2 Evidence from years 2008 - 2022 UNIVERSITY OF VAASA School of Accounting and Finance Author: Jussi Virkajärvi Title of the Thesis: The Fed’s Monetary Policy Decisions and Stock Market Uncertainty Degree: Master of Science in Finance Programme: Finance Supervisor: Timo Rothovius Year: 2022 Pages: 48 ABSTRACT: In the 2020s financial markets have faced unseen wavering due the breakout of Covid-pandemic which eventually led into massive easing-programs from central banks. The Central bank of United States, The Federal Reserve, decisions have significant impact on stock markets, and the uncertainty related on the stock markets, through monetary policy decisions. In this study, I examine the Federal Open Market Committees, FOMC, decisions impacts on the volatility index VIX which is measured from the S&P 500 index on the announcement date of FOMC monetary policy decisions. Additionally, to previous studies, this study also examines whether the introduction of the press conference after FOMC announcement have an impact on the implied volatility. The sample period used in this study includes the years 2008 – 2022 and during sample period 125 FOMC meetings were held. Out of these meetings 117 were scheduled meetings and 8 were unscheduled meetings. The sample period includes 3707 daily quotes in the volatility index VIX. This thesis is following the study of Vähämaa and Äijö (2011) in which they studied the impact of FOMC monetary policy decisions on the uncertainty of stock markets. Both the result of their study and many other studies indicates that the volatility index VIX tends to decrease after FOMC monetary policy announcement, but previous literature also suggest that surprises related to monetary policy tend to increase the VIX index. The increase is driven by the surprises in the unscheduled meeting monetary policy announcements. (Chen & Clements, 2007a; Gospodinov & Jamali, 2015; Kurov, 2010; Nikkinen & Sahlström, 2004). Ordinary least square (OLS)- regression analysis indicates that the impact of FOMC monetary policy announcement is decreasing the volatility index VIX, which supports the findings from previous studies. The volatility measured from the S&P 500 tends to decrease after meetings and this finding is also statistically significant. Unlike many previous studies, this thesis is unable to identify statistically significant correlation between monetary policy surprises and implied volatility on FOMC announcement days. There were no significant correlations when examining the impact of the press conference, which indicates that it doesn’t play a significant role in stock market uncertainty. Widening the sample period and measuring the element of surprise with different methods is something that should be considered in future studies. KEYWORDS: Fed, implied volatility, S&P 500 index, Fed Fund Rate, Fed Fund Futures. UNIVERSITY OF VAASA Laskentatoimen ja rahoituksen yksikkö Tekijä: Jussi Virkajärvi Tutkielman nimi: The Fed’s Monetary Policy Decisions and Stock Market Uncertainty Tutkinto: Kauppatieteiden maisteri Oppiaine: Finance Työn ohjaaja: Timo Rothovius Valmistusmisvuosi 2022 Sivumäärä: 48 Tiivistelmä: 2020-luvulla rahoitusmarkkinat ovat kokeneet ennennäkemätöntä heiluntaa Covid-pandemian puhkeamisen johdettua lopulta massiivisiin elvytystoimiin keskuspankkien toimesta. Yhdysvaltojen keskuspankin The Federal Reserven rahapoliittisilla päätöksillä on merkittävä vaikutus rahoitusolosuhteiden kautta osakemarkkinoihin ja osakemarkkinoihin liittyvään epävarmuuteen. Tässä tutkimuksessa tarkastellaan Fedin rahapolitiikasta päättävän arvomarkkinakomitean (Federal Open Market Committee, FOMC) rahapoliittisten päätösten vaikutuksia S&P 500 indeksistä johdettuun volatiliteetti-indeksi VIX:n muutoksiin arvomarkkina-komitean päätösten julkistuspäivänä. Lisäksi tässä tutkimuksessa tutkitaan FOMC:n vuonna 2011 esitellyn lehdistötilaisuuden vaikutusta volatiliteettiin. Tässä tutkimuksessa käytetään aineistoa vuosilta 2008–2022 ja tutkimusajanjakson aikana FOMC on kokoontunut yhteensä 125 kertaa. Näistä kokoontumisista 117 on ollut kalenterinmukaisia suunniteltuja kokoontumisia ja 8 kokousta on ollut kalanteroimattomia. Lisäksi aineisto käsittelee 3707 volatiliteetti-indeksi VIX:n päätösarvoa. Tutkimus perustuu pitkälti Vähämaan & Äijön (2011) tekemään tutkimukseen, jossa he tutkivat Fedin rahapoliittisten päätösten vaikutusta osakemarkkinoihin liittyvään odotettuun epävarmuuteen. Sekä heidän, että muiden aiheeseen liittyvien tutkimusten tulokset osoittavat, että osakemarkkinoiden volatiliteetti laskee arvomarkkinakomitean kokousten jälkeen, mutta yllättävät rahapoliittiset päätökset, esimerkiksi odotettua suurempia koronnosto, vaikuttaa VIX-indeksiin nostavasti. Tutkimukset osoittavat, että ennen kaikkea ennakkoon suunnittelemattomiin kokouksiin liittyvät yllättävät rahapoliittiset päätökset nostavat odotettua volatiliteettia merkittävästi. (Chen & Clements, 2007a; Gospodinov & Jamali, 2015; Kurov, 2010; Nikkinen & Sahlström, 2004). OLS-regressioanalyysi osoittaa, että arvomarkkinakomiteon päätöksenteon vaikutukset ovat linjassa aikaisempien tutkimustulosten kanssa. S&p 500 indeksistä mitattu volatiliteetti laskee tilastollisesti merkittävästi arvomarkkinakomiteon päätöksenteon jälkeen. Päätöksentekoon liittyvien yllätysten merkitystä ei voitu arvioida merkittävin tuloksin eikä lehdistötilaisuuden vaikutuksista saatu merkittäviä tuloksia. Voidaan siis todeta, että päätöksenteko rauhoittaa osakemarkkinoita eikä lehdistötilaisuudella ole ollut merkittävää vaikutusta osakemarkkinoiden epävarmuuteen. Tarkempien tutkimustulosten saamiseksi, tutkimusaineistoa tulisi laajentaa entistä suuremmaksi ja tutkimuksen vaikutuksia pidemmän aikavälin epävarmuuteentutkia tarkemmin useammalla eri tavalla mitatulla yllätyskomponentilla. AVAINSANAT: Fed, odotettu volatiliteeti, S&P 500 indeksi, Yhdysvaltain keskuspankin yön yli -korko, Fed Fund -futuurit Contents 1 INTRODUCTION 7 1.1 Purpose of the study 8 1.1.1 Formulation of hypotheses 9 1.2 Structure of the study 10 2 THEORETICAL BACKGROUND 11 2.1 Monetary policy and stock market 11 2.1.1 The S&P 500 Index 12 2.1.2 Implied Volatility Index - the VIX 13 2.2 Federal Open Market Committee 15 2.3 Federal fund target rate 17 2.3.1 Federal Fund Futures contract 18 3 LITERATURE REVIEW 21 3.1 Monetary policy and market responses 21 3.2 Monetary policy surprises and implied volatility 23 4 DATA AND METHOLOGY 27 4.1 Methodology 27 4.2 Descriptive statistics 31 4.3 Explanatory variables 33 5 EMPIRICAL RESULTS 37 5.1 Surprises with scheduled and unscheduled meetings 37 5.2 Additional regressions 40 6 CONCLUSIONS 44 References 46 Figures Figure 1: Historical levels of the S&P 500 Index (Bloomberg, 2022) 13 Figure 2: History of the Volatility index, VIX. (Bloomberg, 2022) 15 Figure 3: History of Federal Fund Target Rate. (Bloomberg, 2022) 18 Figure 4: Historical Fed Fun Futures contract prices. (Bloomberg, 2022) 20 Tables Table 1: Descriptive statistics 32 Table 2. Regression results (models 1–7) 38 Table 3. Regression results (models 8–13) 42 Table 4. Full results of regression models 43 Abbreviations FED = Federal Reserve Bank FOMC = Federal Open Market Committee VIX = Implied Volatility Index S&P 500 = Stock market index including 500 large exchange listed companies in U.S. CBOE = Chicago Board Options Exchange QE = Quantitative Easing QT = Quantitative Tightening INTRODUCTION In United States the central bank’s monetary policy actions have been a very topical subject in stock market especially in the 2020s, where we have first seen massive quantitative easing (QE) and on the other hand recently quantitative tightening (QT). These changes in monetary policy, monetary policy decisions, are usually associated with large movements in stock markets and stock market indexes. The causality with monetary policy decisions and changes in stock markets has been extensively examined and therefore it is commonly known that unexpected changes in monetary policy effect on stock prices. After 2007 the world economy has seen a massive period of expanding monetary policy in form of QE, especially during the aftermath of financial crisis and COVID-19 pandemic. Compared to sample period of earlier study conducted by Vähämaa & Äijö (2011), the circumstances in monetary policy making have changed dramatically: the value of assets in the balance sheet of the Federal Reserve has grown from 0.88 trillion $ to 8.76 trillion $ during January 2008 to December 2021 (Federal Reserve, 2022a). While the balance sheet of the Federal Reserve has grown in aftermath if the financial crisis and Covid-pandemic, the transparency of its policy making have increased compared to sample period of earlier study. This thesis is a replica on earlier study The Fed’s Policy Decisions and Implied volatility (Vähämaa & Äijö, 2011), which examines how does the Fed’s monetary policy decision affect the implied volatility of the S&P 500 index. Earlier study was conducted with data from January 3rd, 1994, through December 31st, 2007, but this extended study will use data from January 2008, through September 2022. In this study, the whole sample period can be viewed as time of expansionary monetary policy and for this reason, the effect of the monetary policy cycle is not investigated in this study. Purpose of the study The purpose of this study is to examine how does the Federal Reserve’s policy decisions impact on the stock market implied volatility. Earlier studies, which we will discuss more in chapter three, suggest that implied volatility is affected significantly by Fed’s monetary policy decisions (see e.g., Chen & Clements, 2007; Nikkinen & Peltomäki, 2020). Vähämaa & Äijö (2011) report similar findings in their study. However, their study does not include the factor whether there was a change in monetary policy (in form of change in Federal Fund target rate) or not. This study will include the factor whether monetary policy decision included a change in Federal Fun target rate and hence provide additional information regarding the monetary policy decision impact on monetary policy uncertainty. Secondary purpose for this study is to find out whether the impact of FOMC meetings on implied volatility has changed due the increased amount of forward guidance by Federal Reserve. FOMC has increased the transparency of its decision making by releasing the post-meeting statement, since February 1994, in which it announces the outcome of the meetings. Votes of individual FOMC members have been included in the statement since 2002. The FOMC meetings minutes are published with a three-week lag after the meeting since December 2004. After meetings a press conference was introduced for the first time after meeting held on 27.4.2011. Before introducing the press conference, a post-meeting statement was released without giving the media any change to question the Fed chair about policy decision. The press conference gives the media representatives the opportunity to ask the chair more detailed questions about the background to the decision-making. The market follows the course of the press conference and interprets the signals of the chairman's statements. A hawkish interpretation of the forward guidance raises the market's interest rate expectations, while a dovish message lowers interest rate expectation. For this reason, the interpretation of the forward guidance also affects the stock market and hence the implied volatility. Thirdly, unlike in multiple previous research such as Basistha & Kurov, (2008; Bernanke & Kuttner, (2005) and Vähämaa & Äijö (2011) This study will not just exclude the policy decisions made after crisis, such as terrorist attack on September 17th, 2001. In this study, I partially equate a terrorist attack with monetary policy decision-making after the Covid pandemic in March 2020. Instead, as the presence of the possible pandemic was known forehand, the outbreak cannot be compared to terrorist attacks as they were unexpected. Hence, the crisis in March 2020 and FOMC meetings held during the time of Covid outbreak are included in the sample used in this study. Formulation of hypotheses Based on the implication of the existing literature, discussed more in chapter 3, and the purpose of the study, the main research question of this thesis is does the Fed’s monetary policy announcement impact on stock market implied volatility. The null hypothesis of this study is: H0: The Fed’s monetary policy announcement has no impact on the stock market implied volatility The contradictive hypotheses are as follows: H1: The Fed’s monetary policy announcement decreases the stock market implied volatility H2: The Fed’s monetary policy announcement increases the stock market implied volatility For example, Vähämaa and Äijö (2011) find significant decrease in the VIX index after Fed’s monetary policy announcements. Respectively, they also find significant increase in implied volatility if the monetary policy decision is associated with a surprises component. These hypotheses of this study are later examined and analyzed in chapter 5. Structure of the study The structure of the study is as follows: introduction is followed by theoretical background that builds the foundation to this research. Theoretical background focuses on the three main topics: Monetary policy and stock market, Federal open market committee meetings and federal fund futures. After the theoretical background, we move on to the literature review. Literature review focuses previous studies around the FOMC meetings impact on stock market and implied volatility. Literature review is followed by data and methodology chapter which is the starting point of the empirical analysis part of this study. Afterwards, I move on to the empirical results chapter where I test the hypotheses. Finally, results are concluded in the conclusion chapter. THEORETICAL BACKGROUND In this chapter I go through the most essential factors for this study. This chapter includes introducing the basic causalities between monetary policy decisions and stock market reactions. I also go through the key indexes which are included most of the previous literature: the S&P 500 equity index and implied volatility index VIX. Later in this chapter I introduce Federal Open Market Committee and finally Federal Fund Futures which are used to identify surprises in monetary policy decision making. Monetary policy and stock market According to the existing literature, a central bank’s monetary policy decisions are associated with large movements in stock prices. The Change in monetary policy is implemented via a change in Federal Fund target rates which have a positive correlation between market interest rates i.e., an increase in the Fed Fund target rate increases market interest rates (Ellingsen & Södeström Ulf 2001). Therefore, an increase in the Federal fund target rate leads to decrease in stock prices and a decrease in the Federal fund target rate leads to an increase in stock prices. To simplify the mechanics of interest rate change impact on stock prices, let us look of one of the most used valuation models, the discounted cash flow model (DCF). The DCF-model is used to calculate the present value of future cash flows and it is widely used to calculate the value of an individual company and therefore it can be used to value individual stock prices, too. The formula of FCF is as follows: (1) Where: = The cash flow for year one = The cash flow for year two = The cash flow for additional years, until year n r = The discount rate. Thus, changes in the discount rate r, have impact on present value of future cash flows and the further we go into future, the greater the impact is. In growth companies incoming cash flows are taking place in the future which means that the discount factor on future cash flows has higher impact on them. The impact of Federal fund target rate movement on specific stock is highly dependent on the characteristics of the stock. Value stocks tend to be less vulnerable on changes in interest rate while growth stocks are more volatile on nature. Similar effects take place in commonly used capital asset pricing model CAPM. The S&P 500 Index The Standard and Poor’s 500, more commonly known as S&P 500, is globally one of the most followed equity indexes and it tracks the stock performance of 500 large exchange listed companies in The United States. It is regarded as one of the best gauges of American equities’ performance and hence overall stock market performance. It is most known rival indexes are Dow Jones Industrial Average (DJIA) which includes 30 large, publicly owned, and traded blue-chip companies and Nasdaq Composite Index (COMP) that includes over 3 700 stocks listed on the Nasdaq stock exchange. Out of these three indexes S&P 500 index is considered the most thorough because of its depth and diversity. The S&P 500 index is widely used in literature when stock market performance is measured. (S&P Global, 2022). The fact that the VIX is measured based on the S&P 500 index options makes it extremely relevant in studies related on implied volatility (CBOE, 2022). Historical prices of S&P 500 index are shown in figure 1. The Index fell to its lowest on sample period during 2008 in the aftermath on the financial crisis. After the crisis, the growth of the stock market index has been relatively steady until 2020 and Covid-pandemic. After the dramatical decrease in spring 2020, massive quantitative easing packages by Federal Reserve led to rapid increase in S&P 500 index. The index started decreasing during 2022 when high inflation led to restrictive monetary policy and rate hikes. Figure 1: Historical levels of the S&P 500 Index (Bloomberg, 2022) Implied Volatility Index - the VIX The Chicago Board Options Exchange’s (CBOE) Volatility Index (VIX), also known as market’s “fear gauge,” was introduced in 1993 by Robert Whaley in The Journal of Derivatives (Whaley, 1993). While the S&P 500 index measures price, VIX index measures volatility, future volatility to be more exact. Elevated levels of the VIX indicates investor anxiety regarding a potential drop in stock prices i.e., it measures the level of uncertainty. In existing literature, the VIX is widely used to measure the level of uncertainty in the stock market (see. e.g., Whaley, 2000, Banerjee et al., 2007, Nikkinen & Peltomäki, 2020, Chicago Board Options Exchange, 2022). The VIX index is based on prices of S&P 500 Index options, and it is quoted by CBOE based on the live market data. CBOE constructs the VIX index from S&P 500 put and call options over a wide range of strike prices and the VIX Index itself therefore represents the expected volatility of underlying S&P 500 index over the next 30 days. (CBOE, 2022). In other words, the level of the VIX is implied by the prices of the options of the S&P 500 at the current time and it represents expected volatility of stock market over the 30 calendar days. Therefore, the VIX is perfect measurement of market uncertainty. During market turmoil’s, the VIX is spiking. It is precisely why this feature that the VIX has come to be called the "investors' fear gauge". (Whaley, 2009). The VIX has been spiking throughout its history during the extraordinary events such as the financial crisis in 2007-2009, breakout of Covid-pandemic in 2020 or during 2022 crisis including high Inflation and the Ukrainian War. In times of great uncertainty, such as the financial crisis in, (spiking VIX), investors need a higher rate of return for their investments to cover the level of risk which is affected positively by higher level of uncertainty. Therefore, investors are willing to pay less for the stocks compared to time of less uncertainty in the market which usually leads to decrease in stock prices. History of the VIX index is shown in figure 2. Spiking levels of the VIX index can be detected in the late 2008 when the uncertainty caused by financial crisis was at its highest level. The second significantly high spike in the VIX index can be seen taken place in the spring of 2020 when Covid-crisis turned into a global pandemic. In figure 2, we see the historical values of the S&P 500 stock index. Figure 2: History of the Volatility index, VIX. (Bloomberg, 2022) Federal Open Market Committee In United States monetary policy as a term refers to actions undertaken by the Federal Reserve system to influence the cost and availability of money and credit. The Federal Reserve System is in control of the monetary policy, and hence, is responsible for maintaining both price stability and sustainable economic growth. To achieve these goals, the Federal Reserve oversees three tools of monetary policy: 1) open market operations, 2) the discount rate and 3) reserve requirements. These actions are taken to promote the economic goals of the Nation. The mendacity of Federal Reserve to be responsible for setting monetary policy in United States was set in 1913 by The Federal Reserve Act. Federal Reserve system practices its’ monetary policy through Federal Market Open Committee, FOMC, that consist of twelve members, including the president of Federal Reserve Bank. The FOMC was added as part of Federal Reserve System with legislation in 1933 and 1935 The FOMC is responsible for overseeing open market operations, which affect the federal fund rate, the size and composition of Federal Reserve’s asset holdings and forward guidance of future monetary policy towards public. The decisions of the FOMC affect to overall monetary and credit conditions through the Federal Fund rates which affects the entire economy. (Federal Reserve, 2022b). The Federal fund rate is the rate at depository institutions lend to each other and it will be looked closer later in this chapter. Through the asset holding tool, the Federal Reserve can impact on public interest rate expectations. By purchasing assets such as the longer-term Treasury securities i.e., increasing the amount of these securities in Federal Reserve’s balance sheet, the Fed reduces the amount of these securities that public would have held otherwise. By reducing supply of these securities, Fed can lower the yield of these securities in markets and hence lower the yield curve. Vice versa, by reducing the longer-term Treasury securities from its balance sheet, the Fed can increase the amount held of securities held by public and by increasing the supply, thus increasing the corresponding yield of these securities. Quantitative easing (QE) was done by massive asset purchase programs where the Fed acquired a massive number of securities into in its balance sheet. This action stabilized markets by providing extended liquidity on markets. Federal Reserve’s QE program has continued been extended on multiple occasions after introduction of it in the aftermath of the financial crisis. The latest period of quantitative tightening was introduced in early 2022 when Fed started to reduce the number of securities in its fight against increased inflation. While asset purchases and holdings impact to the economic environment via true actions, forward guidance can be used to impact on the future expectations of the public and hence to the economic situation. Forward guidance is a tool used by the Federal Reserve to provide communication to the public about the future course of its monetary policy i.e., to increase the transparency of monetary policy. Communications have become an integral part of the monetary policy toolkit (Blinder et al., 2008). Forward guidance is used to prevent surprises that might disturb the markets and cause fluctuation in asset prices as changes in the Federal fund target rate impacts on valuation of multiple instruments such as stocks and bonds. The FOMC has released a statement where its monetary policy decision is announced and explained immediately after each of its meetings. The detailed minutes are released after three weeks of each meeting which gives a close insight of the discussions during held meetings. The minutes became available in between the FOMC meetings in early 2000s. A quarterly press conference held by the Chair of the Fed is following certain FOMC meetings. The First after meeting press conference was held by the Chair Ben Bernanke on April 27th in 2011. (Federal Reserve, 2022c). The findings of Gospodinov & Jamali (2015) imply that Fed can influence on the stock market volatility through forward guidance or better communication (Gospodinov & Jamali, 2015). The FOMC holds yearly eight scheduled meetings at which the Committee reviews financial and economic conditions. Based on these reviews the Committee determinates the appropriate stance of monetary policy to fulfill its’ goals. According to the FOMC meeting current policy, the meeting is followed by a publication of the monetary policy decisions and a press conference that were introduced for the first time in 27.4.2011. During the press conference the chair of the Fed, currently Jerome Powell, introduces the topics discussed in the meeting and answer to the questions presented by media representatives. The increasing amount of forward guidance can have an impact on the quality of monetary policy surprises in current sample period of this study compared to sample period of the study conducted by Vähämaa & Äijö (2011) for example. Federal fund target rate The Federal fund target rate, Fed fund rate, is a key tool for Federal Reserve to manage the supply of the money in the economy. As mentioned earlier, the Fed fund rate is set by the FOMC. Fed fund rate indicates the price in which United States banks are lending money to each other. Currently the Fed fund rate is set in the range between an upper and lower a limit introduced in December 2008. Before introducing the range within target rate, Fed Fund rate was a fixed level of rate. The rate indicates the costs of overnight interbank lending rate on reserves deposited with the Federal Reserve i.e., the borrowing cost for money between banks. The historical federal fund target rates are shown in figure 3. As shown in the figure 3, the Federal fund target rate was decreasing rapidly during year 2008. This led to the beginning of the era of zero-rate policy which lasted until 2015, when Federal Fund target rates were increasing once again. After several rate cuts in the end of the decade, Covid-pandemic forced the central bank to drop rates dramatically to support economy during the outbreak. Afterward, the massive quantitative easing program, elevated level of inflation forced central bank to tighten its monetary policy and hence the Federal Fund target rate has been increasing rapidly during year 2022. Figure 3: History of Federal Fund Target Rate. (Bloomberg, 2022) Federal Fund Futures contract Federal Fund futures are a direct reflection of market expectations of the future course of Federal Reserve’s monetary Policy. They are financial derivatives contracts traded on the Chicago Mercantile Exchange (CME) and their market prices are used to calculate the odds for a rate movement. These probabilities are provided by CME’s Fed Watch Tool and as mentioned, they reflect the likelihood of a specific outcome based on market expectations. The Federal Fund futures are used in existing literature as they provide and are an efficient measure of expectations regarding the FOMC target rate decisions. (See e.g., Bernanke & Kuttner, 2005; Ellingsen & Södeström Ulf, 2001; Krueger & Kuttner, 1996; Vähämaa & Äijö, 2011). As suggested by Krueger & Kuttner (1996), I will use Federal Funds futures contracts to identify monetary policy surprises in this study. Historical prices of current month Federal Fund futures contracts are shown in figure 4. Figure 4 shows similar causalities with the figure 3 with negative causality. When the Federal fund target rates have been cut in 2008, the price of current month futures has been increasing. With similar logic, the price of the current month Federal fund futures decreased after central bank ended the era of zero rate policy in 2015. The latest struggle with high inflation levels and rake hikes associated with it, led to significant decrease in current month futures in 2022. Figure 4: Historical Fed Fun Futures contract prices. (Bloomberg, 2022) LITERATURE REVIEW In this chapter I focus on previous research on central bank decision making and stock market volatility. The previous research is essential since it gives a wider perspective on the topic and the earlier findings of existing literature. The effects of central bank policy decisions on stock market volatility have been studied widely. In the existing literature, exists findings that indicates that the FOMC meeting announcement impacts significantly on implied volatility of the stock market measured by VIX. Depending on the approach used in the studies, findings indicate that the volatility around FOMC meeting can be increasing or decreasing. Main explanatory reason behind the difference in findings is related on the method used: by using intraday data, it is possible to detect increase in volatility during the meeting while daily based data implies that volatility is decreasing in the aftermath of FOMC meeting and post meeting announcement. Most studies have been conducted before the outbreak of the latest financial crises meaning that prior literature is not including one or two of the most recent crises that has impacted the whole role of central banks. The second observation regarding existing literature is that the sample periods of most studies include time timespan that during which FOMC transparency has been lesser compared to the current situation. This means that existing literature has been written under the state in which market needed to guess more what was discussed in FOMC meetings. Though, the most recent studies conducted by Monetary policy and market responses The main question of existing literature related to central bank’s policy decisions and policy surprises is: Does the policy decisions, especially policy surprises impact on stock markets. Bernanke & Blinder (1992) were the first ones to prove that the Federal fund rate is an extremely informative indicator of monetary policy actions (Bernanke & Blinder, 1992). Furthermore, based on the findings of Bernanke & Blinder (1992), Krueger & Kuttner (1996) extended to see whether Fed Fund futures market can predict the changes in Federal Fund rates. Their study suggests that Federal Fund rate-based forecast of future changes in monetary policy are statistically significant. They also mention that due to the brief history of Federal Fund futures market, back in 1992, the efficiency of Federal Funds market was yet an open question. Their findings also suggest that Federal Fund futures rate is a simple but effective way to identify monetary policy surprises. (Krueger & Kuttner, 1996) The impact of an unexpected change in Federal funds rate target has been studied previously by Bernanke & Kuttner (2005). In their study, they found out that a hypothetical unexpected 25 basis point rate cut is associated with roughly a 1% increase in broad stock indexes. They also claim that the impact of monetary policy changes tends to differ across industry-based portfolios meaning that certain industries are more volatile to changes in monetary policy while other are less volatile. Industrywide responses to changes in monetary policy seems to be consistent with the predictions of standard Capital Asset Pricing Model, CAPM. While Bernanke & Kuttner state that stock values should be independent of monetary policy in the exceedingly long run, they also state that in medium term monetary policy may well influence stock values through changes in volatility. (Bernanke & Kuttner, 2005). Similar findings were found by Chen & Clements (2007) when they expanded the study to from the S&P 100 index to the S&P 500 index. Their study suggests that United States monetary policy announcements effect on the VIX index in a systematic manner prior and after the announcement. Both studies document that implied volatility systematically decreases on the FOMC meeting days, though both studies do exclude the impact of unscheduled monetary policy decisions and therefore ignore the surprise component. Existing literature includes studies where the relationship between unexpected Federal fund target rate cuts and stock market returns became positive during the financial crisis in 2007-2008. This means that while the Federal fund target rate was cut unexpectedly, stock prices also declined. During crisis, lowering rate was viewed as a signal of worsening state of the economic outlook in United States. (Kontonikas et al., 2013). This means that the unexpected rate cut of easing monetary policy would be inefficient to stabilize stock market and therefore the “Fed put” would become ineffective at the time when it is most needed. On the other hand, Kurov & Gu (2016) suggest that the bias detected in earlier literature (see e.g., Kontonikas et al., 2013, Florackis et al., 2014) exists because of the methodology used in the studies. Using intraday and daily time series regressions instead of event study methodology with daily data, Kurov and Gu were able prove that during the crisis stock market reacted positively to unexpected cuts in the Federal fund target rate. This is in line with what investors and policymakers believe: during a period of crisis, easing monetary policy has a positive impact on equity prices. Kurov and Gu (2016b) used a larger data sample than most of the previous studies: their sample contains meetings from January 1994 until October 2015. They also acknowledged that after 2008 there has been an exceptional era in the history of Federal reserve what comes to QE programs and forward guidance. Therefore, Kurov and Gu (2016b) divided their sample period into two subperiods. (Kurov & Gu, 2016b). Kurov & Stan (2018) extend the previous study by examining the monetary policy uncertainty influence on various qualities of equities. Their findings indicate that higher political uncertainty weakens the stock and crude oil markets while strengthens the Treasury and foreign exchange markets. Similar to Kurov & Gu (2016b), Kurov & Stan (2018) use Eurodollar futures to measure the changes in expected monetary policy. (Kurov & Stan, 2018). Monetary policy surprises and implied volatility Existing literature has proven the connection between monetary policy changes and stock markets. The important question in existing literature is that how the policy surprises impact on stock market implied volatility does i.e., does the monetary policy surprise increase or decrease the VIX. Previously these effects of this connection have been studied by Chen & Clements, (2007a); Farka, (2009); Florackis et al., (2014); Gospodinov & Jamali, (2012); Krieger et al., (2012); Kurov & Stan, (2018); Nikkinen & Sahlström, (2004); Vähämaa & Äijö, (2011). Stock market uncertainty, the degree to which information is unknown, tend decrease regardless of the content of the meeting i.e., what kind of decision was made (Krieger et al., 2012). Nikkinen & Sahlström (2004) suggest that S&P 100 implied volatility increases prior to, and decreases after a scheduled macroeconomic announcement, such as a FOMC board meeting. Their results imply that the uncertainty that is related to the content of the FOMC post meeting’s statement directly affects stock and option valuation. Their findings support the fact that the uncertainty related to FOMC meetings is a key factor affecting the level of uncertainty that market is facing and hence key factor for the VIX levels. In their study they use daily U.S. stock market data in form of the S&P 100 index as well as daily quotes of the volatility index VIX. Due the high significancy of conditional heteroscedasticity in regression error terms, Nikkinen and Sahlström (2004) apply GARCH (1,1) in the model structure in their study. (Nikkinen & Sahlström, 2004). Slightly comparable results shown by Chen & Clements, (2007a) where they examined the returns of the VIX index around FOMC meetings. In their study instead of using the S&P 100 index like Nikkinen & Sahlström (2004), Chen and Clements (2007) apply similar GARCH (1,1) model in their study, but they widen their study to include the VIX index from 5 days prior the meeting until 5 days after the meeting. Chen and Clements (2007) cannot find significant increase in the VIX prior the meeting which is inconsistent with the findings of Nikkinen & Sahlström (2004), but they do find s significant decrease in the VIX after the FOMC meeting which is consistent with earlier findings. They are still able to find systematic link in behavior between the VIX and monetary policy announcements prior and after the meetings. (Chen & Clements, 2007b). Previous studies have only accounted monetary policy impacts on implied volatility without accounting the surprise element in monetary policy. Farka (2009) extends the research by examining the impact of monetary policy surprises on the VIX. To identify the surprises element in monetary policy in monetary policy decision, Farka (2009) follows Bernanke & Kuttner, 2005; Krueger & Kuttner, 1996 by using the change Federal Fund future prices as measure. Farka (2009) also studies the impact of policy surprises during time of easing and tightening monetary policy. The GARCH model by Farka (2009) identified greater response in stock market returns compared to earlier studies. Her finding suggests that the 1% surprise increase in policy rates, causes a decline of 5,6% in stock returns. Her intraday GARCH model also suggests that the volatility decreases to an abnormally low level before the announcement time, rises noticeably during the announcement and starts to decrease slightly after the announcement – yet staying on elevated level. A substantial decrease in the volatility is identified the on the day after the announcement. Farka’s (2009) findings also suggests that the direction of monetary policy surprise (rate hike or cut) has an impact in perspective of reaction of stock markets. (Farka, 2009). In their study Vähämaa & Äijö (2011) extend the earlier studies by separating policy actions into target surprises and path surprises. They do also find out that implied volatility is affected by FOMC meetings, and their findings are consistent with other studies: implied volatility tends to decrease after FOMC meetings. Their findings also suggest that a positive target has a positive correlation with the VIX index (i.e., larger increase than expect or a smaller decrease than expected). Their findings further indicate that both scheduled and unscheduled meetings have an impact on the implied volatility. Their key findings indicate that stock market uncertainty tends to decrease after FOMC meetings. Furthermore, by extending their study to include the effects on monetary policy cycle, they findings indicate that the stock market reaction is more significant during periods of expansive monetary policy. (Vähämaa & Äijö, 2011). A similar study was conducted by Gospodinov & Jamali (2012), where they examined the effect of monetary policy to realized volatility and implied volatility. Their sample period is similar with the study conducted by Vähämaa and Äijö (2011), extending from January 1994 to December 2007. They examine the element of surprise from Federal fund futures contracts with three different approaches that include the current month surprise proposed by Bernanke & Kuttner (2005) and used also by Vähämaa & Äijö (2011). Gospodinov and Jamali (2012) findings are consistent with other studies and propose that surprises in monetary policy effects on implied volatility. They also point out that the increased transparency of FOMC is yet not on desired level if Federal Reserve is trying to avoid policy surprises. An enchanted level of communication is needed to reduce the element of surprise in the future. (Gospodinov & Jamali, 2012). A more recent study by Gospodinov & Jamali (2015) supports their earlier findings as the result of improved analysis, they can find out the significant correlation with Federal Reserve policy actions and stock market volatility. These findings implies that Fed can influence on stock market volatility through forward guidance or better communication if lower volatility is the desired outcome. (Gospodinov & Jamali, 2015). This thesis extends the existing literature by examining the impact of monetary policy decision on implied volatility through different sample time during which the amount of forward guidance has been increasing throughout whole period. This thesis also provides insight whether the significancy of Federal Reserve policy making and policy surprises has changed over time where the role of a central bank, measured from size of the balance sheet, has increased. This thesis uses a sample period that includes the events of the financial crisis and Covid-pandemic that is relatively fresh information. DATA AND METHOLOGY The data used in empirical analysis of this study consists of daily quotes of the implied volatility index, the VIX, the monetary policy decisions of the Federal Reserve and the daily quotes of current month federal fund futures prices. The quotes of the VIX and federal fund futures rates are collected from the Bloomberg database while the monetary policy decisions are collected from the Federal Reserve database. The sample period of this study span from 2.1.2008 to 21.9.2022. During the sample period, 125 FOMC meetings were held, and the Federal funds target rate was changed 26 times. 117 of the meetings were scheduled meetings while 8 were unscheduled. The VIX implied volatility index is used to measure stock market uncertainty. The impact of FOMC meeting on implied volatility is calculated from the change of the VIX index. Federal Fund future prices are used to measure the surprise element of the FOMC decision. These approaches are used, for example, by Vähämaa & Äijö (2011). The sample is divided into smaller portions based on characteristics of the FOMC meeting. These characteristics are whether the decisions had a surprise in Federal fund target rate or not, whether the type of the meeting was scheduled or unscheduled, whether there was change in Federal fund target rate or not and whether the FOMC meeting took place before introducing the press conference 27th of April 2011 or not. Full sample contains 3707 quotes of the VIX and current month Fed Fund futures prices. The full description of statistics are presented in chapter 4.2. Methodology I have chosen a quantitative research method to this thesis. Before getting to regressions, I need to measure the possible surprise element of FOMC policy decision making as well as the change in implied volatility. To extract monetary policy surprises from Federal Fund futures contracts, I use follow the approaches proposed by Bernanke & Kuttner (2005) and by Vähämaa & Äijö (2011), Kurov (2010) and Krieger et al., (2012) in their studies respectfully. A specific change in the Federal fund target rate is possible to detect from the change of federal fund futures contract’s price change before and after the policy decision on specific day on a specific month. The surprise element in Federal fund target rate is therefore the change in the current-month federal future contract that occurs on the date of the meeting compared with day before the meeting. As the Federal fund futures settlement price is based on the monthly average of the Federal Fund rate, the change needs to be scaled accordingly to cover the dates that are taking place after the meeting. The surprise in implied target rate of current-month contract is measured as follows: ) (2) where: = unexpected component of the FOMC announcement = first difference operator = current month futures rate at the end of the FOMC day d = current moth futures rate at the end of the day prior FOMC day d D = number of the days in the month d = number of the day on FOMC meeting date I will follow the method of Vähämaa & Äijö, (2011) to examine the impact of Federal Reserve’s monetary policy decisions on stock market volatility. I will regress the daily changes in the VIX index on alternative variables as follows: (3) where: = log of implied volatility (the VIX index) at time t = first difference operator = different variables or sets of variables in monetary policy = error term The dependent variable, denoted as , can also be written out like (Nikkinen & Sahlström, 2004) in their study. Similar denotation was used by Chen & Clements (2007) and it is as follows: (4) These seven monetary policy variables altering in regression models are discussed more in subchapter 4.3. The seven selected explanatory variables are: I. A meeting dummy that receives value of 1 on FOMC meeting days II. A surprise variable that receives the value of surprise in basis points on the FOMC meeting days. III. A Scheduled meeting surprise variable that receives the value of surprise on the scheduled FOMC meeting days IV. An unscheduled meeting surprise variable that receives value of surprise on the unscheduled FOMC meeting days V. A before press conference dummy variable that receives the value of 1 on FOMC meeting days if the meeting has held on 27.4.2011 or after VI. An after-press conference dummy variable that receives value of 1 on FOMC meeting days if meetings was held before 27.4.2011. VII. A Fed Fund target rate change variable that receives the value of the hike/cut on FOMC meeting days To test the impact of specific variables to implied volatility, I’ll introduce the series of regression models that. In Model (1) the daily changes of the VIX index are regressed on FOMC meeting dummy (I). In Model (2) the implied volatility changes are regressed on monetary policy surprise variable (II) and in Model (3) the FOMC meeting dummy (I) is added to Model (3). In Model (4) the changes in VIX are regressed on scheduled surprise meeting variable (III) and in Model (5) the FOMC meeting dummy (I) is added. Respectively, Models (6 & 7) are similar to Models (4 & 5) with the difference that unscheduled meeting variable (IV) is now used instead of scheduled meeting variable. In Model (8) the change in implied volatility is regressed on Fed Fund target rate change variable (VII) and in Model (9) the FOMC meeting dummy (I) is added once again. The volatility is regressed on the before press conference variable (V) in Model (10) and in Model (11) the implied volatility is regressed on before press conference variable (V) and FOMC meeting dummy (I). Finally, the change in implied volatility is regressed on an after-press conference variable (VI) (Model 12) and finally on after-press conference variable (VI) and FOMC meeting dummy (I) (Model 13). This approach corresponds to the approach of Vähämaa & Äijö, (2011). Since the data used in these regressions is in the form of time series, deploying an ordinary least square (OLS) regression has certain requirements which needs to be met. Gauss-Markov assumptions are stricter for a time series analysis compared to cross-sectional data. The residual term in each regression model must be homoscedastic and there cannot be autocorrelation in the residual term of the regression. The residual term must be endogenous meaning that it does not change with the independent variables. Multicollinearity does not exist as the regressions include only one independent variable and a dummy variable. Multicollinearity could be detected from variance inflation factors. To test for possible serial correlation, I use the Durbin-Watson (DW) test. This test is used to detect possible serial correlation in the residuals of regression analysis. If the test result is within the range 1.5 and 2.5, it indicates that no significant autocorrelation is present in the residuals. If the DW-test indicates that there’s significant autocorrelation in the regression model, AR (1) term is added into the regression structure. Regressions are controlled against heteroscedasticity by using White’s heteroscedasticity test. Similar methodology is used in several times in existing literature (Basistha & Kurov, 2008; Nikkinen & Sahlström, 2004; Vähämaa & Äijö, 2011). Descriptive statistics Descriptive statistics of the sample are presented in table 1. As we can see from the table, the sample includes 125 FOMC meetings. Out of those 125 meeting, 117 of them were scheduled meetings while 8 were unscheduled meetings. Not surprisingly most of these unscheduled meetings took place either in the aftermath of the financial crisis or during Covid-19 pandemic. It is possible to see that the monetary policy decisions of the Federal Reserve have been well anticipated by investors. Positive surprise indicates that the rate hike has been larger than expected or rate cut has been smaller than expected and negative surprise indicates that rate hike has been smaller than expected or rate cut has been larger than expected. The median (mean) surprise of the detected surprises is only 2.28 (0.54) basis points. Surprisingly, the direction of surprise is positive in this sample when in the existing literature the surprises has been negative (Nikkinen & Sahlström, 2004; Vähämaa & Äijö, 2011). As we can see from the table 1, the magnitude of surprises, mean 2.28 (0.54) basis points, has been driven, less surprisingly, by the unscheduled meeting surprises with mean 14.70 (0.95) basis points. The direction of the unscheduled surprises can be explained by the big surprises during 2008. The same statistics were also used by Vähämaa & Äijö (2011) in their study. As an addition to earlier studies, this study includes the actual changes in Federal Fund Target rates that included an element of surprise. These 26 detected changes were associated with a positive surprise with mean of 7.65 (2.23) basis points. Additionally, the meetings are divided into two groups based on whether they took place before or after introduction of the press conference on FOMC decision day. From the table 1 we can see that the 29 meetings took place before the press conference have higher element of surprise with mean of 4.73 (0.50) basis points. The meetings that had a press conference after the announcement have significantly smaller mean 1,50 basis points while the median is the same with before press conference meetings. Table 1: Descriptive statistics Descriptive Statistics   No. Of Obs. Mean Median St.Dev Min Max FOMC meeting 125 Scheduled FOMC meeting 117 Unscheduled FOMC meeting 8 Surprise 83 2.28 0.54 12.02 -30.00 70.61 Scheduled meeting surprise 75 0.95 0.52 8.07 -30.00 38.75 Unscheduled meeting surprise 8 14.70 0.95 28.45 -12.18 70.61 Fed Fund change and surprise 26 7.65 2.23 21.75 -30.00 70.61 Meeting before press conference 29 4.73 0.50 23.60 -30.00 70.61 Meeting after press conference 96 1.50 0.54 4.24 -4.77 15.87 The VIX 3707 20.40 16.05 9.56 9.14 82.69 Change in the VIX (equation 4) 3706 0.0 % -0.3 % 3.4 % -15.2 % 334 % Table 1 shows that implied volatility of the S&P 500 index has, on average, been about 20.40%. The mean is similar with earlier studies of Vähämaa & Äijö (2011) with sample mean of 19.4% and Nikkinen & Sahlström (2004) with sample mean of 23.71%. The VIX maximum value of 82.69% is twice as large as the maximum values of earlier studies with sample maxima of 45.7% and 48.56%, respectfully. This indicates that the peaks of implied volatility have been extremely large in this sample compared to earlier studies. Not surprisingly these are two peaks in the VIX index with exceedingly high values: first one in the aftermath of Financial Crisis in late 2008 and second one during the outbreak of Covid-pandemic in March 2020. The change of the volatility implied index VIX has, on average, been 0% and median is only -0.30% and it has been varying between -15.2% and 33.4% over the sample period. To test my hypotheses, I use models (1-7) to identify whether the monetary policy decisions impact on stock market implied volatility. Additionally, these models are a re-creation of the models used by Vähämaa and Äijö (2011) in their research and hence gives us a possibility to compare the findings over time. If the result of these models indicates significant increase or decrease in stock market volatility, null hypotheses is rejected. Explanatory variables In this subchapter the explanatory variables are taken a closer look. Choosing each of variable is explained and respectfully the possible impact of each variable is discussed in this subchapter. First variable used in this study is a FOMC meeting dummy (I). This dummy is used to identify the Federal Open Market Committee announcement days from the sample by giving it a value of 1 on the FOMC announcement days and otherwise a value of 0. Using a FOMC meeting dummy is common way to identify the surprises on correct days and it has commonly been used in prior literature as well. (Bernanke & Kuttner, 2005; Chen & Clements, 2007b; Nikkinen & Sahlström, 2004; Vähämaa & Äijö, 2011). Without this dummy variable conducting a time series regression would be much harder to construct. It is possible to construct an event study by using selected FOMC announcement dates if using a meeting dummy variable is not suitable for a reason or another. Yet using the time series analysis with a meeting dummy should lead to more exact regression results. As the FOMC announcement is associated with decreasing impact on implied volatility, as mentioned earlier, it is likely that FOMC meeting dummy variable will be associated with negative coefficient with the VIX index too. This decreasing coefficient between a FOMC meeting and implied volatility was found by (Chen & Clements, 2007b; Nikkinen & Sahlström, 2004; Vähämaa & Äijö, 2011) for example. Therefore, including this dummy variable is crucial for conducting the time series analysis in this study. As the FOMC meeting dummy variable (I) is only indicating a meeting that took place during sample period, it does not account whether there was a monetary policy surprise or in the announcement. Thus, the second explanatory variable used in this study is a surprise variable (II) that identifies the surprise based on the change in Federal fund futures prices. The extraction of surprises is based on equation 2 earlier in this study. Different variation of extraction has been used in prior literature depending on the surprise type (target or path) surprise is wanted. For example, Kurov, (2010) and Vähämaa & Äijö (2011) are using different month Federal fund futures contracts to measure the element of surprise on the different timeframe after the announcement. Vähämaa & Äijö (2011) are using similar surprise variable to identify the meetings those decisions include the element of surprise as well as the size of surprise (in basis points). This variable is receiving values of surprise only on the FOMC announcement days that include the surprise, otherwise the value of variable is zero. Surprise variable is widely used in prior literature to identify the FOMC meetings that included a surprise in the monetary policy decision (see e.g. (Gospodinov & Jamali, (2012) and Vähämaa & Äijö, (2011). Baseline assumption is that surprise variable would be associated with increasing impact on stock market uncertainty measured by the VIX index. It is highly likely that the impact of monetary policy surprises differs between the type of FOMC meeting. Scheduled meetings are known for a long time before taking place and therefore the possibility of change in monetary policy can be anticipated by market participants well ahead of the meeting. On the other hand, monetary policy decisions, especially surprises, have more significant impact on stock market uncertainty if the meeting is unscheduled. In case of unscheduled meetings, market participants have less time to predict the possible monetary policy decisions. Although it is important to acknowledge that the decision made in unscheduled meeting is based on some recent event, most likely some surprising event or global surprise such as breakout of Covid-pandemic for example, and therefore the announcement of FOMC can be anticipated at least on some level. Yet, it is more likely that monetary policy surprises on unscheduled meetings have greater impact on implied volatility. Finding this coefficient from regressions would support findings of Vähämaa & Äijö (2011) who characterized FOMC meetings into scheduled and unscheduled meetings. Therefore, I apply similar variables in my regressions as well. Scheduled meeting surprise variable (III) that receives the value of surprise on the scheduled FOMC announcement days is applied as well as Unscheduled meeting surprise variable (IV). Both variables are explanatory variables in my regression models and used to test whether the type of the meeting is driving the coefficient of surprise element. Furthermore, as discussed earlier in this study, the amount of information in the markets regarding the monetary policy decisions is crucial for market participants to anticipate the possible changes in monetary policy. The forward guidance of monetary policy decisions by Federal Reserve was introduced for the first time after the FOMC meeting in 27th of April in 2011. It is possible that the impact of FOMC announcement on the S&P 500 index uncertainty differs from before and after the meetings. To identify this effect, I introduce first a before press conference variable (V) that identifies the surprises that took place before 27th of April 2011 regardless of type of the meeting. Secondly, I introduce the after-press conference variable (VI) that identifies the surprises that took place on or after 27th of April 2011. Baseline assumption is that the forward guidance of Federal Reserve after FOMC announcements strengthens the ability of market participants to predict the monetary policy decisions and lower the impact of surprises. Hence, the monetary policy decisions after 27th of April 2011 should have smaller impact on the stock market uncertainty. Respectively, Coefficient of variable (V) should be greater than variable (VI). Similar variables have not been used in the prior literature as the samples used in prior literature are usually mainly from periods before the introduction of the press conference. The last variable used in regression of this study is the Fed Fund target rate change variable (VII). This variable identifies the FOMC announcement days on which the Federal Fund target rate was changed and measures the change in the Fed fund target rates as well. It is unarguable that surprises of monetary policy decisions are crucial for market participants, but surprises are identified regardless of whether the monetary policy rates increased or decreased. This explanatory variable (VII) identifies the possible impact of actual changes and implied stock market volatility. Notably to mention, this variable does not account whether the change was anticipated by the market participants or not and therefore it is possible that this variable does not provide significant results. Yet, it may still provide additional information. Assumably, the impact of actual rate change should have decreased impact on implied volatility. The assumption is based on the baseline idea that FOMC announcement includes rate change only on occasions where market participants are expecting a rate change. EMPIRICAL RESULTS The regression analyses are conducted with ordinary least squares method as described in subchapter 4.1. The purpose of these regression analyses is to try and find the variables or set of variables that might explain the behavior of implied volatility index VIX on the FOMC meeting days. Based on earlier studies on the topic, most notably Vähämaa & Äijö (2011) and Nikkinen & Sahlström (2004), I apply certain characteristics of the FOMC meetings to the regression models as control variables. These models were explained in subchapter 4.1 and the variables in subchapter 4.3 respectfully. FOMC meeting dummy (I) is used to identify the FOMC meeting days on which the after-meeting announcements were made. Surprises with scheduled and unscheduled meetings These results are used to test my hypotheses. The Fed’s policy making impact on stock market implied volatility is measured with seven models similar with Vähämaa & Äijä (2011) in their research. In Model 1 the daily changes of implied volatility index VIX are regressed on FOMC meeting dummy. This model also provides a re-examination of the findings documented in existing literature (Chen & Clements, 2007b; Nikkinen & Sahlström, 2004; Vähämaa & Äijö, 2011). The results of regression Models (1-7) are shown in the table 2. Consistent with earlier findings, results indicate that the VIX index decreases on the FOMC meeting days. The coefficient is statistically significant and implies that the uncertainty related to monetary policy decision making is resolved by the market participants. Despite the significant coefficient detected by the Model 1, it is notable to mention that this model ignores the actual monetary policy decision made on FOMC meeting. Therefore, in Models (2-3) are extended to take account the surprise component of the FOMC decision. Table 2. Regression results (models 1–7) Results of regression Models (2-3) show that target surprises identified from Federal Fund futures are positively associated with stock market uncertainty, although the result from Model (2) is not statistically significant and from Model (3) statistically significant on level 0,1. However, the results are similar with existing literature indicating that positive surprises (higher larger than expected rate hike or smaller than expected rate cut) would increase the uncertainty of stock market and negative surprises (smaller rate hike than expected or larger rate cut than expected) lead to decrease in stock market volatility. In Model (3) the FOMC meeting dummy receives negative coefficient that is statistically significant indicating that FOMC meetings usually leads to decrease of implied volatility. These findings are suggesting similar coefficient between surprises and volatility, although less statistically significant, than Vähämaa & Äijö (2011). This indicates that volatility reacts similar on way on FOMC meetings and surprises during period varying from 1996 to 2007 and during period from 2008 and 2022. These models measure the surprise impact on implied volatility, but do not take account the type of the meeting. Earlier studies imply that surprises detected on scheduled FOMC meetings have different impact on stock market implied volatility compared to surprises detected on unscheduled FOMC meetings. Thus, in regression Models (4-5) daily changes of implied volatility index VIX are regressed on Scheduled FOMC meeting surprise variable and in models (6-7) on unscheduled FOMC meeting surprise variable. As table 2 shows, both scheduled and unscheduled surprises are impacting associated with positive impact on Implied volatility. It is surprising that none of the findings appears to be significant on any level, although the FOMC meeting dummy is still statistically significant and suggesting that meetings tend to lead on decrease of implied volatility. It is possible that the sample includes opposite surprises that counter the effect of each other leading to insignificancy in the results. Especially the significancy levels in models included unscheduled FOMC meetings are very insignificant. It can still be detected that surprises tend to have increasing impact on stock market implied volatility that is in line with earlier studies. The results from regression Models (1-7) imply that the FOMC meeting impact on implied volatility index VIX is significant and leads to decrease in the index. As the findings are significant, we can reject our null hypothesis H0 that FOMC meetings has no impact on the stock market implied volatility. The hypothesis H2 is rejected as well, even when using a sub-sample of FOMC meetings with an element of surprise as the results are not statistically significant. Therefore, the hypothesis H1 stating that the Fed’s monetary policy announcement decreases the stock market implied volatility, is accepted. Additional regressions Models (1-7) were used to re-examine the findings of previous study conducted by Vähämaa & Äijö (2011). However, their study does not account whether the surprise included actual change in Federal Fund target rate. Thus, in Models (8-9) daily changes of the VIX are regressed on the actual changes in Federal Fund target rates on the meetings days that included the element of surprise. The results are shown in table 3 and although both models offer statistically insignificant positive coefficient on implied volatility. Despite of the insignificancy, the results show that the actual change in Federal Fund target rate is not able to explain the magnitude of coefficient between implied volatility index and the element of surprise. This factor could be research more closely for further to gain more information. Introduction of FOMC press conference should increase the amount of information related on future decision making of FOMC. Increased amount of information in form of forward guidance should remove the effect of FOMC decision making on stock market implied volatility, at least partially. Therefore, in Models (10-13) the daily changes in the VIX index are regressed on before press conference dummy which identify the meetings which took place before the introduction of the press conference (Models 10 and 11) and on after press conference dummy variable which identify the meetings which took place after introducing the press conference (Models 12 and 13). The results of these regressions are shown also in table 3. The results of Models (10-11) indicate that meetings that took place before the 27th of April 2011 tend to lead to the decrease in implied volatility. Notably, the findings are not statistically significant on any level. However, the decreasing effect identified by Model (12) is statistically significant and shows that the FOMC meetings held after introduction of the press conference leads to decrease in volatility. Model (13) is not able to identify similar coefficient between meetings and volatility, though the findings are not statistically significant. Surprisingly, Model (12) has negative coefficient between surprise and implied volatility while Model (13) indicates positive correlation. As Model (13) findings are not statistically significant, they are disregarded. Further investigation into the impact of press conference is needed and better models are needed to examine it. Throughout the additional models, the meeting dummy variable is yet associated with a negative coefficient on implied volatility suggesting that FOMC meetings tend to lead into a decrease of implied volatility index VIX. The coefficients of the meeting dummy variable are significant on every model (9, 11 and 13). The results of every regression model are presented in the table 4. Table 3. Regression results (models 8–13) Table 4. Full results of regression models CONCLUSIONS This study focuses on the impact of monetary policy decisions on stock market uncertainty, particularly the effect of Federal Reserve policy decisions on the implied volatility of the S&P 500 index. In this study, Federal Fund futures contracts are used to detect the surprises in monetary policy decision making. Furthermore, these detected surprises are used to assess the effect of monetary policy surprises on the implied volatility index VIX. In this study the surprises on Federal Open Market Committee meetings are categorized into scheduled and unscheduled surprises. Additionally, this study extends the prior literature by attempting to examine whether the effect of monetary policy decisions on implied volatility depends on the actual change in Federal Fund target rates. Furthermore, this study examines whether the impact of monetary policy surprises on implied volatility depend on the introduction the press conference in 27th of April 2011 and thus, surprises are categorized into before press conference and after the press conference surprises. The empirical findings reported in this study indicate that monetary policy decisions affect on the stock market uncertainty measured by implied volatility index VIX. The findings show statistically significant decrease in the VIX index after the FOMC meeting policy announcements. These findings are consistent with the prior literature as well. These findings suggest that the uncertainty in stock market decreases after the information related to monetary policy is provided to market participants. On the contrary, my findings are inconsistent with the prior literature as I am unable to find significant increase in the VIX index after the monetary policy announcement with a surprise element. Breaking surprises into scheduled and unscheduled surprises does not provide further evidence on behavior of implied volatility. Neither of the meeting types have a statistically significant results supporting the findings of prior literature that the volatility tends to increase after a surprise in monetary policy announcement. Additionally, I studied the impact of Fed’s forward guidance on the monetary policy by introducing a press conference variable. Introduction of the press conference dummy variable did not impact on the coefficients between the daily changes of implied volatility and monetary policy decisions in statistically significant way. Meetings that took place before introduction of the press conference led to a decrease in implied volatility while meetings after introduction of press conference appear to have a positive coefficient with implied volatility. As the findings are not statistically significant, it is likely that the statistically significant coefficient can be negative if another better model is applied on regressions. Thus, the impact of the press conference is not significant, and the additional information provided by forward guidance does not apply on the monetary decision surprises. Further research is needed and the steadily extending sample offers us increasing amount of FOMC meetings to widen our after-press conference samples. References Banerjee, P. S., Doran, J. S., & Peterson, D. R. (2007). 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In Journal of Portfolio Management; Spring (Vol. 35). https://doi.org/10.3905/JPM.2009.35.3.098   Historical development of S&P 500 Index Last Price 44827 44826 44825 44824 44823 44820 44819 44818 44817 44816 44813 44812 44811 44810 44806 44805 44804 44803 44802 44799 44798 44797 44796 44795 44792 44791 44790 44789 44788 44785 44784 44783 44782 447 81 44778 44777 44776 44775 44774 44771 44770 44769 44768 44767 44764 44763 44762 44761 44760 44757 44756 44755 44754 44753 44750 44749 44748 44747 44743 44742 44741 44740 44739 44736 44735 44734 44733 44729 44728 44727 44726 44725 44722 44721 44720 44719 44718 44715 44714 44713 44712 44708 44707 44706 44705 44704 44701 44700 44699 44698 44697 44694 44693 44692 44691 44690 44687 44686 44685 44684 44683 44680 44679 44678 44677 44676 44673 44672 44671 44670 44669 44665 44664 44663 44662 44659 44658 44657 44656 44655 44652 44651 44650 44649 44648 44645 44644 44643 44642 44641 44638 44637 44636 44635 44634 44631 44630 44629 44628 44627 44624 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