level of volatility that would make observed option prices consistent with the

Black-Scholes model (Sundaram, 2016 pp.329 - 335). If the option markets are efficient, the

implied volatility should be an efficient forecast of future volatility.

In order to calculate the implied volatility, the five Black-Scholes variables must be

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known. Lastly, the implied volatility is the amount of volatility that equalizes the model

option value with the observed option value (Sundaram, 2016, pp. 332 - 335). To measure

market-wide implied volatility, Dumas et al. (1993) introduced and developed the VIX. The

underlying index of the VIX is the S&P500. The level of the VIX on a random day is

determined by using various range of strike prices of options on the S&P500. This also means

that the VIX measures the skewness of the distribution, which is an indication of tail risk

(Veronesi et al. 2014). Because of this characteristic, the implied volatility can also be used to

measure risk premium. The test would be to examine whether historical variance differs from

implied variance. However, that is beyond the scope of this thesis. Another characteristic of

the VIX is that its also a measure for expectations besides a measure uncertainty (Barth and

So, 2013). The VIX uses nearby and second options with at least 8 days left and weights

them to yield a constant 30-day measure of expected volatility (Sundaram, 2016, pp.

332-335). The options used are the ones that are at-the-money and out-the-money and are both

calls and puts (Sundaram, 2016, pp. 332-335).

There are numerous events that affect the level of the VIX and changes in the VIX.

Bomfim (2003) finds that unexpected macroeconomic policy announcements increases US

equity volatility. Bekaert et al. (2013) find that lax monetary policy increases risk appetite in

the future. They furthermore find that high uncertainty and high risk aversion lead to laxer

monetary policies in the short run. Bernanke et al(2014) have similar findings when testing

for the effects of unexpected monetary policy actions and the stock market. As a proxy for

uncertainty, the authors use the federal funds futures implied volatility. There is some

evidence that the effect is large when the policy change is of a permanent nature rather than

being unexpected.

Donders and Vorst, (1996) find that implied volatility substantially drops after a

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increase before an macroeconomic announcement and tends to decrease after the

announcement has been made. Barth and So (2013), have similar findings. The authors find

that there are risk premiums embedded in prices of options that hedge against market

volatility when there is an earnings announcement due. This indicates that investors anticipate

these announcements to convey market-wide news and are averse to the increase in market

uncertainty (Barth and So, 2013, pp.1221-1257). Chen and Clements, (2007) find that the

VIX decreases after the federal reserve announces the federal fund rate. A study by Füss et al.

(2011), finds that three macroeconomic variables have an negative effect on the VIX which

are GDP, CPI and PPP. This is aligned with the findings of Veronesi (1999) who finds that

return volatility will be higher during recessions.

This study extends prior research by including fiscal policy events and use variables

that measure the expected state of the economy such as GDP and CPI as controls. As a proxy

for fiscal events this study uses legislations and acts that have passed Congress and are

subsequently adopted. It can be argued that legislations are associated with a changes in

market uncertainty. This has been shown in a study by Li et al. (2008). In this study the

authors find that the announcement of the Sarbanes-Oxley Act had a positive effect on the

return on equity. A drawback of their result is that they examine only one policy event

extensively. This result is consistent with their theory that the Sarbanes-Oxley Act leads to

enhancing the credibility of financial reports. Furthermore, the authors argue that this increase

in regulation leads to a decrease in earnings management by firms. A similar conclusion has

been drawn by Barth and So, 2013. The effects of increased regulation will lead to more

precise expectations for market participants. This study will take into account all the above

mentioned findings by the numerous authors.

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In order to test whether there is a relation between policy events and market uncertainty, the

following predictions are made. On the date of a policy event, market uncertainty changes

which means an increase in the VIX. Moreover, to take into account the probabilities of each

event, a second regression model will be used. According to the literature, the announcement

of a policy event should increase market volatility. However after the act passes Congress

approval, market volatility should be lower because the probability that the act is adopted

increases. Lastly, the act being adopted should decrease market volatility because there is no

uncertainty anymore. Moreover, monetary policy events are defined as announcements of the

Federal Fund rate. Because this is a single announcement, there is no need to take

probabilities into account. The prediction is that uncertainty decreases after the announcement

has been made. Based on the predictions, the following hypotheses are created.

Hypothesis 1: Policy events and market uncertainty are negatively related.

Hypothesis 2: The proposal of an act or legislation increases market uncertainty and uncertainty drops after the proposal is adopted.

Hypothesis 3: Monetary policy and market uncertainty are negatively related.

To empirically verify these hypotheses, the following regression models are used.

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In the first model, the VIX is the dependent variable which is used as a proxy of uncertainty.

The variable Event is equal to 1 on the date a fiscal policy event is announced, has passed the

Congress, or is signed by the president. Furthermore, this Event dummy is equal to 1 if the

FOMC announces the Federal Fund rate. All the fiscal policy events included in this model

are mentioned in the appendix. The variable GDP is a control variable and is used as a proxy

for the economic growth forecast. The data for this variable is obtained via the OECD

database. This variable is also used as a control variable in previous studies by Veronesi et al.

(2014) and Füss et al. (2011). The reason why this variable is included is to give a forecast of

economic growth. A second control variable that is used in this model is the Consumer Price

Index constructed by OECD. This is the forward-looking expectation of consumer prices. The

reason why these two growth forecast variables are included is because the VIX is also a

forward-looking index. The problem that arises between comparing realized results with

expected results is a problem of identification. Therefore, it would be better to have mostly

forward looking variables in the model. The variable Return represents the daily return on the

S&P500 and is annualized using 252 trading days

The prediction is that the variable Event is significant. This implies that the

announcement of an Act, the Act passing Congress, and the adoption of the Act have an effect

on the VIX. It implies also that monetary policy events have an effect on the VIX. A

drawback of this model is that it does not take into consideration the probability effects of

each event. To circumvent this issue another OLS regression model is used. In this model, as

mentioned before, probabilities are also taken into account. The controls are still defined in

the same way as in the previous model.

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In this model, the event variable is split-up in three other indicator variables. This is because

of the prediction that some events have a larger effect on the VIX than others. The control

variables are still defined in the same way as in the previous model. The first dummy variable

is equal to 1 on the date the act is proposed by a Representative or a Senator. This study

predicts that the effect on the VIX is positive because the announcement of an Act should

increase uncertainty about future regulation. The variable Congress is equal to 1 on the date

the Act has been passed by Congress. The uncertainty that the act will be adopted has

decreased. This study predicts that the effect on the VIX of this event is lower than the

announcement effect. Lastly, the variable Adoption is equal to 1 the act on the date the act is

signed by the president. This study predicts that the effect on the VIX of this event is negative

because the amount of uncertainty dropped to zero.

Furthermore, this study uses the index constructed by Baker et al. (2013) as a proxy

for policy uncertainty. The authors constructed this index in the following way. The authors

use the ten most prestigious newspapers in the countries. After they have established which

newspapers fit the description, they search the digital archives of each newspaper. The articles

of these newspapers should contain the following three key-words: ‘economic’, ‘uncertainty’,

and ‘legislation. To circumvent the issue that the overall volume of articles varies across newspapers and time, the authors scale these raw counts. This yields a monthly EPU series for

each newspaper. Secondly, they standardize each newspaper-level series to unit standard

deviation and then average across the ten newspapers by month. Lastly, they normalize the

ten paper series to a mean of 100 (Baker et al, 2013).

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I predict that the index constructed by Baker et al. (2013) has a positive and significant effect

on the VIX because it is similar to the Event variable in regression model 1

Lastly, I constructed a model which measures the change in market expectations with

respect to a monetary policy event. As a proxy for this event, I use the statements issued by

the Federal Open Market Committee.

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4.0 Data description

I obtained the data for the US VIX via DataStream. The VIX is an index constructed by the

Chicago Board Options Exchange (CBOE) and is measured on a daily basis. My sample

covers the period 2001 to 2014. This sample period interesting because of the financial crisis

which starts around the fourth quarter in 2008. In this period, the amount of uncertainty was

especially high because of the uncertainty in the banking sector. Citizens demanded tighter

financial regulations, and because of that the government implemented the Dodd Frank Act.

The data for the acts and legislations are from U.S. Government Publishing office. All the acts

have a timeline that indicate on which date the act is announced, has passed Congress, and is

adopted. This study uses the following methodology to find the acts to include in the dataset.

Firstly, the study finds all the acts proposed and also implemented by the government. After

the archive is located, a similar filter method is used as the one in the article by Baker et al.

2013. Acts need to contain three key words before it can be considered as an observation.

These are for example Tax, Debt, and Economy. If an act incorporates these words, it can

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acts are explicit targeting the financial markets, then they will included in the dataset. Data

for the Federal fund rate is obtained from the Board of Governors of the Federal Reserve

System. The Federal Reserve organizes the Federal Open Market Committee approximately

eight times per year. (Mishkin, F, 2011, pp.331 – 340 ). The data on the CPI forecast and the

GDP forecast are calculated by the OECD. The forecasts are based on a large numbers of

parameters and the OECD uses this information to calculate the composite leading indicators

(CLI). CPI forecast and GDP forecast are two of these composite leading indicators.

After finding the right data, there were still some problems before arriving at the final

dataset. The dataset needed to be prepared for testing. This was necessary because there are

numerous days on which no trading occurs. I circumvented this issue by using Excel and

deleting the dates on which there was no ‘perfect match’ using a Pivot table. That is, all variables must be present on a certain day. If this were not the case, I would delete the entire

column in Excel. My final dataset consists of approximately 3500 observations from the

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5.0 Results

Table 2 represents the OLS, set for robust standard errors, of the four above mentioned

regressions [insert table 2 here]. I find that the coefficient Event is 0.51.which implies that a

policy event is positively related to uncertainty. This coefficient is not significant at a 5%

critical value. A drawback of this model is that it does not take into account the different

probabilities of each event. This would imply that without accounting for probabilities of

different events, there is no significant change in the market expectations resulting from a

change in fiscal or monetary policy. The output for this regression can be found in column 1

of table II.

The second regression model, which accounts for probabilities of each fiscal event and

its impact on market expectations, finds that the three coefficients, which measure a fiscal

policy event, have the predicted sign but the result is not significant. The coefficient for

proposal is 0.436. This implies that a proposal of an certain Act or Legislation has a positive

effect on market uncertainty, however this effect is not significant at a 5% critical value. The

coefficient for Congress is 0.570, which implies that the Act passing the Senate has a positive

effect on market uncertainty, however is not significant at a 5% critical value. Lastly, the

coefficient Adoption is -0.025 which indicates that the Act being signed by the president

decreases market’s uncertainty. These results are consistent with my predictions. I predicted that the uncertainty would increase from the moment the act is being proposed and

subsequently drop when the act is adopted. But because the coefficients are not significant at

a 5% critical value no empirically sound conclusion can be drawn from these results. The

output of this regression can be found in column 2 of table II

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uncertainty, as the explanatory variable. I find a coefficient of 0.047, which is also highly

significant at a 5% critical value. The reason for this might be because the authors use a

different way to measure political uncertainty. The result is consistent with my first

hypothesis, that policy events have an effect on market uncertainty. The details of this

regression output can be found in column 3 of table II

The fourth regression tests whether monetary policy events have an effect on market’s expectations. I find that the coefficient is 0.968 which is positive and also significant at a 5%

critical value. This result is not consistent with my predictions because I predict that

monetary policy and market uncertainty are negatively related. The details of this regression

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6.0 Robustness Test

A drawback with all the models described above, is that the variables are measured on a daily

basis while the GDP forecast and CPI forecast are measured on annual basis. To circumvent

this issue, I construct four new models which measures the specifications in changes; it only

includes variables which are observed on a daily basis. The dependent variable in this test is

the change in VIX. The explanatory variables are the same as in the first three models. The

crucial difference between these models and the previous other four models is that I do not

include the control variables.

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The coefficient of Event is -0.013 which indicates a negative effect on market uncertainty and

it is also significant at a 5% critical value. This means that an policy event is negatively

related with a change in market’s expectations. To take into account the probabilities of each

event, I divide the Event variable in the three fiscal policy variables which are the same

variables from the second regression model. However, the dependent variable is now the

change in VIX. Furthermore, I leave out the control variables.

(6) I find a coefficient of -0.0009 for Proposal which indicates a negative but not significant

effect of the proposal of an act on the change in market uncertainty. Furthermore, I find a

coefficient of -0.0085 for congress which indicates a negative effect of the act passing

Congress on the change in market’s uncertainty. However, this coefficient is not significant at a 5% critical value. Lastly, I find a coefficient of 0.007 for Adoption, which indicates a

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significant at a 5% critical value. Moreover, I test if there is a relation between the change in

the EPU and the change in VIX.

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I find a very low coefficient of -0.238 for FED. This coefficient is also significant at a 5%

critical value. The sign of the coefficient indicates that market uncertainty and announcements

of the federal fund rate are negatively related. The reason for this could be is that uncertainty

drops immediately after the FOMC issues their statement about the federal fund rate. Because

the announcement is made right after the decision has been made, the level of uncertainty

drops on the same date of the meeting.

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I find a coefficient of -0.00014 for which implies that the change in the EPU and the change in VIX are negatively related. This coefficient is not significant at a 5% critical value.

The reason why this coefficient might be insignificant is because the newspapers do not

usually report the same event twice only if it is a continuous event which needs regular

updates. Because a newspaper does not report the next day about yesterday’s announcement,

it does not mean that uncertainty has decreased in the markets. There still might some

uncertainty in the market, but it is not reported in the newspapers.

In conclusion there could be several reasons why there are no significant results to be found. .

A reason why the results are not significant could be due to the distribution of the

observations and other testing fallacies. I have used the Log of the VIX and the log of other

independent variables to test if the non-normal distribution could be the reason for the

insignificant result. Furthermore, I have also used the Decile Rank function in Stata to find

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however did show that a monetary policy event implies a drop in the VIX and this was also

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7.0 Conclusion

The purpose of this study is to examine whether policy events have an effect on market

uncertainty. I find that this is not the case for fiscal policy events. There could be several

reasons why there is no effect. Firstly, the announcement was not unexpected. Academic

literature tells us that a shock should be unexpected because otherwise the effect is already

incorporated in the stock price. A second reason could be is that markets did not regard the

event as a reason for uncertainty. With regards to monetary policy, I find that the statements

issued by the FOMC about the Federal fund rate lowers market uncertainty and this result is

significant. This can be derived from the fact that the relation between this event and the VIX

is negative. Furthermore, I find that the EPU constructed by Baker et al. (2013) is positively

related to the VIX in levels. In other words, when the market’s expectations are high, the policy index is also high. By using the EPU as a proxy for policy events, my prediction that

policy events affect market’s expectations is correct. However, I find a very weak relation between the change in the EPU and the change in VIX. This result is not consistent with my

predictions. It can be argued that the results may differ when taking into account the recession

period in 2008 to 2009. However, because of data availability, the study cannot isolate the

2008 to 2009 period because there not enough policy events in that period to have a good

sample. The statistical power would be too weak.

A topic for further research could be to test whether the announcement of a regulatory

reform is expected or not. This could increase the precision of which each event is measured.

This study does not incorporate the fact that some Acts are already expected among market

participants before it is publicly known. To circumvent this issue, the VIX should be

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Reference list

Bakeart, G., Hoerva, Marie., Duca, Marco. (2013). Risk, Uncertainty and Monetary

Policy. Journal of Monetary Economics 60, pp. 771-788.

Baker, S., Bloom, N., Davis, S., (2015, October). Measuring Economic Policy

(Working Paper No. 21633). Retrieved from National Bureau of Economic Research

website: http://www.nber.org/papers/w21633.

Barth, M., So, E. (2013, January). Non-diversifiable Volatility Risk and Risk

Premiums at Earnings Announcement. The Accounting Review 89(5), pp. 1579 – 1607.

Bernanke, B., Kuttner, K. (2004, March). What Explains the Stock Market’s Reaction

to Federal Reserve Policy? The Journal of Finance 60(3), pp. 1221- 1257.

Black, F., Scholes, M. (1973, June). The Pricing of Options and Corporate Liabilities.

The Journal of Political Economy, 81(3), pp. 637 – 654.

Bodie, Z., Kane, A., Marcus, J. (2014). Investments. New York, NY: Mcgraw-Hill

Education

Bomfim, A. (2003). Interest Rates as Options: assessing the market’s view of the liquidity trap. Report of the Federal Reserve Board.

(22)

Chen, J., Clements, A. (2007). S&P500 Implied Volatility and Monetary Policy

Announcements. Finance Research Letters 4, pp. 227-232.

Christensen, B., Prabhala, N. (1997, December). The Relation between Implied and

Realized Volatility. Journal of Financial Economics, 50, pp. 125 – 150.

Donders, M., Vorst, A. (1994, March) Statistica Neerlandica 50(1), pp. 52-68.

Dumas, B., Jeff, F., & Whaley, R. (1998, December). Implied Volatility Functions:

Empirical Tests. The Journal of Finance, 53(6), pp. 2059-2106.

Füss, R., Mager, F., Wohlenberg, Holger., Zhao, Lu. (2011). The Impact of

Macroeconomic Announcements on Implied Volatility. Applied Financial Economics

21(21), pp. 1571-1580.

Kelly, B., Pastor, L., Veronesi, P. (2014, January). The Price of Political Uncertainty:

Theory and Evidence From the Option Market (Working Paper No. 19812). Retrieved

from National Bureau of Economic Research website:

http://www.nber.org/papers/w19812.

Li, Haidan., Pincus, M., Rego, S. (2008). Market Reactions to Events Surrounding the

Sarbanes-Oxley Act of 2002 and Earnings Management. The Journal of Law and

Economics 51(1), pp. 111-134.

Mishkin, F. (2012). The Economics of Money, Banking and Financial Markets, New

(23)

Nikkinen, J., Omran, M., Aijo, J., Sahlstrom, Petri.(2006). Global Stock Market

Reactions to Scheduled U.S. Macroeconomic News Announcements. Global Journal

of Finance 17, pp. 92-104.

Pastor, L., Veronesi, P. (2012, August). Uncertainty about Government Policy and

Stock Prices. The Journal of Finance, 67(4), pp. 1219 – 1264.

Schwert, G. (1981). Using Financial Data to Measure Effects of Regulation. Journal

of Law and Economics, 24(1), pp. 121 – 159.

Sundaram., R. Das, S.(2016). Derivatives: principles and practice. New York, NY:

Mcgraw-Hill Education

Veronesi, P. (1999). Stock Market Overreaction to Bad News in Good Times: A

rational Expectations Equilibrium Model. Review of Financial Studies 12(5), pp.

975-1007.

Appendix

Statutes - Acts and legislations that passed Congress and were subsequently signed by the

President.

Accountant, Compliance, and Enforcement Staffing Act of 2003

American Jobs Creation Act of 2004

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American Taxpayer Relief Act of 2012

Economic Growth and Tax Relief Reconciliation Act of 2001

Emergency Economic Stabilization Act of 2008

Fraud Enforcement and Recovery Act of 2009

The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010

The Sarbanes-Oxley Act of 2002

Budget Control Act of 2011

Consolidated and Further Continuing Appropriations Act of 2015

Consolidated Appropriations Act of 2012

Creation Act of 2010

Economic Stimulus Act of 2008

Federal Funding Accountability and Transparency Act of 2006

Hiring Incentives to Restore Employment Act of 2010

Housing and Economic Recovery Act of 2008

Improper Payments Elimination and Recovery Act of 2010

Investor and Capital Markets Fee Relief Act of 2002

Jobs and Growth Tax Relief Reconciliation Act of 2003

Mortgage Forgiveness Debt Relief Act of 2015

No Budget, No Pay Act of 2013

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Premier Certified Lenders Program Improvement Act of 2004

Preserving Independence of Financial Institution Examinations Act of 2003

Reverse Mortgage Stabilization Act of 2013

Small Business Investment Act of 1958 through March 15, 2004

Small Business Investment Company Amendments Act of 2001

Tax Relief, Unemployment Insurance Reauthorization, and Job

The Creating Credit Rating Agency Reform Act of 2006

The Credit Card Accountability Responsibility and Disclosure Act of 2009

The Insurance Capital Standards Clarification Act of 2014

Undertaking Spam, Spyware, And Fraud Enforcement With Enforcers beyond Borders Act of

2006

Working Families Tax Relief Act of 2004

Other fiscal events

Government shutdown of 2011

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Table I Descriptive Statistics

Variable | Obs Mean Std. Dev. Min Max

IV is the implied volatility obtained from Datastream. It is the daily implied volatility of the S&P500 index. dIV is the daily change in the implied volatility calculated in Excel. EventAll is a dummy variable that includes both monetary as policy events. The FedRate represents the dummy that is equal to one on the day of the announcement by the FOMC. EPU is the index calculated by Baker Et al. (2013. dStock is the daily return of the S&P500. GDP forecast is the forecast predicted by the OECD. CPI is the consumer price index forecast by the OECD.

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Table II

Uncertainty and Fiscal and Monetary Policies – OLS regression Robust Dependent variable: VIX

Regressor model 1 model 2 model 3 model 4

Event 0.51 (0.55) Date of Proposal 0.436 (1.15) Congress 0.570 (1.18) Adoption -0.025 (0.99) EPU 0.047 (0.00) FedRate 0.968 (0.76) GDP Forecast -3.45 -3.45 -3.45 -2.90 (0.13) (0.13) (0.09) (0.11) Return Index -84.30 -84.13 -84.43 -87.94 (24.30) (24.29 (10.53) (21.12)

Consumer Price Index 119.40 119.52 119.25 110.89

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Table III

Uncertainty and Fiscal and Monetary Policies – OLS regression Robust Dependent variable:

Regressor model 5 model 6 model 7 model 8

Event -0.013 (0.00) Date of Proposal -0.0009 (0.00) Congress -0.0085 (0.01) Adoption 0.007 (0.01)

Federal Fund Rate -0.02

(0.00)

EPU -0.0004

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