ECB and U.S Monetary Policy Spillovers to Sweden: Transmissions Channels and the Effects of Central Bank Information Bias/ Unconventional Monetary Policy

Department of Economics Spring 2021 Master's Thesis Work, 30.0 HP

Author: Ludvig Mannerson Supervisor: Spyridon Sichlimiris

Abstract

This paper revisits the transmission of ECB and U.S Fed monetary policy to Sweden by using recently developed measures of ECB and Fed monetary policy shocks, corrected for the central bank information element. To analyze the spillovers of foreign monetary policy to Sweden I rely on the Local Projections methodology. First, I show that standard measures of foreign monetary policy shocks that are known to contain a central bank information element can cause counterintuitive spillover effects to Swedish macroeconomic and financial variables. Second, my results suggest that monetary policy spillovers from the ECB and U.S Fed both have substantial impact on the Swedish economy, particularly through the aggregate demand channel. While an ECB monetary contraction has a strong negative impact on Swedish macroeconomic variables, the tightening of the U.S Fed monetary policy mostly exhibits an expansionary effect. Lastly, but crucially, my thesis demonstrates that US monetary policy has more substantial spillovers during the conventional monetary policy period, as the aggregate demand channel is muted during periods of unconventional monetary policy.

Acknowledgements I would like to thank Spyridon Sichlimiris for his valuable time, comments, suggestions, and proof-reading. Spyridon has been a fantastic supervisor and this thesis would not have been possible without our numerous discussions regarding everything from topic selection to methodology application. His quick email replies and availability for our “short notice” meetings has been much appreciated. Thank you!

Table of contents

1: Introduction ...... 4 2: Theory ...... 7 2.1 The Exchange Rate Channel ...... 7 2.2 The Aggregate Demand Channel ...... 8 2.3 The Financial Channel ...... 9 3: Previous Empirical Research ...... 11 3.1 Monetary Policy Spillovers, the Bias of Information Shocks ...... 14 3.2 Contribution of My Thesis to the Monetary Policy Spillover`s Literature ...... 16 4: Data of ECB and U.S Fed Monetary Policy ...... 16 4.1 Swedish Economic and Financial Variables ...... 18 5: Methodology to Study the Effects of Spillovers ...... 19 6: Results...... 21 6.1: Spillovers Effects from ECB Monetary Policy Shocks Vs ECB Information Shocks to Macroeconomic Variables ...... 21 6.1.2 Spillovers to Financial Variables ...... 26 6.2 Spillovers Effects from ECB Monetary Policy Shocks Vs U.S Fed Monetary Policy Shocks 29 6.2.1 Spillovers to Macroeconomic Variables ...... 29 6.2.2 Spillovers to Financial Variables ...... 33 6.3 Spillovers Effects from U.S Conventional Vs Unconventional Monetary Policy Shocks ...... 35 7.1: Sensitivity Analysis ...... 38 7.2 Robustness Test Using Different Construction Methods of US Monetary Policy Shocks .. 41 8: Conclusion ...... 44

1: Introduction

The economic and financial crises of the past two decades and the increasing reliance on unconventional monetary policy measures has contributed to rekindle the debate regarding the impact of international monetary policy spillovers. In an influential “pre-crisis” paper published by Obstfeld and Rogoff (2002), the authors argued that the cross-border effects of domestic monetary policy spillovers were likely to be negligible, even in a fully economically integrated world. Because of this, the authors further argued that any international coordination of monetary policy would be redundant, and that domestic monetary policies should only focus on domestic objectives. However, evidence from the past decades have demonstrated the possibility for foreign monetary policy shocks to transfer across borders though different transmission channels, and strongly affect economic and financial conditions in other nations (Hajek and Horvath, 2017; Saskia et al, 2018). Consequently, the effects of foreign monetary policy spillovers have become critically important to for central banks and economic researchers to consider.

Understanding how foreign monetary policy can affect the domestic economy is quickly becoming more important in a rapidly globalizing world. As individual countries have become more integrated through the ever-expanding trade of goods, services, capital, and technology, they have also become more vulnerable to foreign financial and economic conditions that propagate through these transmission channels, such as foreign monetary policy stances (Bräuning and Sheremirov, 2019, Ca' Zorzi et al., 2020, Ehrmann and Fratzscher, 2009; Ehrmann et al., 2011; Hale et al., 2016, Jarocinski and Karadi, 2018). It has also been proposed that growth and financial conditions worldwide are to some extent determined by a global financial cycle, which is largely determined by U.S monetary policy (Rey, 2013; Bekaert et al., 2013).

The large increase in worldwide economic integration in addition to the extensive policy of monetary expansion has consequently contributed to renewed interest regarding the impact of foreign monetary policy spillovers (Ca' Zorzi et al., 2020). For example, a large strain of previous research has in aggregate found that economies are subject to experience substantial economic consequences due to monetary policy spillovers, particularly from larger entities such as the European Central Bank or the U.S Federal Reserve (Hajek and Horvath, 2017; Ca' Zorzi et al., 2020; Gertler & Karadi 2015; Georgias, 2016). However, previous literature has mainly focused on monetary policy spillovers form one single central bank, and few papers have

4 attempted to make any comparisons between spillovers emanating from different central banks in a unified framework. This is of crucial importance, as monetary policy spillovers emanating from different central banks could propagate though different transmission channels and exhibit heterogeneous effects on the economies that they impact. Therefore, the purpose of this thesis is to contribute with a better understanding of the different effects and transmissions channels between monetary policy spillovers deriving from the ECB and U.S Fed on the small open economy of Sweden. By analyzing the response of a wide variety of Swedish economic and financial variables, the strength of the different monetary policy spillovers and the transmission channels through which they propagate, can better be established.

Sweden is chosen as the country of analysis because of its openness to trade, floating exchange rate, and tight integration with the European Union. Furthermore, previous research by Hajek and Horvath (2017) found that Sweden exhibited some of the most potent responses to foreign monetary shocks out of all the countries analyzed in his dataset. The timeframe selected for this analysis goes from January 1999, when the ECB assumed responsibility for Euro-area monetary policy (ECB, 2021), to May 2019. All data for this analysis has been acquired at monthly frequency, which allows for investigation of the foreign monetary policy spillover effects to the Swedish economy at a relatively high frequency. Lastly, the choice to compare the effects of ECB monetary policy spillovers to spillovers from the U.S Fed is because U.S. monetary policy is often recognized, due to its size and the role of its dominant currency, as a principal driver of global business cycles (Rey, 2013; Bekaert et al., 2013).

Regarding the estimates of foreign monetary policy, recently published research by Nakumura and Steinsson (2018) and Bu et al (2020) show that previous monetary policy estimates contain a “central bank information effect”, which contributes to estimation biases, particularly for ECB monetary policy (Jarocinski, 2020). The central bank information effect can best be described as the ability of central banks to affect market expectations and behavior, in addition to monetary policy, by disclosing privately held information regarding the future economic outlook (Ca' Zorzi et al., 2020). This fact presents a problem, as the spillover effects that emanate from foreign central bank information are likely to be different compared to the spillover effects that emanate from foreign monetary policy (Nakumura and Steinsson 2018). To account for bias associated with the central bank information effect, this paper will apply newly estimated monetary policy shock series developed by Jarocinski (2020) and Bu et al (2020), which takes the information effect from the ECB and US Fed into account. By

5 accounting for the central bank information effect, the estimates of the respective central bank’s monetary policy can be considered as truly exogenous (Jarocinski 2020).

With the application of the new monetary policy and central bank information shock series, I argue that this paper's contribution is threefold. Firstly, this paper contributes to the existing literature by comparing the effects and transmission channels of exogenous monetary policy spillovers from 2 large central banks in a unified framework. Second, in addition to the comparison between the spillover effects from the ECB and the U.S Fed, this paper contributes to the existing literature by also contrasting the spillover effects of monetary policy and central bank information form the ECB. Results suggests that omitting the central bank information effect leads to incorrect conclusions regarding the effects of ECB monetary policy spillovers. For example, an ECB monetary contraction has a negligible effect on Swedish inflation, while the central bank information effect has an expansionary effect. Therefore, omitting the central bank information effect contributes to weaker and less significant response for most of the analyzed Swedish variables.

Third, due to the stark difference between the effects of conventional and unconventional monetary policy found by previous authors (e.g., Babecká Kucharčuková et al. 2016), this paper adds to the existing literature by comparing the effects of U.S conventional and unconventional monetary policy while controlling for the information effect. The empirical results show that the effect of unconventional monetary policy spillovers strongly impacts the exchange rates, the Swedish short-term rate, and the Swedish 10- year government bond yield. However, the effects of the unconventional spillovers are negligible and insignificant for most of the remaining variables. On the contrary, conventional monetary policy exhibits a substantial impact for most of the Swedish macroeconomic variables, in line with the findings of previous literature (e.g., Hajek and Horvath, 2017).

The remainder of the paper is organized as follows: Section 2 will present the underlying theory of monetary policy spillovers and the channels through which they are transmitted. Section 3 will review previous existing literature on monetary policy and central bank information spillovers, while Section 4 describes the data. Section 5 presents the method used to estimate the monetary policy shocks on the Swedish variables and section 6 presents the results. Section 7 follows with a sensitivity analysis and section 8 concludes the paper.

2: Theory

In a globalized world where markets are closely integrated, monetary policy conducted abroad can be transmitted to other countries through spillovers. The effects of foreign monetary policy spillovers can according to economic theory and previous empirical studies, either have a positive or a negative impact on the economy of the receiving country, depending on the relative strength of the three most common transmission channel through which monetary policy spillovers propagate across countries (Ca' Zorzi et al., 2020). These channels are the exchange rate channel, the aggregate demand channel, and the financial conditions channel (Ammer et al., 2016; Degasperi et al, 2019). Each transmission channel will be reviewed in the following sections.

2.1 The Exchange Rate Channel

The first channel through which monetary policy spillovers can be transferred abroad is the exchange rates, where foreign monetary policy causes the domestic currency to appreciate/ depreciate relative to the foreign currency. The exchange rate adjustment in turn affects the relative price of foreign and domestic goods, causing the demand for imports and exports to change, ultimately affecting the trade balance and aggregate demand. The exchange rate is determined by the interest rate parity condition where all variables are denoted in nominal terms:

푒푒 1 + 푖 ∗= (1 + 푖) (1) 푒

Here, i* represents the foreign interest rate, i represents the domestic interest rate, e represents the exchange rate and 푒푒 represents the expected future exchange rate. However, countries with a floating exchange rate have their exchange rate determined by the currency market and the Central bank interest rates (Gottfries, 2013). Therefore, the absence of a specific central bank exchange rate target and currency interventions allows the interest rate parity condition to be written as follows:

1+푖 푒 = 푒푒 (2) 1+푖∗

Here, the floating exchange rate depends on the domestic and foreign interest rate quota and the expected future exchange rate. A foreign monetary contraction increases the foreign interest rate, causing domestic investors to receive better returns abroad. Under a floating exchange rate

7 regime, investors exchange their domestic currency to invest in foreign currency-denominated assets with higher returns. In line with the uncovered interest rate parity condition, the increase in foreign interest rate coupled with the increased (decreased) demand for foreign (domestic) currency, causes the domestic exchange rate to depreciate. Likewise, a foreign monetary expansion or increase in the expected future exchange rate causes the domestic exchange rate to appreciate.

2.2 The Aggregate Demand Channel

The effects from exchange rate channel can either positively or negatively impact domestic net exports and output, depending on whether the impact of the exchange rate following a foreign monetary policy adjustment is more substantial than that of the change in domestic aggregate demand (Degasperi et al, 2019). The effect which foreign monetary policy spillovers can affect domestic aggregate demand is shown by the I.S. equation:

푒푃 푌 = 퐶(Y − T, 푌푒 − 푇푒 , i − π 푒 , A) + 퐼( i − π푒 , Y 푒 , 퐾) + 퐺 + 푁푋( , 푌 ∗, 푌´) (3) 푃∗

Here, Y represents domestic aggregate demand/ output and C represents private consumption which is dependent on income (Y), expected income (Ye), Taxes (T), expected Taxes (Te), the interest rate (i), expected inflation (π 푒) and lastly, an endowment (A). Similarly, I represents domestic investments, which is additionally determined by the capital stock (K). Lastly, G represents domestic government expenditure, and NX represents net exports. In turn, net exports depend on the price level of foreign and domestic goods (P* and P), foreign and domestic production/ income (Y* and Y´) and finally, the nominal exchange rate (e). The aggregate demand function (3) can be rewritten into a modified I.S. curve by substituting the exchange rate (e) for the floating interest rate parity condition expression (2) from the previous section:

1+푖 푃 푌 = 퐶(Y − T, 푌푒 − 푇푒 , i − π 푒 , A) + 퐼( i − π푒 , Y 푒 , 퐾) + 퐺 + 푁푋( 푒푒 , 푌 ∗, 푌) (4) 1+푖∗ 푃∗

Equation 4 represents the modified I.S curve where net exports (NX) are shown to be affected by the foreign and domestic interest rates set by the foreign and domestic central bank (i*, i). Here, foreign monetary policy affects the domestic aggregate demand as the change in the foreign interest rates causes investors and consumers to alter their behavior, while the

8 corresponding exchange rate adjustments determine the quantity of exports and imports. Whether the impact of foreign monetary policy on trade is positive or negative also depends on the increase/ decrease in foreign demand following a foreign monetary policy adjustment (Degasperi et al, 2019).

The effects of foreign monetary policy on aggregate demand have important implications for the Swedish economy, as it is strongly integrated with the European Union. Following Sweden’s entrance into the E.U in 1995, its export share of GDP has risen from 37 % to 47 % by 2019 (The Global Economy), causing Swedish output to become more vulnerable to foreign demand fluctuations (Nordström, 2019).

In addition, the effect of foreign monetary policy also depends on the foreign country's relative share of imports from the domestic economy. “Countries” such as the E.U. or U.S, which are essential Swedish trading partners, could through their respective monetary policy stances significantly reduce or increase the demand for Swedish exports. This would then by extension, contribute to a significant reduction or increase in Swedish output. As global trade has increased rapidly over the last decades, following the reduction of trade barriers and the WTO's inception, so has the interdependencies of economies and the monetary spillovers that operate through aggregate demand (Ca' Zorzi et al., 2020).

2.3 The Financial Channel

The last channel through which monetary spillovers are transmitted is the financial channel. Following deregulations during the past decades, financial markets have become more integrated and international capital mobility has increased (Bodie et al., 2017). Under these conditions, domestic investors can buy foreign assets or assets denoted in foreign currency, making them exposed to foreign financial market conditions. This development has consequently contributed to the emergence of a financial channel of monetary spillovers (Agénor et al., 2018).

Regardless of issuing country, foreign assets and liabilities can be denominated in a globally dominant currency such as the USD. As the global banking system has adopted the US dollar as a dominant global currency (Degasperi et al, 2019), firms in emerging market economies have recently begun to issue large amounts of debt denominated in USD (Bruno and Shin, 2018). Because of this, the amount of outstanding bonds denominated in USD outside the dollar currency area has significantly increased over the past two decades (Ca' Zorzi et al., 2020). As

9 an increasing amount of assets and liabilities are being denoted in a dominant foreign currency, the monetary policy of that dominant currency can have substantial spillover effects worldwide. For example, a U.S monetary expansion will cause the price of USD denominated bonds to fall, raising the yield while negatively affecting investors as their bond position's value is reduced. The impact is particularly powerful when the bond is considered "safe" (e.g., Farhi and Maggiori, 2018).

Moreover, the dominant currency's monetary policy can also impact foreign assets and liabilities through exchange rate revaluation. A foreign monetary tightening depreciates the domestic exchange rate, causing the value of foreign assets and liabilities to increase in the domestic currency. The transmission of foreign monetary policy spillovers through exchange rate revaluation can be heterogeneous depending on a given country's financial situation. Countries (and investors) with a net long balance sheet of foreign currency denominated assets experience an increase in wealth when a foreign monetary policy tightening occurs, stimulating output and inflation (Meier, 2013). Similarly, an economy with a net short balance sheet of foreign currency denominated assets will experience a decrease in wealth, contributing to a reduction in output and negative pressure on inflation (Lane and Shambaugh, 2010; Georgiadis and Mehl, 2016). The revaluation effect of monetary policy spillovers can also impact foreign currency-denominated collateral value, affecting the ability to borrow and lend.

The effects of monetary spillovers on balance sheets also impact investors, where changes in valuation can cause cross-border financial stress among highly leveraged individual and institutional investors. For highly leveraged investors, small changes in their assets' value could force them to deleverage, reducing their asset and debt positions. If these investors constitute a significant source of domestic lending, reductions in their debt and asset position may transfer the foreign monetary contraction to the domestic economy (Ca' Zorzi et al., 2020).

Lastly, monetary policy operates through the financial channel due to its effect on credit spreads. As investors can seek foreign and domestic investments that are traded across countries, the forces of demand, supply, and arbitrage will cause foreign and domestic assets' returns to equalize (Bodie et al., 2017). Consequently, borrowing cost across countries will also converge. The equalized cost of borrowing coupled with the market integration for risk-free bonds leads to an equalization of credit spreads between countries. A foreign monetary tightening will therefore increase credit spreads abroad, consequently affecting aggregate demand (Ca' Zorzi et al., 2020). The co-movements in credit spreads will also cause other financial and macroeconomic variables such as productivity, asset prices, and the need for 10 capital to move in the same direction. (e.g., Dedola and Lombardo, 2012, and Devereux and Yetman, 2010).

3: Previous Empirical Research

The issue presented by previous research when estimating monetary spillovers is that of endogeneity, where foreign and domestic monetary policy is influenced by a wide range of economic and financial conditions. Monetary policy is therefore correlated with these conditions and cannot be directly measured as the estimates will be biased (Jarocinski and Karadi, 2020). A further complication of measuring monetary policy that has arisen during recent years is the central bank interest rates hitting the zero-lower bound (ZLB), causing central banks to lose their ability to influence the economy through conventional monetary measures (Babecká Kucharčuková et al. 2016). Monetary policy has therefore been increasingly conducted through forward guidance (information disclosure regarding future monetary policy) and asset purchasing programs, known as unconventional monetary policy or quantitative easing (Nakamura and Steinsson, 2018; Bu et al, 2020). To combat the problems of endogeneity and the ZLB, a wide variety of approaches to estimate foreign monetary policy and its spillover effects have been applied throughout the monetary policy spillover´s literature.

In a paper written by Georgiadis (2015), the author employs a global VAR model (GVAR) to estimate the cross-country impact of ECB monetary policy on 61 individual emerging and developed economies. The dataset consists of quarterly observations from 1999, when the ECB assumed responsibility for monetary policy in the Euro-area, to 2009. The authors impose sign restriction to identify the impact of ECB monetary policy on variables such as output, prices, and short-term interest rates, respectively for the euro area and non-euro countries. The results show that monetary policy spillovers caused by an ECB monetary tightening had large contractionary effects on real GDP in foreign countries. The GVAR model's implementation also allows the authors to account for the multilateral nature of global interlinkages, resulting in the conclusion that variables such as trade, financial integration, foreign asset position, and exchange rate regime play an important role. For example, the authors find that financial market structure and financial integration explain a large fraction of the cross-country heterogeneities.

Another paper written by Babecká Kucharčuková et al. (2016), investigates the effects of conventional and unconventional monetary policy from the ECB to six non-euro small open economies within the E.U. They refer to conventional policy as the outcome of the ECB altering

11 the interest rate, while unconventional policy refers to the purchasing of assets and other forms of quantitative easing. To identify conventional and unconventional policy measures as exogenous, they measure the Euro area's overall monetary condition by constructing a synthetic index using factor analysis from January 2000 until July 2015. The factors include the money supply, interest rates, exchange rates, credit spreads, and the ECB balance sheet.

To estimate the impact of ECB monetary policy, subcomponents of the synthetic index are included in a block-restricted Structural Vector Auto Regression model (SVAR), where the effect is obtained via impulse response analysis. The block-restricted SVAR model is then used independently for each economy, where each SVAR model contains domestic variables for each country as well as the Euro area variables. The authors also assume that the small economies' shock-response do not transmit back to the ECB, a key identifying assumption throughout the monetary policy spillover´s literature. The results suggest that for conventional monetary policy, the spillovers' effect on foreign inflation and output is the same as the policies effect in the Euro area. For unconventional policies, the effect of the spillovers is transmitted through exchange rates which quickly responds to the shock. The impact on the macroeconomic variables is however weak and slow, where inflation is unaffected in most cases.

Similar results are found by Hajek and Horvath (2017), who study the effects of conventional and unconventional monetary spillovers for the U.S. and the ECB. Their paper uses a Global VAR (GVAR) model to estimate the impact of monetary policy spillovers from the U.S Fed and the ECB to economic activity and prices in non-euro E.U economies. The data consist of monthly observations from 2001 to 2016, where the authors employ a measure of the shadow monetary policy rate to account for unconventional monetary policy and the short-term rate hitting the zero-lower bound. The authors find that an unexpected increase in the ECB or U.S Fed shadow monetary policy rate of 100-bp respectively lead to a contraction of real output by 0.6 % within the euro-area. In contrast to the response of economic activity, the increase in the ECB and U.S shadow policy rate only led to a -0.1 % reduction in domestic consumer prices, considerably lower than when estimates are made with conventional interest rates (see Hajek and Horvath, 2016). Like Babecká Kucharčuková et al. (2016), the authors conclude that the effects of conventional monetary spillovers are more potent compared to unconventional spillovers.

For the individual non-euro countries, Hajek and Horvath (2017) show that the most substantial impact of monetary policy spillovers is found in Sweden, where an increase in the ECB shadow policy rate of 100-bp led to a 0.8 % reduction in Swedish output. A similar response is observed 12 following the same contraction in the U.S Fed shadow policy rate. Related impacts on real activity from ECB and FED shocks are also found for other countries. In aggregate however, the authors finds that the euro area monetary policy shocks have a more significant impact than those emanating from the U.S. Like Georgiadis (2015), the authors conclude that countries with stronger trade-links and financial integration experience more significant impacts from monetary policy spillovers.

In contrast to the findings of Hajek and Horvath (2017), an impactful paper by Chen et al (2015) analyzes the impact of unconventional monetary policy spillovers from ECB and U.S. Fed for 24 countries, including Sweden. Thought the application of shadow interest rates developed by Lombardi and Zhu (2014), in addition to sign restrictions, the authors identify unconventional monetary policy shocks which are then included in a Global Vector Error-Correction model (GVECM). The authors find that unconventional monetary policy spillovers from the US have a more substantial and longer lasting impact than those from the ECB for all countries included in their sample.

Another impactful paper by Gertler & Karadi (2015) employs a hybrid approach with a High Frequency Identification (HIFI) method proposed by Kuttner (2001), and a VAR model to estimate U.S monetary policy's impact on economic activity through its effect on credit costs. Their data consist of monthly observations between august 1979, when Paul Volcker's began his term as Fed chair, to June 2012. The data also includes the period during the great recession when the interest rates hit the ZLB, which is in addition to forward guidance, accounted for by the HFI. The HFI is used to estimate monetary policy shocks by calculating the "surprise" in yields of federal funds futures within 30 minutes of policy announcements on Federal Open Market Committee (FOMC) days. The calculated monetary policy shocks are then used as external regressors in a VAR model to capture the effects of U.S monetary policy. The results show that an unexpected monetary contraction reduces foreign output, positively impacts foreign credit cost, and exhibits a small and non-significant negative effect on foreign inflation. Lastly, the authors show that forward guidance is important to consider.

A paper that tries to replicate the impulse responses obtained by Gertler and Karadi (2015) is Dedola et al (2017), who estimates U.S monetary policy shocks though the application of sign restrictions. The authors then regress macroeconomic and financial variables on the estimated monetary policy shocks for 18 emerging and 18 advanced economies. The authors find that a surprise monetary contraction by the U.S Fed caused the foreign currency to depreciate against the USD, which contributed to an improvement in the trade balance for the foreign country. 13

However, despite the improved trade balance, industrial production, real GDP, and inflation still contracts, while unemployment rises. In terms of financial variables, the response is more muted as well as heterogeneous between countries. For example, the authors observe an increase in bond yields compared to the US yields for most countries, while housing and real equity prices exhibit contractionary responses in half the countries analyzed. Interestingly, in contrast to Georgiadis (2015), the authors also finds that there exists no systematic relation between the characteristics of a country and that country’s response to the monetary policy shocks.

Related to the work of Babecká Kucharčuková et al. (2016), Hajek and Horvath (2017) and Gertler & Karadi (2015) is the paper by Albagil et al (2019). They compare the spillover effects of U.S Fed conventional and unconventional monetary policy to foreign bond markets. The authors identify exogenous U.S monetary policy shocks by observing the change in yields of short-term interest rate derivatives around a Federal Open Market Committee (FOMC) meetings, as in Gertler & Karadi (2015). The monetary policy shock estimates are then included in a panel regression to estimate their effects on foreign bond yields. The authors find that the impact of U.S monetary policy have large impacts on long term-yields, and that this effect has become stronger following the financial crisis of 2008. Similar findings have also been observed by (Neely, 2013).

3.1 Monetary Policy Spillovers, the Bias of Information Shocks

In recent years, research of monetary policy spillovers has found that previous estimates of foreign monetary policy are likely to exhibit biased results due to the presence of the “central bank information effect” (Bu et al, 2020). The central bank information effect can best be described as the ability of central banks to affect market expectations/ behavior, in addition to monetary policy, by disclosing privately held information regarding the future economic outlook. For example, if a central bank were to reduce the interest rate while also disclosing negative information regarding the economic outlook, different adjustments in the economy are expected to be observed compared to when only an interest rate reduction is announced (Saskia et al, 2018, Ca' Zorzi, 2020). In other words, the spillovers emanating from a central bank information shock are likely to be different compared to the effects of spillovers from a monetary policy shock. Therefore, failing to account for this vital effect could cause biased estimates to the effects of monetary policy spillovers (Jarocinski, 2020).

In a paper by Saskia et al (2018), the authors estimate monetary policy shocks as well as central bank information shocks by using a HIFI method. The paper follows the method of Gurkaynak et al (2005), where the authors study the changes in interest rates for a multitude of ECB money market instrument around the same time as an ECB monetary policy announcement. Through observing the interest rate changes in the different market instruments, the authors construct two different shock series, one for the monetary policy shocks and another for the central bank information shocks. Related methods have also been used in recent papers (e.g., Nakumura and Steinsson, 2018; Degasperi et al, 2019; Bu et al, 2020). The shock series are then included in a panel regression to estimate their spillover effects on financial variables in Sweden, Norway, and Denmark. These findings suggest that the monetary policy shocks and the information shocks both have large and significant effects on small open economies in close proximity to the euro area. Moreover, while the spillover effects of a monetary policy shock policy die out quickly, the impact of an ECB information shock is more persistent. The authors therefore suggest that the information spillovers are also likely to be able to affect macroeconomic variables in the small open economies.

Another paper that tries to solve for the bias associated with the central bank information effect is Jarocinski and Karadi (2020). The authors first estimate U.S Fed and ECB monetary policy using an HFI method like Gertler and Karadi (2015). Then, they observe the co-movement of stock prices and the yields of central bank interest rate derivatives during central bank policy announcements. Through the application of sign restrictions, the estimated monetary policy shocks are decomposed and classified as either a central bank information shock or a monetary policy shock. In line with the method of Gertler & Karadi (2015), the authors then use the decomposed monetary policy/ information shocks in a VAR model to study their spillover effects on foreign economies. The results suggest that omitting the information shock contributes to severely biased estimates of monetary policy spillovers, especially for the ECB.

Using the monetary policy shock series of Jarocinski and Karadi (2020), Ca' Zorzi et al (2020) investigates the impact of U.S Fed and the ECB transatlantic monetary policy spillovers on each other’s economies, in addition to emerging market economies. The shock series are included in a standard Bayesian VAR model (Sims, 1980), where the results for the endogenous variables are obtained via impulse response caused by a 1 percentage point ECB or U.S Fed monetary contraction. Their results suggest that while the spillovers to consumer prices are small, the spillovers stemming from the U.S Fed have larger impacts on Euro-area financial markets and real activity compared with the ECB effects on U.S variables. The same result is also found for

15 emerging market economies, where the spillovers from the U.S Fed have much stronger impacts.

3.2 Contribution of My Thesis to the Monetary Policy Spillover`s Literature

While the literature on monetary policy spillovers is vast, there are to the best of my knowledge, no papers which has compared the effects of monetary policy spillovers from the US Fed and ECB to a small open economy while controlling for the information effect. Furthermore, while this paper relates to previous research, as estimates of monetary policy and central bank information shocks have been obtained from the work of previous authors, the method applied to estimate their respective spillover effects is different. In contrast to most previous papers who applies some form of the VAR model, my paper estimates the effects of spillovers through the application of a Local Projections model proposed by Jorda (2005). While this model has been used by some of the previous papers (e.g., Bu et al., 2020; Jarocinski, 2020), it has never been applied a comparative setting such as this, since its application within the monetary policy´s spillovers literature is rather new.

The Local Projections model is superior in my view as it is less vulnerable to misspecifications, can account for non-linear specifications, and is also able accommodate a wide range of different endogenous variables, something which can cause problems with degrees of freedom in a VAR model (Sandström, 2017). Therefore, this paper is able to add to the findings of Saskia et al (2018) and Hajek and Horvath (2017) as estimates of exogenous ECB monetary policy and central bank information spillovers can be made to a wide range of both real and nominal macroeconomic and financial variables. In addition, the local projections model also allows for an in-depth analysis between the different effects of U.S and ECB monetary policy spillovers to a small open economy. Lastly, this paper contributes to the literature by comparing US conventional and unconventional monetary policy spillovers that has been purged from the information effect. While previous papers have made extensive comparisons between the two, there are to the best of my knowledge, no comparison which has controlled for this crucial effect.

4: Data of ECB and U.S Fed Monetary Policy

To estimate the effects of ECB and US Fed monetary policy spillovers, measures of the respective central bank’s monetary policy shocks have been obtained from previous papers

16 who, using different methods, have purged these shocks from the central bank information effect. The dataset for the exogenous ECB monetary policy shocks (figure 4.1) has been obtained from updated dataset of Jarocinski (2020) and consists of monthly observations between Jan 1999 - May 2019 with 126 ECB monetary policy shocks. Data on the ECB information shocks have also been obtained from the Jarocinski (2020) dataset and consists of monthly observations with 113 ECB information shocks during the same time-period (figure 4.2). For a detailed description of the construction of ECB monetary policy/ information shocks, see appendix 1A.

Figure 4.1: ECB Monetary Policy Shocks

0,25 0,2 0,15 0,1 0,05 0 -0,05 -0,1 -0,15 -0,2 -0,25

Figure 4.2: ECB Information Shocks

0,25 0,2 0,15 0,1 0,05 0 -0,05 -0,1 -0,15 -0,2

To contrast the results obtained from the ECB monetary policy shocks, estimates of the impacts emanating from U.S monetary policy spillovers will also be considered. The dataset for the U.S has been obtained by Bu et al (2020) and consists of 156 monthly monetary policy shocks between Jan 1999 - May 2019 (figure 4.3). The dataset from Bu et al (2020) has like the one from Jarocinski (2020), been purged from the information effect. For a detailed description of the construction of US monetary policy shocks, see appendix 1B.

Figure 4.3: U.S Federal Reserve Monetary Policy Shocks

0,25 0,2 0,15 0,1 0,05 0 -0,05 -0,1 -0,15 -0,2 -0,25

4.1 Swedish Economic and Financial Variables

To estimate the effect of US and ECB monetary policy spillovers on the Swedish economy, the response of several financial and economic variables will be considered. The dataset consists of monthly data collected for five real and nominal macroeconomic variables and four financial variables. The variables are summarized in the following tables. For figures and a more detailed description of the Swedish variables, see appendix 1C.

Table 4.1: Swedish Real and Nominal Macroeconomic Variables

Variable Definition Source CPIF Consumer Price Index at fixed Statistics Sweden interest rates Industrial Production Log of seasonally adjusted Federal Reserve Economic Swedish industrial Production Data Exports Log of Swedish total export to World Integrated Trade the US and Germany Solution Exchange rates Nominal USD/SEK and Swedish Central Bank EUR/SEK exchange rate Swedish Short Rate Swedish short-term interest Swedish Central Bank rate

Table 4.2: Swedish Financial Variables

Variable Definition Source Government Bond Yield 10-year Swedish government Swedish Central Bank bond yield Term-Spread 10 minus 2-year Swedish Swedish Central Bank and government bond yield authors calculations

Real SIX Total Return Index Log of SIX index price Bloomberg and authors divided by CPIF. The index calculations measures the average return, including dividends of over 300 companies listed on the Stockholm Stock exchange.

Corporate Bond Spread Option adjusted investment Bloomberg grade Swedish Corporate Bond spread

5: Methodology to Study the Effects of Spillovers

To estimate the effect of the monetary policy shocks, the Local Projections model of Jorda (2005) is applied in a time series setup, where a response horizon (h) between 0 and 25 months is estimated for each endogenous variable. The response horizon of 25 months has been chosen to allow the impact of the shock series to have adequate time to fully materialize. Similar response horizons can be found in previous literature (e.g., Ca' Zorzi, 2020; Jarocinski, 2020).

The Local Projections method is used here instead of the typical VAR model commonly found in the monetary policy spillover’s literature for several reasons. Firstly, the Local Projections model can provide estimates that are less vulnerable to misspecification since the impulse response is separately estimated for each horizon of interest. This is not the case in a VAR model where estimates instead are extrapolated into periods which lies in distant horizons (Jorda, 2005). The Local Projections model also permits controlling for a large set of variables, something which could cause problems with degrees of freedom in a regular VAR model (Sandström, 2018). Thanks to this feature, estimates of foreign monetary policy and central bank information spillovers can be made to a wide range of both real and nominal macroeconomic and financial variables. This is of great importance as this paper can in contrast

19 to some of the previous literature, fully gage the effects of foreign spillovers to different parts of the Swedish economy. Lastly, the local projections model is also preferable due to its ability to accommodate non-linear specifications. For each response horizon, the following specification is applied:

Yt+h = βjΣShockt-j +δjYt-1-j +ut For h=1,…..30 (5)

Here, h is the response horizon, j is number of lagged months and Y is the endogenous variable, such as the total industry production, exchange rates, inflation etc. ΣShockt-j is the summation of the shock series while Yt-j is the lag of the dependent variable, both including up to 6 lags respectively, in line with previous papers. The lag of the dependent variable has been included to control for the predetermined path, while the lag of the monetary policy shock has been included to control for serial correlation. The subscript t marks the dates of ECB/ U.S Fed monetary policy announcements and Ut is an error term.

The coefficient of interest is βj which shows how the endogenous variables changes over h months per one basis point of ECB/ US Fed monetary contraction. In addition to the monetary policy shock series, the shock series included in ΣShockt-j can also be a central bank information shock series or a total shock series, depending on what type of spillovers one seeks to estimate. In this setting, a total shock is referred to as a monetary policy shock that has not been purged from the information effect.

Since there are no ways to correctly estimate the appropriate lag length of the variables in the Local Projections model (Brugnolini, 2016), this paper considers multiple numbers of lags for both the shock series and the endogenous variables. The results suggest that the estimates are robust to the use different lag-lengths (see appendix 2A). In addition to the criticisms regarding the inability to accurately specify the correct lag length of the included variables, the Local Projections model have also received criticism related to the bias associated with the use of small samples (Herbst and Johannsen, 2020). However, in the case of this paper, the sample size studied is relatively large with more than 250 observations. Therefore, the problems proposed by Herbst and Johannsen (2020) should not be of any concern here.

6: Results

This section presents the results from the Local projections model (Jorda, 2005), where the effects of ECB monetary policy and central bank information shocks are estimated on the Swedish endogenous variables. The results from the individual shocks are then contrasted with the effects obtained from a total shock, which includes both the monetary policy and central bank information shock. Estimations show that when the information effect is omitted, a severe bias in the estimates emerges. For example, spillovers from a total ECB shock appears not to affect the Swedish short rate and has a much weaker effect on Swedish exports and industrial production. Moreover, failing to control for the information effect also results in expansionary effect for Swedish inflation, which is counterintuitive. Throughout the rest of this section, this paper shows that controlling for the information effect contributes to rationalizing these otherwise puzzling findings.

6.1: Spillovers Effects from ECB Monetary Policy Shocks Vs ECB Information Shocks to Macroeconomic Variables

The result from the individual shocks highlights the difference between the exchange rate response to a monetary policy shock and a central bank information shock. In line with economic theory, a monetary tightening by the ECB results in a positive spread between Swedish and Euro-area interest rates, making investors exchange their Swedish currency for Euros to seek better returns in Euro-denominated assets. In line with the uncovered interest rate parity condition, figure 6.1.1 shows the Euro to appreciate against the SEK for about 15 months and depreciates thereafter.

While in line with the findings of Soyoung and Roubini (2000) and economic theory, the response is however not statistically significant at the 10 % level. The results are also somewhat different from Ca' Zorzi et al. (2020), whose estimates suggest that the Euro strongly appreciates against the USD following an ECB monetary contraction. The appreciation in their paper is also significant and persistent over the full period. A possible explanation for the weaker appreciation of the Euro observed here is given by Babecká Kucharcuková et al. (2016). Their results suggest that the effects of unconventional monetary policy spillovers can have an opposite effect for different countries compared with the effects of conventional monetary policy spillovers. Therefore, they argue that the total response of the exchange rate to both

21 conventional and unconventional monetary policy can be weaker and less significant for some economies.

In contrast to the effect of a monetary policy shock, the EUR/SEK exchange rate response following an ECB information shock is negligible and insignificant. The response suggest that the impact of the ECB information shock is global in nature (Ca' Zorzi et al., 2020), meaning that the revised positive economic outlook from the ECB can be applied to the Swedish economy as well. A positive outlook in the Euro-area and Sweden should therefore not cause any changes to the exchange rate. Moreover, because of the insignificant effect of an ECB information shock, the exchange rate response to a total ECB shock is like the response observed in the case of a monetary policy shock. Although the 90 % confidence bands get wider, the effect suggests that omitting information effect does not bias the exchange rates' results.

When the ECB information shock effect is different from zero however, a substantial difference in the estimates emerges. In response to an ECB monetary contraction, Figure 6.1.2 shows that Swedish industrial production falls, reaching a trough of approximately - 0.4 % after ten months. Related contractionary response for Swedish output following an ECB monetary tightening is also observed by Hajek and Horvath (2017). Likewise, similar contractionary responses have been observed by Feldkircher et al. (2017) for central Europe and Babecká Kucharcuková et al. (2016) for European small open economies.

In contrast, the response of Swedish industrial production to an ECB information shock is, although insignificant, expansionary. The heterogeneous response is in line with Jarocinski (2020), whose results suggest that endogenous variables' response can have opposite signs following an ECB monetary policy/ information shock. Moreover, as the industrial production's positive response to the ECB information shock is included in the total shock, the response is much weaker and less significant compared to a monetary policy shock, causing the results to exhibit a positive bias. Instead of declining by 0.4 %, total industrial production declines by 0.2 % and returns to its previous state after 20 months.

Like the estimates observed in the case of industrial production, the omission of the ECB information shocks causes the response of Swedish exports to exhibit a weaker response following a total shock compared to a “pure” monetary policy shock. Figure 6.1.3 shows that following a monetary contraction from the ECB, Swedish exports to Germany declines by close to – 0.4 % after ten months, compared to 0.2 % in the case of a total shock. Ca' Zorzi et al

(2020) find similar results where exports in the Euro area significantly decline after an ECB monetary contraction. In their estimates, they also find that while monetary policy conducted by the Federal Reserve causes large impacts on financial conditions, spillovers from ECB are to a large extent propagated through their effects on trade.

This is supported by the estimations below which shows that the reaction of Swedish exports follows a similar pattern to the one observed for industrial production. The result is not surprising, as Swedish exports accounted for on average 41 % of Swedish GDP during the period of analysis (The Global Economy, 2021). The results therefore suggests that exports account for a vital channel through which monetary spillovers affects the output of some foreign economies, as was argued by Ca' Zorzi et al (2020) and demonstrated by the modified IS curve in section 2.2. The idea of spillovers having a substantial impact on foreign output trough trade is further supported by Georgiadis (2015). In his paper, he argues that the extent of the spillovers effect largely depends on the receiving country's characteristics, where trade openness and financial integration play a critical role. Similar results are also found for spillovers from the U.S by Bräuning and Sheremirov (2019). They argue that trade relationships appear to better explain foreign monetary spillovers' effects on the real economy than that of financial relationships.

Another variable whose response differs vastly following a monetary policy/central bank information shock is the Swedish short rate. Figure 6.1.4 shows that the Swedish short rate exhibits a large and significant decline following an ECB monetary policy shock, reaching a trough of – 6 % after ten months. In contrast, the short rate increases, although less significantly, after an ECB information shock, contributing to an almost insignificant response in terms of a total shock. These results advocate that the Swedish central bank moves to mitigate the effects of the ECB monetary policy shock, but not the ECB information shock. Consequently, the results from the ECB total shock on the Swedish short rate exhibits a strong underestimated effect in contrast to the monetary policy shock. Similar behavior for the U.S short rate following an ECB monetary policy shock is found by Jarocinski (2020), where the US Federal Reserve moves to mitigate the effects of an ECB monetary policy shock by lowering the federal funds rate. Following an ECB information shock, the federal funds rate response instead goes in the same direction as the ECB short rate.

The heterogeneous response of the Swedish central bank short rate is intuitive and makes sense. From the Swedish central banks' point of view, a foreign monetary contraction can be interpreted as an external demand shock to the Swedish economy. Therefore, reducing the short 23 rate helps mitigate such a negative shock (Lassen, 2020; Jarocinski, 2020). The short rate reduction also helps explain the weak response of inflation and financial variables, which will be covered in the next subsection. On the contrary, an ECB information shock could be regarded as an adjustment to the future economic outlook at a global level. Therefore, the related contractionary policy response by different central banks is unsurprising (Jarocinski, 2020).

Another serious bias observed following a total ECB shock is the Swedish inflation, whose response is expansionary, reaching a significant peak of 2 % after 3 months. This result is however mainly caused be the central bank information effect as the results suggests that Swedish inflation responds unambiguously following an ECB monetary contraction. Similar results are found by Nakamura and Steinsson (2018). The weak response of inflation following a monetary policy shock could be attributed to effects of unconventional monetary policy, which is shown to have initial expansionary effects on Swedish inflation (Babecká Kucharcuková et al., 2016). However, like previous papers, the estimation shows that monetary policy spillovers to consumer prices are relatively small and short-lived (e.g., Ca' Zorzi et al., 2020, Hajek and Horvath, 2017).

ECB Monetary Contraction ECB Information Shock ECB Total

Figure 6.1.1: 1 Shock EUR/SEK exchange rate

Figure 6.1.2: Log Production of Total Industry

Figure 6.1.3: Log Exports to Germany

Figure 6.1.4: Swedish Short Rate

Figure 6.1.5: Inflation (CPIF)

1 The left-hand column shows the median impulse response to an ECB monetary contraction (blue), the middle column shows the median shock response to an ECB information shock (red) and the right-hand column shows the median shock responses to a total shock (grey). The solid line shows the median impulse response with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each shock is reported as a 1 percentage point monetary contraction and the change to the endogenous variables are presented in % on the Y axis.

6.1.2 Spillovers to Financial Variables

Like previous papers who finds that ECB monetary policy has negligible effects on foreign financial variables (e.g., Ca' Zorzi (2020); Hajek and Horvath (2017); Jarocinski (2020), the estimates below show similar results. However, the same weak response is not observed following an ECB information shock, where many of the financial variables exhibit significant adjustments as in Jarocinski (2020). However, despite the significant effect of the ECB information shocks, the total shock bias associated with the financial variables is somewhat less severe than that of the previous section's macroeconomic variables. Nevertheless, the results here suggest that failing to account for the information effect can lead to wrong conclusions regarding the impacts of ECB monetary policy to Swedish financial conditions.

Figure 6.1.6 shows that following an ECB monetary tightening, the 10-year government bond yield initially increases for approximately five months to depreciate thereafter, reaching a significant trough of -3% after 28 months. This result may appear somewhat puzzling at first since an increase in the short rate should, ceteris paribus, contribute to increased bond yields. However, when considering the sizeable expansionary response of the Swedish short rate, which in turn has a depressing effect on bond yields, the puzzle disappears. Figure 6.1.4 and figure 6.1.6 also shows that the Swedish short rate and the Swedish Government bonds yield follow a similar pattern after an ECB monetary contraction, lending credibility to the short rate's response having an important role. The suggestion that the short rate have an important offsetting response is also argued by Albagli et al (2019).

Likewise, the Swedish short rate response also helps to explain the impact that the ECB information shocks has on the government bond yields. To mitigate the positive impact of an ECB information shock, the Swedish central bank raises interest rates which depresses government bond prices and consequently leads to an increase in government bond yields. Furthermore, like that of the macroeconomic variables from previous section, the effect of the central bank information also causes the response of the government bond yields to exhibit biased results following a total shock.

Another variable whose weak response could be explained by the effect of the expansionary Swedish short rate response is the Swedish stock market observed in figure 6.1.7. In the absence of the short rate reduction, the Swedish stock market price index would likely have fallen due to the subsequent expected decrease in industrial production and trade. However, the decrease

26 in the discount rate caused by the reduced short rate likely offsets the ECB monetary contraction's negative impact.

A similar argument can be given for the stock market's negative response following an ECB information shock. While the information effect contributes to expansionary responses of the macroeconomic variables, it also causes the Swedish central bank to raise the short rate. Investors therefore anticipate an increase in the Swedish short rate, resulting in a stock market drop as the expected discount rate increases. Related results have also been presented by Nakamura and Stiensson, (2018). Like that of the government bond yield, the results for the Swedish stock market consequently shows that the Swedish short rate increase has a stronger impact than the ECB information shock. Furthermore, related to what has been observed for the previous variables, the reduction in the Swedish stock market following an ECB total shock is also misleading and can mostly be contributed to the information effect.

Furthermore, the effect of an ECB monetary policy shock exhibits a negligible impact on corporate bond spreads in the short run. The response is however significantly expansionary in the long run. Unlike the impact on the stock market, an ECB information shock causes a large and significant expansionary response. Therefore, in the case of the corporate bond spreads, it would appear as if the impact of the information shock outweighs the contractionary effect caused by the increased Swedish short rate. The result is in line with the findings of Jarocinski (2020). In his estimates, an ECB information shock causes a significant reduction in U.S high yield OAS corporate bond spreads. He also finds that the response to an ECB monetary contraction is negligible.

Out of the four financial variables considered in this analysis, the only variable to exhibit a stronger response to an ECB monetary contraction is the term spread, whose expansionary response reaches a peak of about 3 % after 10 months. However, the response is also significant following an ECB information shock, where the term spread reaches a trough of about 2,5 % within 15 months. Therefore, it would appear as if the ECB information spillovers that propagate though the financial conditions channel are stronger and more significant compared to the ECB monetary policy spillovers. In terms of the exchange rate and aggregate demand channel in the previous section, the relationship is the reveres, where ECB monetary policy spillovers exhibit the strongest and most significant impacts.

ECB Monetary Contraction ECB Information Shock ECB Total

Figure 6.1.6: 2 10-year Gov bond yield

Figure 6.1.7: Stock Market Index

Figure 6.1.8: Corporate Bond Spread

Figure 6.1.9: Term Spread

2 The left-hand column shows the median impulse response to an ECB monetary contraction (blue), the middle column shows the median shock response to an ECB information shock (red) and the right-hand column shows the median shock response to a total shock (grey). The solid line shows the median impulse response with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each shock is reported as a 1 percentage point monetary contraction and the change to the endogenous variables are presented in % on the Y axis. 28

6.2 Spillovers Effects from ECB Monetary Policy Shocks Vs U.S Fed Monetary Policy Shocks

This section will concern monetary policy shocks from the U.S Fed and their effects on the same endogenous variables analyzed above. While the previous section covered the impact of both monetary policy and central bank information shocks, this section will only concern the comparison of monetary policy shocks for the U.S and ECB. The motivation for this is highlighted by Jarocinski (2020), who find that the effect of the US central bank information shock on endogenous euro-area variables is minor, and not as strong as the ECB information shocks. Moreover, recently published papers by Hoesh et al. (2020) and Bauer and Swanson (2020) even question the importance of the U.S Fed information effect, where Hoesh et al. (2020) argues that the U.S Fed has lost its informational advantage during the last decade. However, while the information effect for the U.S shocks may exhibit a minor effect on endogenous variables, omitting it can still cause biased results. Because of this, the U.S monetary policy shocks have like the shocks for the ECB, been purged from the information effect. The comparison between the ECB and US will focus on the different transmission channels through which spillovers from the two central banks propagates, where their respective strengths and persistence will be compared. Previous papers have found strong spillover effects from U.S Fed monetary policy compared with ECB monetary policy effects, particularly for financial variables. For example, Feldkircher and Huber (2016), Navarro and Iacoviello (2019), and Georgiadis (2016), find that spillovers from the U.S Fed have strong effects on euro-area economies, sometimes stronger than their domestic effects in the U.S. In contrast, results found by Lassén (2020) and Hajek and Horvath (2017), suggest that Euro-area monetary policy spillovers exhibit stronger and more significant impacts compared with spillovers from the U.S, whose effect is suggested to be weak and/or insignificant on average.

6.2.1 Spillovers to Macroeconomic Variables

In comparison with the ECB's effects, a monetary contraction from the U.S Fed contributes to more expansionary responses for the Swedish endogenous variables. Following a Fed monetary contraction, figure 6.2.1 show that Swedish industrial production increases slightly, however insignificant. This response can be somewhat puzzling as economic theory suggests that a tightening of monetary conditions should decrease production, as was observed for the ECB. For example, Bräuning and Sheremirov (2019) and Bu et al (2020) found that a U.S monetary

29 contraction decreased foreign output, especially for countries relatively more open to trade. The same results are observed by Jarocinski (2020) and Nsafoah & Serletis (2019). One explanation for the contrasting results observed here is the strong ties of the U.S. economy to the economy of the euro area. Because of these ties, the euro-area might be able to absorb part of the U.S monetary spillovers before they are transmitted to the individual countries (Hajek and Horvath, 2017).

Another likely explanation for the expansionary impact is the response of the Swedish short rate, the exchange rate, and their respective effects on Swedish exports. Figure 6.2.2 show that in response to a U.S monetary contraction, the USD considerably appreciates against the SEK over the whole period, reaching a significant peak of 4 % in 15 months. This exchange rate appreciation is much stronger than the one observed for the Euro and is also more persistent. The reason for the relatively stronger appreciation is however not clear, since the short rate response after a U.S Fed monetary contraction is less expansionary compared to the one observed for the ECB. One possible explanation for the stronger appreciation of the USD/SEK exchange rate could be attributed to the USD's dominant role in global markets, as was found by Degaspari and Hong (2019). Therefore, a monetary contraction from the U.S Fed could cause a larger appreciation of the USD compared to the EUR.

The USD's strong appreciation also causes an expansionary response of Swedish exports to a U.S Fed monetary contraction. Figure 6.2.3 shows that exports to the U.S initially increase by 0.5 % within 10 months, where it reaches a significant peak and then declines thereafter. This finding is like the one observed by Ca' Zorzi et al. (2020), where they found that Euro-area exports exhibit a mild increase in the short run following a U.S monetary contraction. The same results are also observed by Dedola et al (2017), who finds that a U.S contraction contributes to an improvement in the trade balance for foreign countries. However, the authors also find that despite the improved trade balance, industrial production still contracts.

The results also stand in stark contrast to Degasperi et al (2019), whose findings suggest that the demand-reducing effect in the US is stronger than the positive effect of the exchange rate. Nevertheless, while the response of Swedish exports to the U.S is positive, it is somewhat muted and less significant compared to the impact of an ECB monetary contraction, illustrating the more robust effect of ECB shocks on Swedish macroeconomic variables, as was argued by Lassén (2020) and Hajek and Horvath (2017).

Further in line with the findings of Lassén (2020) and Hajek and Horvath (2017) is the result obtained for the Swedish short rate in figure 6.2.4. In response to a U.S Fed tightening, the Swedish central bank moves to mitigate against the spillover effects by reducing the short-term rate. The Swedish short rate response is however much weaker and less significant compared to an ECB tightening. According to Lassén (2020), this is to be expected as Sweden is more integrated with the Euro- area in terms of real variables and should therefore have a stronger response to an ECB monetary contraction.

Lastly, like the response following an ECB monetary contraction, figure 6.2.5 shows that the response of inflation to a U.S Fed monetary tightening is negligible over the entire period. The same results are found in the literature of monetary policy spillovers.

ECB Monetary Contraction U.S Monetary Contraction

Figure 6.2.1:3 Log Industrial production

Figure 6.2.2: EUR/SEK and USD/SEK exchange rate

Figure 6.2.3: Log Exports to Germany/U.S.

Figure 6.2.4: Swedish short rate

Figure 6.2.5: Inflation (CPIF)

3 The left-hand column shows the median impulse responses to an ECB monetary contraction (blue) and the right-hand column the median impulse responses to a Federal Reserve monetary contraction (orange). The solid line shows the median impulse response to the respective central bank spillovers with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each monetary contraction is reported as a 1 percentage point monetary contraction. The change to the endogenous variables is presented in % on the Y axis. 32

6.2.2 Spillovers to Financial Variables

Unlike previous papers which finds that U.S monetary policy shocks negatively impact foreign stock markets, estimates here suggest that the response of the financial variables is mostly negligible, at least in the short run. In particular, figure 6.2.7 shows that after a U.S Fed monetary contraction, the Swedish stock market does not respond until after 15 months, where it starts to decline for five subsequent months, reaching a trough of – 2%. A similar estimate is observed for the term spread in figure 6.2.9, whose response in the short run is negligible but falls in the long run. The heterogeneous response of the stock market and the term spread following an ECB and U.S Fed monetary contraction could be contributed to the response of the Swedish short rate, whose response to an ECB monetary contraction is much larger and more significant. Furthermore, the Swedish short rate also starts to increase approximately 20 months after a U.S monetary tightening, around the same time- period as the stock market begins to decline. A related but muted pattern can also be observed for the term spread, further confirming the importance of the Swedish monetary policy response reflected in the short-term rate.

Another variable whose response following a U.S Fed shock differs significantly to an ECB shock is the 10-year government bond yield in figure 6.2.6. After a U.S monetary contraction, the yield of the 10-year government bond declines, reaching a significant trough of -2 % within 5 months. Interestingly, it would appear as if the expansionary response of the Swedish short rate offsets the positive effect of a U.S monetary contraction to the Swedish bond yield in the short run. The yield however starts to increase after 5 months, returns to its previous state within 15 months and continues to increase thereafter. In contrast, the corporate bond spread response in figure 6.2.8 appears to follow that of an ECB tightening, where the effect of the U.S/ ECB monetary contraction also appears to be of similar significance.

The overall response of the financial variables appears to be of similar strength after a U.S / ECB monetary policy shock. Moreover, the response of the financial variables also appears to a follow the Swedish short rate to a large extent. Therefore, the trivial effect to a Fed monetary contraction observed in the short run could be, to some part, attributed to the Swedish central bank's offsetting response. Similar results have also been found by Albagli et al (2019) and Nakamura and Stiensson (2018). In the long run, the Swedish short rate increases, causing the response of some of the financial variables to turn negative. In summary, it would appear as if both spillovers from the ECB and U.S Fed affect the Swedish economy through their respective

33 effects on aggregate demand. In line with some of the previous literature, it appears as if the strongest spillover effect comes from the ECB, which is not surprising considering the stronger integration between the Swedish and Euro-area economy. While the U.S and ECB spillovers that propagate through the financial channels are similar, it appears as if the U.S spillovers operate more though the exchange rate channel.

ECB Monetary Contraction U.S Monetary Contraction

Figure 6.2.6: 4 10-year government bond

Figure 6.2.7: Stock market index

Figure 6.2.8: Corporate bond spreads

Figure 6.2.9: Term Spread (10 - 2 year government bond yield)

4 The left-hand column shows the median impulse responses to an ECB monetary contraction (blue) and the right-hand column the median impulse responses to a Federal Reserve monetary contraction (orange). The solid line shows the median impulse response to the respective central bank spillovers with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each monetary contraction is reported as a 1 percentage point monetary contraction. The change to the endogenous variables is presented in % on the Y axis. 34

6.3 Spillovers Effects from U.S Conventional Vs Unconventional Monetary Policy Shocks

In this section is I compare the spillovers of conventional and unconventional monetary policy as a large part of the sample period of interest is characterized by unconventional monetary policy. Many of the previous cited papers have found that while conventional spillovers have a strong effect on macroeconomic variables, unconventional monetary policy affects the exchange rates but for the real economy the effect is muted and less significant (e.g., Babecká Kucharčuková et al. 2016, Hajek and Horvath 2017). In this comparison, data from the U.S will be used since unconventional monetary policy did not start in the EU area until 2015 (Riksbank, 2015). Therefore, since the practice of U.S unconventional monetary policy started during 2008, the use of U.S data provides more periods of unconventional monetary policy. The period of conventional policy is defined between the years 1999-2007, before the quantitative easing period following the Great Recession, while unconventional monetary policy is defined as the period between 2008-2019. Like previous comparisons, all monetary policy shock data has been purged from the information effect.

As can be seen from the graphs below, the estimates of conventional and unconventional monetary policy are in line with previous literature. The effect of unconventional spillovers strongly impacts the exchange rate in figure 6.3.3, causing the USD to appreciate considerably more compared to a conventional monetary contraction. Interestingly, figure 6.3.6 shows that the yield of 10-year government bonds also appears to be more strongly affected by an unconventional foreign monetary contraction, as was found by Albagil (2019) and Saskia et al (2018). The response of the bond yields following both a conventional and unconventional monetary contraction is however somewhat puzzling, since economic theory dictates that the yields should rise following a monetary contraction. Instead, the observed response is the opposite, where the yields instead decline. The puzzling result can be rationalized when considering the response of the Swedish short rate, where an unconventional monetary contraction in the U.S causes a large and significant reduction of the Swedish short rate. This large reduction can be argued to consequently contribute to a significant reduction in the yields of the 10-year government bond. Although less apparent, the same pattern can be observed in the case of a conventional tightening. Likewise, the same argument could be applied to rationalize the stronger appreciation of the USD/SEK exchange rate following an unconventional contraction.

The strong exchange rate appreciation after an unconventional tightening also causes Swedish exports to the U.S to increase in figure 6.3.2, however the effect is weaker and less significant in comparison to a conventional shock. The weaker response of Swedish exports, in addition to the strong appreciation of the USD, suggest that the reduction in foreign demand is stronger after an unconventional monetary contraction. The impact of a weaker SEK and slight increase in exports is however not strong enough to affect total industry production in figure 6.3.1, where the response is negligible over the full period. These findings are also more in line with the results obtained by Degasperi et al (2019). The weaker effect of unconventional monetary policy spillovers can also be observed for inflation in figure 6.3.5, similar to what was found by Babecká Kucharčuková et al. (2016) and Hajek and Horvath (2017). Lastly, the effect of a U.S unconventional contraction has a negligible effect on both the term spread and the stock market (see appendix 3A).

In summary, unconventional monetary policy appears to have a strong effect on the Swedish short rate, the exchange rate and the 10-year government bond yield, while the response of the term spread, and stock market index and macroeconomic variables are insignificant. These results would therefore suggest that the spillovers from conventional monetary policy spillovers propagate through the aggregate demand channel and to some extent the financial channel. In contrast, unconventional monetary policy mainly propagates through the exchange rate channel.

Conventional Unconventional Full timespan Figure 6.3.1: Production of Total Industry

Figure 6.3.2: Exports to the U.S

Figure 6.3.3: USD/SEK exchange rate

Figure 6.3.4: 5 Swedish Short Rate

Figure 6.3.5: Inflation (CPIF)

Figure 6.3.6: 10 Year Government Bond Yield

5 The left-hand column shows the median impulse responses to an US conventional monetary contraction (green), the middle column shows the median impulse response to an US unconventional monetary contraction (brown) and the right-hand column shows the median impulse responses to a US total monetary contraction (orange). The solid line shows the median impulse response to the respective central bank spillovers with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each shock is reported as a 1 percentage point monetary contraction. The change to the endogenous variables is presented in % on the Y axis. 37

7.1: Sensitivity Analysis

To analyze the robustness of the endogenous variable’s response, this section will compare the baseline ECB estimates with results obtained after aggregating the monetary policy shock series into quarterly frequency (figure 7.1). The data for the quarterly shock series have like the monthly shock series been obtained from the updated dataset of Jarocinski (2020), where the quarterly shock series have been constructed using the same method as the monthly ECB shock series. The choice to include a robustness test using a quarterly shock series is to observe if the results change when the constructed monetary policy shocks are aggregated into quarterly frequency. Moreover, the test also allows for inclusion of the variable GDP, which was not present in the previous section due to the lack of monthly data.

Figure 7.1: Quarterly ECB Monetary Policy Shocks

0,30 0,20 0,10 0,00 -0,10 -0,20

-0,30

2004 Q 2 Q 2004 1 Q 2011 4 Q 2017 1999 Q 1 Q 1999 4 Q 1999 3 Q 2000 2 Q 2001 1 Q 2002 4 Q 2002 3 Q 2003 1 Q 2005 4 Q 2005 3 Q 2006 2 Q 2007 1 Q 2008 4 Q 2008 3 Q 2009 2 Q 2010 4 Q 2011 3 Q 2012 2 Q 2013 1 Q 2014 4 Q 2014 3 Q 2015 2 Q 2016 1 Q 2017 3 Q 2018 2 Q 2019

To compare the respective effects, the quarterly shock series is included in the Local projections model with the same properties as in the baseline model. Therefore, the response horizon is of similar length, with 8 quarters for each endogenous variable. Likewise, the model also includes 2 lags for the dependent variable as well as the shock series. The endogenous variables are for the most part the same as in the baseline model, where quarterly data has been obtained instead of monthly data. However, with the use of quarterly data, the endogenous variables total industry production is now swapped for seasonally adjusted real GDP. Moreover, due to the lack of quarterly data for the variable CPIF, quarterly data for the variable CPI has been included instead.

The figures below suggests that the results are robust to the use of quarterly data, where most of the endogenous variables exhibit a similar response to a monthly/quarterly monetary policy shock. For example, the response of both the EUR/SEK exchange rate and term spread are next to identical. The other variables also follow similar patterns with close confidence intervals. 38

There are however some differences between the responses, especially for the Swedish short rate. Following a quarterly shock, the decline of the Swedish short rate in figure 7.1.3 is considerably stronger than the response after a monthly shock. The quarterly shock causes a decline of around 9 %, where a monthly shock contributes to a decline of about 6-7%. The explanation for the stronger quarterly response is however unknown.

Another interesting disparity can be found in the response of the total industry production/ GDP in figure 7.1.1, where the response of total industry production is much stronger compared to GDP. This result could be explained by the fact that Swedish industry production contains sectors of the economy that are more vulnerable to foreign monetary policy shocks. However, the similar response lends credibility to the use of total industrial production as a proxy for GDP.

The last major difference that can be observed is found in the results obtained for inflation. The response of the CPI to a quarterly ECB monetary contraction is both stronger and more significant compared to the baseline estimates. The response is also more in line with economic theory. However, like the comparison between GDP and industrial production, the variables CPI and CPIF cannot properly be compared since they are calculated differently. For the remaining figures not included in this section, see appendix 3B.

ECB Monthly Shock ECB Quarterly Shock Figure 7.1.1:6 Log Industrial Production/ Log GDP

Figure 7.1.2: EUR/SEK exchange rate

Figure 7.1.3: Swedish short rate

Figure 7.1.4: Inflation (CPI)

6 The left-hand column shows the median impulse responses to a monthly ECB monetary contraction (blue) and the right-hand column the median impulse responses to a quarterly ECB monetary contraction (purple). The solid line shows the median impulse response to the respective central bank spillovers with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each monetary contraction is reported as a 1 percentage point monetary contraction. The change to the endogenous variables is presented in % on the Y axis. 40

7.2 Robustness Test Using Different Construction Methods of US Monetary Policy Shocks

This papers monetary policy shocks have been obtained from Jarocinski (2020) for the ECB, and from Bu et al (2020) for the U.S Fed. The methods which these two papers use is however somewhat different and could therefore potentially yield different results. Since Jarocinski (2020) and Bu et al (2020) both have estimated monetary policy shocks for the U.S, the results from their respective methods can be compared. Hence, the last sensitivity analysis will compare the different estimated effect of the two methods using monthly U.S data.

As can be seen from the figures below, it would appear as if the different shock series yielded roughly the same result. The correlation between the two series is 0.48 and the response of all the variables included goes in the same direction and follows a similar pattern. What is striking however is the stronger and more significant effect observed for the Swedish exports, USD/SEK exchange rate, the Swedish short rate and 10-year government bond yield from the use of the Bu et al (2020) monetary policy shock series. In contrast, only the response of the stock market and industrial production appears to exhibit a stronger response to the Jarocinski (2020) shock series.

One major difference which can be observed however is the response of inflation. In the dataset from Bu et al (2020) the effect of a monetary contraction on inflation is both negligible and insignificant for the full period. On the contrary, the response of inflation is both contractionary and significant for most of the period in the case of Jarocinski (2020). However, the negative response of inflation is counterintuitive, considering the expansionary effect of the other macroeconomic variables. The difference in the estimates can likely be attributed partly to the impact of using sign restriction to purge the monetary policy shocks. While there are two types of sign restriction methods, this paper only considers the stricter version, where a monetary policy shock can either entirely be a monetary policy shock or an information shock, not both. The less restrictive approach assumes that a policy announcement can contain a combination of both monetary policy and information shocks. Because of this use of the stricter version, there could be a possibility of omitting some of the variation caused by monetary policy.

While the estimation results lend credibility to the previously observed results, they also raise an important question. Since the response of the endogenous variables is stronger when using the Bu et al (2020) dataset, there might be a possibility to get more significant results for the ECB by applying the same shock decomposition method on the ECB shocks. However, since

41 no such data currently exists, such a comparison is not feasible. For the remaining figures not included in this section, see appendix 3C.

The conclusion which can be drawn from both sensitivity analysis is that the baseline estimates appear robust to the use of differently constructed monetary policy shock series. This is of great importance as these results lends credibility to the shock decomposition as well as the response estimates. A sensitivity analysis which utilizes a different model such as the VAR would be ideal to analyze the robustness of the Local Projections model. However, this is outside the scope of this paper.

Bu et al (2020) Jarocinski (2020) Figure 7.2.17: Production of Total Industry

Figure 7.2.2: Swedish Log Exports to the U.S

Figure 7.2.3: USD/SEK Exchange rate

Figure 7.2.4: Swedish Short rate

Figure 7.2.5: CPIF

7 The left-hand column shows the median impulse response to a US monetary contraction constructed by Rogers et al (2020) (orange) and the right-hand column the median impulse response to a US monetary contraction constructed by Jarocinski (2020) (purple). The solid line shows the median impulse response to the respective central bank spillovers with the dark (light) shaded areas representing a one standard deviation (90%) confidence interval. Each monetary contraction is reported as a 1 percentage point monetary contraction. The change to the endogenous variables is presented in % on the Y axis. 43

8: Conclusion

This paper estimates the spillover effects of foreign monetary policy and central bank information on the Swedish economy during the period Jan 1999 - May 2019. The analysis relies on monetary policy and central bank information shock series for the ECB obtained from Jarocinski (2020), and U.S Fed monetary policy shocks obtained from Bu et al (2020). The shock series have been included in a Local Projection model a lá Jorda 2005 to estimate their respective effects on Swedish macroeconomic and financial variables. What can be observed from the results of the baseline regression and the robustness tests is that many of the variables respond significantly to ECB and U.S monetary policy/ information shocks. In addition, the second robustness test also shows that the different shock decomposition methods yield similar outcomes, lending credibility to the estimated results.

The main conclusion that can be drawn from my thesis is that foreign monetary policy spillovers from the ECB and U.S Fed have significant effects on the Swedish economy. Interestingly, the results also show that there is a large difference between the response of the variables following an ECB monetary tightening compared with a U.S Fed tightening. In line with previous research and economic theory, a monetary contraction from the ECB contributes to a reduction in Swedish industry production as well as other macroeconomic variables. In aggregate, the effects from the ECB spillovers are also stronger compared to a U.S tightening. This result is unsurprising, considering the closer economic integration between Sweden and the Euro area.

Despite the weaker effects of U.S Fed monetary policy, the response of the macroeconomic variables following a U.S tightening are, in contrast to what was found for the ECB and previous papers, expansionary. This result can partly be explained by the considerably stronger appreciation of the USD/SEK compared with the EUR/SEK. Another contributing factor might be the effect of foreign demand, where U.S demand for Swedish exports is not as negatively affected following a monetary contraction compared to German demand, and instead contributes to increased demand for Swedish exports following the strong appreciation of the USD/SEK exchange rate.

The response of the Swedish financial variables to a U.S Fed monetary policy shock are more in line with economic theory and are also of similar strength when compared to the response after an ECB monetary policy shock. This result could be attributed by some extent to the dominant role of the U.S dollar and U.S monetary policy determining the global financial cycle,

44 as was proposed by Rey (2013) and Bekaert et al (2013). However, while of similar strength, the respective impacts of ECB and U.S monetary policy to the Swedish financial variables are for the most part negligible. In conclusion, spillovers from the ECB and U.S Fed affect the Swedish economy through their respective effects on the aggregate demand channel, where the strongest spillover effect is observed following an ECB monetary contraction. While the U.S and ECB spillovers that propagate through the financial channels are for the most part insignificant, it appears as if the U.S spillovers propagate more through the exchange rate channel.

The second main finding of this paper is the crucial importance of information shocks and their effect on the endogenous variables. While monetary policy spillovers from the ECB have for the most part a negligible impact on the Swedish financial conditions, the ECB information effect that propagate though the financial conditions channel are stronger and more significant. In terms of the exchange rate and aggregate demand channel, the relationship is the reverse, where ECB monetary policy shocks exhibit the strongest and most significant impact. Therefore, as in Jarocinski (2020), the omission of the ECB information shocks contributes to biased results, where for example the response of inflation following a total monetary policy contraction is expansionary. When controlling for the information effect, however, the response becomes contractionary. Therefore, I show that the arguments of Jarocinski (2020), is valid also for the ECB monetary policy spillovers to Sweden.

The last finding that can be drawn from the observed results is that there exists a stark difference in impact between conventional and unconventional monetary policy spillovers. The impact of conventional monetary policy spillovers is stronger and more significant, especially for the macroeconomic variables. The effect of unconventional monetary policy spillovers strongly impacts the exchange rates, government bond yield and Swedish short-term rate, but is negligible and insignificant for the remaining variables. These findings are in line with previous literature that does not control for the information effect, which have found stronger spillover effects of conventional monetary policy. These results also resemble the findings of previous literature like Saskia et al (2018) and Albagil et al (2019) whose results suggests that unconventional monetary spillovers are particularly strong for longer maturity yields. These results would therefore suggest that the spillovers from conventional monetary policy spillovers propagate through the aggregate demand channel and to some extent the financial channel. In contrast, unconventional monetary policy mainly propagates through its effect on the exchange rate channel and to a lesser extent, the financial channel.

The results obtained in this study are, in my opinion, of significant academic value. To the best of my knowledge, there are no prior papers which have analyzed and compared the effects of monetary policy shocks from two large central banks on a small open economy while controlling for the information effect. The results also highlight the crucial importance for central banks and macroeconomic researchers to take the central bank information effect, as well as the different effects between conventional and unconventional monetary policy spillovers into account when studying the spillovers of foreign monetary policy.

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Appendix 1A: Construction of Exogenous ECB Monetary Policy Shocks

While many of the previous papers have used different methods to calculate exogenous monetary policy shocks, the unfolding of high-frequency financial market data has contributed to the emergence of new identification methods of monetary policy shocks (e.g. Kuttner, 2001). The method of Kuttner (2001) utilizes a High Frequency Identification method (HFI) to estimate the "surprise" in yields of markets interest rate derivatives which has the central bank short rate as an underlying asset. By observing the movements of the yields around short time windows on policy announcements days, the unexpected yield change can be viewed as an exogenous deviation from the expected central bank response to the economic outlook.

The time-window used by Jarocinski (2020) for observing the changes in the underlying derivatives around the central bank policy announcement is 30 minutes, 10 minutes before and 20 minutes after the announcement. The shocks for the ECB are identified as the change in the Eonia swaps' first principal component with remaining maturities between 1 month to 12 months. Through the inclusion of derivatives with maturities up to one year, the monetary policy shocks both capture present changes in policy rates as well as expected future interest rates. Thus, these shocks can capture unconventional monetary policy and forward guidance. Important to note is that the monetary policy shocks only contain a small share of the overall monetary policy stance, where the average monthly exogenous policy rate shock is around 1-2 basis points.

To purge the monetary policy shocks from the information effect, the "poor man's sign restriction" identification method of Jarocinski and Karadi (2020) is applied, where the ECB information shocks, and monetary policy shocks are identified through the observations of co- movements between the stock market prices and the central bank interest rate. If interest rates set by the ECB positively co-move with the stock market around the time of a policy announcement, this is identified as a “pure” central bank information shock. In contrast, if a negative co-movement is observed around policy announcements, this is identified as a “pure” monetary policy shock. Therefore, this approach assumes that the surprise in ECB interest rates is either entirely a central bank information shock or a monetary policy shock.

1B: Construction of Exogenous U.S Monetary Policy Shocks

The method of Bu et al (2020) estimates exogenous US monetary policy shocks by applying a two-step regression (Fama and MacBeth 1973). In the first step, the authors analyze the responsiveness of different interest rates derivatives during monetary policy announcements. For this step, they use a time-series regression to compare short-term and long-term interest rate derivatives. The authors show that their procedure eliminates the information effect if the short- term and the long-term derivatives are impacted differently by the central bank policy announcement. The same desired result is also achieved if the short derivates are affected by the policy announcement while the long derivatives are unaffected. To filter out any news that is unrelated to monetary policy, the authors employ a heteroskedasticity-based estimator (Rigobon, 2003) in combination with instrumental variables. In the second step, the authors estimate the outcome variables from the estimated responsiveness index that was constructed in the first step, for each time t.

1C: Data on Swedish Endogenous Variables

Figure 1 and 2 shows the monthly average exchange rate for USD and EUR to the Swedish Krona. An exchange rate increase coincides with the SEK's depreciation, as more SEK is needed for every USD or EUR. As shown by the graphs, the exchange rate has, on average, fluctuated between 8-11 krona per Euro and 6-10 krona per USD. Following the Euro area crisis, the SEK has continuously depreciated against both the USD and the Euro.

Figure 1: USD/SEK Exchange rate

12 10 8 6 4 2

2000-05-01 2017-01-01 1999-01-01 1999-09-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-01-01 2009-09-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2013-09-01 2014-05-01 2015-01-01 2015-09-01 2016-05-01 2017-09-01 2018-05-01 2019-01-01

USD/SEK Monthly avarge

Figure 2: EUR/SEK Exchange rate

12 10 8 6 4 2

2006-05-01 2009-01-01 2011-09-01 2014-05-01 1999-01-01 1999-09-01 2000-05-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2007-01-01 2007-09-01 2008-05-01 2009-09-01 2010-05-01 2011-01-01 2012-05-01 2013-01-01 2013-09-01 2015-01-01 2015-09-01 2016-05-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01

EUR/SEK Monthly avarage

Figure 3 and figure 4 shows the monthly Swedish short-term interest rate and term-spread (10- 2-year government bond yield) between 1999-2019. After the financial crisis of 2008, the short rate fell considerably and has continued to fall after the euro area crisis of 2012, hitting the ZLB in early 2015. From there, it remained in negative territory until December 2019 (not shown in the graph). In contrast to the Swedish short rate, the term spread increased considerably following the financial crisis and the increased cross country financial stress. It has however remained relatively stable around 1 % since 2011.

Figure 3: Swedish Short-Term Rate

5,00 4,00 3,00 2,00 1,00 0,00 -1,00

-2,00

1999-01-01 1999-09-01 2000-05-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-01-01 2009-09-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2013-09-01 2014-05-01 2015-01-01 2015-09-01 2016-05-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01

Swedish Short Rate

Figure 4: Term Spread

3,00 2,00 1,00 0,00

-1,00

1999-01-01 2009-01-01 2014-05-01 1999-09-01 2000-05-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-09-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2013-09-01 2015-01-01 2015-09-01 2016-05-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01

Term spread

Figure 5 shows the CPIF (consumer price index measured with fixed interest rates), which the executive board of the Riksbank has adopted as a formal target for monetary policy since September 2017 (Riksbank, 2017). As is shown by the graph, the CPIF fell below the Riksbank target of 2 % in 2011. Despite extensive monetary easing, partly shown by figure 7, the CPIF did not recover to its 2 % level until mid-2017.

Figure 5: Inflation (CPIF) 4,0 3,0 2,0 1,0

0,0

2000-05-01 2014-05-01 1999-01-01 1999-09-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-01-01 2009-09-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2013-09-01 2015-01-01 2015-09-01 2016-05-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01

CPIF

Figure 6 shows the log of the SIX Portfolio index divided by inflation (CPIF). These calculation has been carried out to construct a real measure of the Swedish stock market index. The SIX Portfolio index measures the average return, including dividends of over 300 companies listed on the Stockholm Stock exchange.

Figure 6: SIX Return Portfolio Index

10 8 6 4 2

LN SIX Portfolio Return

Figure 7 shows the yield for the Swedish 10-year government bond. Following the expansionary monetary policy conducted by the Swedish central bank trough low/negative short-term rates and quantitative easing in particular, the yield has continuously fallen since 2001/2002, hitting the ZLB in 2016 and 2019. Between 2015 and 2019, the Swedish central bank bought an estimated 300 billion SEK worth of government bonds, contributing to a strong increase in bond price and consequently a large reduction in bond yields (Andersson and Jonung, 2020).

Figure 7: 10-Year Government Bond Yield 8,00 6,00 4,00 2,00

0,00

2007-01-01 2010-05-01 2013-09-01 2017-01-01 1999-01-01 1999-09-01 2000-05-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2006-05-01 2007-09-01 2008-05-01 2009-01-01 2009-09-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2014-05-01 2015-01-01 2015-09-01 2016-05-01 2017-09-01 2018-05-01 2019-01-01

Longterm GOV bond Y 10 year

Figure 8 shows the log of the seasonally adjusted Swedish total industry production (TIP). Since GDP is published quarterly, the TIP is used as a proxy for Swedish GDP. The TIP's is an acceptable proxy as it has previously been extensively used as a monthly indicator for assessing current and short-term GDP outlooks (OECD, 2012). It has also been used as a proxy for GDP in previous literature (eg Degasperi et al, 2019)

Figure 8: Production of Total Industry 4,90 4,80 4,70 4,60 4,50 4,40

4,30

2005-01-01 2009-09-01 1999-01-01 1999-09-01 2000-05-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2004-05-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-01-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2013-09-01 2014-05-01 2015-01-01 2015-09-01 2016-05-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01

LN Production of Total Industry

Figure 9 shows the log of Swedish total export to the US and Germany. Germany is used here as a proxy for the E.U since no data exist for Swedish exists for this region. Germany is an acceptable proxy as it is one of the largest importer of Swedish goods in the E.U (WITS, 2021).

Figure 9: Exports to Germany and The US

10 9,5 9 8,5 8

7,5

1999-09-01 2002-05-01 2013-09-01 2016-05-01 1999-01-01 2000-05-01 2001-01-01 2001-09-01 2003-01-01 2003-09-01 2004-05-01 2005-01-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-01-01 2009-09-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2014-05-01 2015-01-01 2015-09-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01 Log exports US Log exports Germany

Lastly, figure 10 shows the Option adjusted investment grade Swedish Corporate Bond spread.

Figure 10: Swedish Corporate Bond Spread

5 4 3 2 1

2004-05-01 1999-09-01 2000-05-01 2001-01-01 2001-09-01 2002-05-01 2003-01-01 2003-09-01 2005-01-01 2005-09-01 2006-05-01 2007-01-01 2007-09-01 2008-05-01 2009-01-01 2009-09-01 2010-05-01 2011-01-01 2011-09-01 2012-05-01 2013-01-01 2013-09-01 2014-05-01 2015-01-01 2015-09-01 2016-05-01 2017-01-01 2017-09-01 2018-05-01 2019-01-01 1999-01-01

2: Robustness Test for Local Projections Models Using Different Lag Lengths

ECB Monetary Contraction (6 Lags) ECB Monetary Contraction (12 Lags)

Figure 5.1: Log Industrial Production

Figure 5.2: Swedish Short Rate

Figure 5.3: Inflation (CPIF)

Figure 5.4: 10-year Government Bond Yield

Figure 5.5: 10-year Government Bond Yield

3A: Comparison Between U.S Conventional and Unconventional Monetary Policy Shocks Conventional Unconventional Full timespan Figure 6.3.7: Term Spread

Figure 6.3.8: Stock Market Index

Figure 6.3.8: Corporate Bond Spread

3B: Robustness Test Using Quarterly Monetary Policy Shocks ECB Monthly Shock ECB Quarterly Shock

Figure 7.1.5: Term Spread (10 – 2- year government bond yield)

Figure 7.1.6 Log exports to Germany

Figure 7.1.7: 10-year government bond

Figure 7.1.8: Corporate Bond Spread

Figure 7.1.9: Stock Market Index

3C: Robustness Test Using Different Methods to Construct U.S Monetary Policy Shocks

Bu et al (2020) Jarocinski (2020)

Figure 7.1.6: 10-year Government Bond Yield

Figure 7.1.7: Stock Market Index

Figure 7.1.8 Corporate Bond spread

Figure 7.1.9 Term Spread