QUANTITATIVE EASING AND STOCK PRICES - DIVA PORTAL

Quantitative Easing
and Stock Prices

An analysis of the impact of US Quantitative Easing on
Chinese Stock Prices

 BACHELOR
 THESIS WITHIN: Economics
 NUMBER OF CREDITS: 15 ECTS
 PROGRAMME OF STUDY: International Economics
 AUTHOR: Yuxiang Cao/Edvin Hygrell
 JÖNKÖPING 5/2022

Bachelor Thesis Project in Business Economics
Title: Quantitative Easing and Stock Prices
Authors: Yuxiang Cao/Edvin Hygrell
Tutor: Helena Nilsson
Date: 2022-06-13

Key terms: Quantitative Easing, Stock Prices, MFD Model, VAR Model.

Abstract

Following the financial crisis of 2008, the United States (US) Central Banks were placed in an
unfamiliar circumstance. The interest rates were nearly zero, implying that most conventional
monetary policies would be ineffective in enhancing the economy. As only a few alternatives
were available, Central Banks chose to implement Quantitative Easing (QE), a type of
unconventional monetary policy representing large-scale asset purchases, also called LSAP´s.
This monetary policy has the intention of increasing liquidity in the economy, thus increasing
money supply and boosting the economy.

The purpose of this paper is to analyse the impact of changes of US Money Supply on stock
prices in China. The size of the money supply in the US directly corresponds to the application
of Quantitative Easing and is thus utilized as the measurement of Quantitative Easing. The stock
index used in this paper is the Shanghai SSE Composite Index, which is composed of all stocks
traded in the Shanghai Market. To assess the correlation between US QE and stock prices
in China, a Vector Autoregression Model was performed. Findings reflect the positive and
statistically significant correlation between US money supply and Chinese stock prices.

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Table of Contents

1. Introduction .......................................................................... 1
1.1 Background ............................................................................................. 3
1.2 Purpose ................................................................................................... 5

2. Literature Review .................................................................. 5

3. Theoretical Framework ......................................................... 6
3.1 Theoretical Analysis ................................................................................. 6

4. Hypothesis ........................................................................... 10

5. Empirical research............................................................... 11
5.1 Methodology.......................................................................................... 11
5.2 Data selection ........................................................................................ 11
5.3 VAR Model ........................................................................................... 12

6. Results................................................................................. 13
6.1 Stationarity ............................................................................................ 13
6.2 Inverse Roots of AR Characteristic Polynomial ......................................... 13
6.3 Granger Causality Test ............................................................................ 14
6.4 Impulse Response Analysis ..................................................................... 16
6.5 Variance Decomposition ......................................................................... 17

7. Discussion ........................................................................... 18

8. Conclusion........................................................................... 20

Reference list .................................................................................. 23

Appendix ........................................................................................ 27

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1. Introduction

The 2008 financial crisis arose due to housing price bubble brought about by record low-
interest rates and lending standards. Millions of people obtained loans in order to purchase
homes, and to keep up with this, the banks sold mortgages on the secondary market, which
subsequently allowed them to grant further mortgages. The financial establishments,
which represent the major purchaser of these mortgages, collected them in instalments
and resold them to investors. Another name for these tranches is mortgage-backed
securities. When the mortgages started to default, it eventually became obvious to holders
of mortgage-backed securities that the papers they held had no value (Hellwig, 2008).

The financial crisis forced the US Central Banks to take unconventional measures in order
to avoid a liquidity trap, a risk that exists when interest rates approach the Zero Lower
Bound. In response to the crisis, the Federal Reserve Bank committed to large-scale asset
purchases in order to stimulate the economy (Gerlach & Lewis, 2013). This form of the
unconventional method is equally referred to as Quantitative Easing (QE) and is
interchangeable in this paper with increasing money supply. From the open market, the
Central Bank buys long-term securities and subsequently injects new money into the
economy. It aims to ensure the stimulation of the economy through the promotion of
investment and credit.

The vast increase in money supply as a response to the financial crisis brought about
declining interest rates and depreciation of the United States dollar (USD) and caused
holders of assets denominated in USD to experience a reduction in income. Since the
USD denotes the primary currency used for international settlements and international
reserves, it becomes essential and there are a considerable number of USD holders around
the world, ranging from governments to financial institutions. Following the gradual
depreciation of the USD, a considerable proportion of these holders opted to convert their
USD into bullish currencies, thereby subjecting the USD to a situation referred to as “Hot
money” which signifies a currency that frequently oscillates between financial markets,
typically from markets with low-interest rates to markets with high-interest rates (Chari
& Kehoe, 1997). This tends to cause an asset pricing and real estate bubble.

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From the perspective of economic globalization, the primary purpose for selecting China
over the United States is because China represented the world's third-largest economy as
of 2008 and occupied the first rank position in emerging markets (World Bank, 2020).
Moreover, among developing countries, China is also the largest holder of US Treasuries
in 2018 making the link between China and the US stronger in comparison to other
emerging economies (Economic Commission for Latin America and the Caribbean,
2019). China's closer economic relations with various countries globally following its
accession to the World Trade Organization have made it a prime choice for international
capital seeking investment opportunities.

The period 2003-2014 will be assessed. This period will be divided into two sub-periods,
the first from 2003 to 2008, and the other from 2008 to 2014. The period between 2008-
2014 is selected in this study to ensure that the effects of quantitative easing are isolated
as much as possible, since the implementation of the first round of quantitative easing,
referred to as QE1 was executed in November 2008 and the concluding round, also called
QE3, ended in October 2014. The money supply in the United States during those years
will be analyzed, in order to determine whether it directly impact Chinese stock prices
during those years.

Furthermore, the sub-period 2003-2008 will also be analyzed. During this period, no
quantitative easing was implemented in the US. The start year is selected so that the
potential effects of the dotcom bubble in the early years of the 2000’s do not affect our
results. By comparing the two periods, the goal is to provide a more intuitive response to
whether quantitative easing in the US had an impact on the Chinese equity market. We
aim to determine the impacts of QE by analyzing high-frequency data, where large spikes
in volumes of trade or returns are identified and could be attributed to increases in US
QE. This will assist in establishing a relationship between the changes in monetary policy
and stock prices.

Previous research has been analyzing the impact of quantitative easing on various
economic and financial variables, but these have tended to focus on the impact on the US
or European countries. The novelty of this study is that it concentrates on the impact on
stock market prices in China, which have not been analyzed to the same extent prior to
this research. The study examines the impact of the implementation of quantitative easing

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monetary policy on China's stock price index, and the analysis reveals the pathways
through which the implementation of quantitative easing monetary policy has affected
China's stock market, which presents a theoretical basis for regulators to respond to
quantitative easing monetary policy in the United States and promotes the stable
development of capital markets in China, as well as in developing countries, with
important practical implications.

The variables of interest are US M2 (United States money supply), China M2(Money
supply), SSE Composite Index (also known as Shanghai Composite Index, SCI), S&P500
and US Federal Funds Rate. US money supply M2 and the Chinese money supply M2
represents the broad money supply, commercial banks repurchase agreements, etc. and
contains the widest range of data compared to M1 (Fred, 2022). SCI is one of the largest
stocks indices in China, and contains all stocks traded in the Shanghai market (Google
Finance, 2022). S&P 500 is an American stock index containing 500 of the leading US
publicly traded companies (Encyclopedia Britannica, 2022). The US Federal Funds Rate
is the interest rate in the U.S. interbank lending market refers to the target interest rate
that is decided by the Federal Open Market Committee (Fred, 2022). In this paper, data
from the first to the last quantitative easing policy in the US, i.e., monthly data from
November 2008 to October 2014, are selected for the empirical study.

The first part will introduce the background of the 2008 financial crisis. The second part
denotes the theoretical framework, in which we mainly utilize the Mundell-Fleming-
Dornbusch Model (MFD). The third part is the empirical part. Data analysis processes
and analyzes the data through the application of vector Autoregression (VAR) and the
result is assessed in the final section. Further, the paper offers suggestions on how to
respond to the U.S. quantitative easing policy, as well as suggestions for future research.

1.1 Background

The need for QE generally implies a sustained economic downturn when marketing
interest rates are near zero and cannot be lowered any further. Hence the Central Bank is
unable to enhance the economy by adjusting interest rates (Joyce et al., 2012). To avoid
a recession, the central bank uses QE to purchase medium and long-term government
bonds from the market, thus expanding the money supply and stimulating the real
economy. The main difference QE and conventional monetary policy, it is the fact that

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conventional monetary policy is generally applied for boosting the economy through the
adjustment of interest rates. Hence, in the case of a chronic recession, for example, when
the nominal interest rates drop to a near zero level, conventional monetary policy fails,
and special methods are required for reviving the economy. For instance quantitative
easing, direct purchases of medium and long-term government bonds by the central bank
(Learning, 2022).

Japan was the first to utilize quantitative easing due to the continued weakness of the
Japanese economy prior to 2001 and the turmoil in the capital markets, the Japanese
government implemented an expansionary fiscal policy and lowered interest rates,
however, since nominal interest rates were reduced to zero, the Japanese economy still
failed to recover. Therefore, Japan resorted to quantitative easing in 2001 in the hope of
gaining ground from an economic perspective, to counter deflation and to stimulate
economic growth (Wieland, 2009). In response to the 2008 financial crisis, the United
States adopted Japan's quantitative easing policy in the hope that by doing the same, it
could escape the impacts of the financial crisis on the capital markets.

Quantitative easing denotes a relatively aggressive monetary policy that assist in easing
tight credit conditions and plays a crucial role in the country's economic recovery. By
enhancing the supply of base money and flooding global markets with liquidity, the
United States equally sowed the seeds of global inflation. Thus, the quantitative easing
monetary policy of the United States has pros and cons (Hausken & Ncube, 2013).

Following the 2008 subprime mortgage catastrophe, three quantitative easing measures
were applied by the United States. The implementation of the first quantitative easing
measure occurred on November 25, 2008 (Yardeni Research, 2020). During the period
the Federal Reserve launched its first round of quantitative easing and made its intention
known to purchase real estate-related debt directly from government banks and
mortgages-backed securities in which both the banks alongside the National Mortgage
Association of the federal government-backed up. Around April 2010, when the first
round of quantitative easing ended, the Fed had brought approximately $100 billion of
direct debit. It cut the federal funds rate to a record low of 0.08% in late 2008
(Macrotrends, 2022).

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1.2 Purpose

The purpose of this paper is to research the impact of the Federal Reserve's quantitative
easing policy on stock prices in China to increase understanding of the impact of one
country's use of quantitative easing on another country, in addition to providing some
policy recommendations on how to respond to another country's use of quantitative
easing.

2. Literature Review

A strong relation exists between changes in the money supply and the volatility of stock
prices, with world capital markets becoming increasingly connected. As the US dollar is
considered the 'world currency', changes in US policy tend to have a marked impact on
capital markets. Stock markets, as an essential component of capital markets, can also be
greatly affected.

The first piece of previous literature that is relevant for this paper is Bernanke & Kuttner
(2005). In the paper, they analyse how unexpected changes in monetary policies affect
stock prices, with the intention of comprehending the economic sources of the reaction
of the stock markets to changes in monetary policies. Similarly to this paper, they adopted
a vector autoregression (VAR) model and subsequently realized the outcome that a 1%
increase in broad equity indices is brought about by a 0.25% increase in federal funds
interest. Furthermore, they observed that for a considerable part of the stock prices
response, the impacts of monetary policy actions that are unanticipated on expected
returns have a dominant effect. Another similar study was carried out by Balatti et al.
(2017), which empirically examines the effectiveness of Quantitative easing as a tool for
recovery following the financial crisis. The data used spanned from 1982 until 2014 for
the UK, and 1971 to 2015 for the US. They conducted a six-variable VAR model,
inclusive of the following variables: output, inflation, QE amounts, equity prices, stock
market volatility and liquidity. The major discovery made by this article was that the
significant impact of exogenous shocks in QE is only reflected on financial variables.
Additionally, they observed that stock market reactions appear to reflect a "V" shape,
suggesting there will be a drastic drop in equity prices following a QE announcement, but
subsequently recover their losses and increase above their initial levels. Moreover, Koijen

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et al. (2016), conducted a corresponding study, but focused on more recent application of
QE, namely when utilized by the European Central Bank (ECB), as a response to the low
inflation rates leading up to 2015, and its subsequent impact on asset prices in countries
within Europe. In early 2015, the ECB announced a QE program with the goal of
enhancing inflation to a rate close to 2%. The study utilizes a micro-level data set from
Eurosystem, inclusive of data on security level portfolio holdings of the main investor
sectors of various European countries, in combination with the QE of the ECB, in order
to answer the question of how QE affects asset prices.

Among the previous literature, the work of Bhattarai et al. (2020) could perhaps denote
the most relevant piece of literature. The paper examines the impacts of US QE on the
markets of developing economies in the wake of the financial crisis, including the
following countries: Chile, Colombia, Brazil, India, Indonesia, Malaysia, Mexico, Peru,
South Africa, South Korea, Taiwan, Thailand, as well as Turkey. They started by
conducting a VAR model for estimating how a 1 deviation shock in US QE impacts US
variables, then utilize the identified response for deducing the spillover effects on the
emerging market economies. They found that an increase in US QE led to an appreciation
of the emerging market economies currencies, as well as a significant stock price increase.

The previous pieces of literature discussed in this section provides empirical support for
a positive correlation between QE and stock prices. Nevertheless, they concentrate mainly
on the US, the UK, and European countries, with the exception of Bhattarai et al (2020)
which examines the impacts on emerging economies. Regarding the impact of US QE on
Chinese stock prices, few studies have been conducted and it is this aspect that this article
intends to contribute to previous research works by providing an assessment of how
Chinese stock prices are impacted by the US QE.

3. Theoretical Framework

3.1 Theoretical Analysis

This chapter aims to evaluate the international transmission mechanism of monetary
policy and expounds on the Mundell-Fleming-Dornbusch (MFD) model. On the basis of
this, the paper expounds the ways that the QE used by the United States impacted the
Chinese stock market over a specific time.

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The MFD model can be used to analyse the impact of the implementation of
macroeconomic policies in one country on another (Barbosa, 2018). The analysis of our
paper is focused on the effects of monetary policies. For the analysis, it is categorized
into two major frameworks. The first concerns a floating exchange rate regime, in which
the implementation of an accommodative monetary policy in the United States is
supposed to benefit residents, but with the drawback of bringing about a decline in
China’s incomes. As for the second, it is under an exchange rate regime that is fixed.
Assuming an accommodative monetary policy is applied by the US, it will invariably
enhance the output and lower interest rates in both countries. IS represents that the
equilibrium condition of the product market IS comprised of the sum of consumption,
exchange rate, government spending and net exports. LM stands for equilibrium in the
money market.

Figure 1 International transmission of monetary policy under a floating exchange rate

 Table 1: Variables of the MFD model
 IS C (Y – T) + I(r*) + G + NX(e)
 LM L (r*, Y)
 Y National income
 I Exchange rate
 r Domestic rate of interest
 r* World rate of interest
 C Real consumption

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NX Real net exports
 G Real government spending
 L Real money demand
 T Real taxes

Source: Adapted from Frenkel & Razin (1987)

Figure 1 depicts the impact of a change in monetary policy in one country on the
economies of two countries under a floating exchange rate regime. During the initial state,
the interest rates of the two countries are equal, 0 = 1 =0, and the two countries attain
equilibrium at points A and B. After the United States utilizes expansionary monetary
policy, the money supply increases, and the LM curve shifts to the right to 1 , as
depicted on the left. Here, US interest rates will drop and there will be an increase in the
market liquidity, which will result in an increase in US production, which will move the
US equilibrium point to the right from point A to the new point at point B following the
implementation of the expansionary monetary policy in the United States. If the output
of the United States increases, the national income increases, and the propensity to import
in the United States remains static. The United States will then increase the number of
imports, which will result in an increase in China’s exports well as increase in China
national income. The equilibrium point of China will move from point D to point E, which
will result in an increase in the China’s interest rate and output since the United States
implements an expansionary monetary policy. Nevertheless, points B and E are not
equilibrium points, because under the floating exchange rate system, the interest rate in
the US is lower compared to China, capital will transfer from the low interest rate in the
US to the high interest rate in China to gain profits. Capital outflow from the United States
leads to short-term balance of payments deficit, thus resulting in a certain depreciation of
the dollar, which will improve the competitiveness of American products and promote
the increase of American exports, while the competitiveness of China’s products will
decline and China's exports will decrease. This causes the IS curve of the United States
to deviate to IS1 and the 1∗ curve of China to shift to 2∗ . The equilibrium points for the
US and China are points C and F, respectively. Thus, under the floating exchange rate,
the United States implements an expansionary monetary policy, which enhances the
money supply, increases national income, and lowers interest rates. As readily observed,

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for the US national income to increase, it will generally result in a decrease in the national
income of another nation.

 Figure 2 International transmission of monetary policy under a fixed exchange rate.

Figure 2 denotes the transmission mechanism of monetary policy between internationals
in a fixed exchange rate regime. The analysis is similar to that of a floating exchange rate
regime. The moment the United States implements an expansionary monetary policy, the
LM of the United States shifts to the right to 1 , causing US production to increase and
interest rates to drop, after which point B is called the equilibrium point. The increase in
national income is enhanced by the increase in national output, stimulating imports. As
China’s exports increase, the foreign IS* curve shifts to the right to 1∗ , and there is an
increase in China's interest rate and output. Under the premise of a fixed exchange rate,
the United States interest rate is lower compared to China's interest rate. A significant
amount of capital will flow abroad, thereby causing the United States currency to
depreciate. The central bank will maintain a tight monetary policy with the aim of
reducing the money supply to ensure that the US currency exchange rate remains
unfavorable and 1 will be moved to the left of 2 , subsequently bringing about a
reduction in the money supply. For the purpose of maintaining the exchange rate stability
owing to the considerable proportion of capital inflow, the government of China will
ensure money supply is increased China will increase, and thus the ∗ curve will shift
to the right towards 1∗ . At this point, the two economies attain a new equilibrium at
points C and F. At this point, the interest rates in both countries drop, but output increases.
Under a fixed exchange rate regime, an accommodative monetary policy in one country

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will ultimately result in an increase in output and a decrease in interest rates in both
countries (Boughton, 2002).

The Mundell-Fleming-Dornbusch model is a macro research method, which is suitable at
analysing the correlation between macroeconomic policies and exchange rates. The
quantitative easing monetary policy of the United States represents a policy measure
implemented by the Federal Reserve to save the American economy. Moreover, the
position of the largest economy in the world is occupied by the United States and the
dollar denotes the main globally accepted currency for international trade. The
implementation of this policy will inevitably affect the exchange rate and therefore have
a significant impact on the global economy. Therefore, the theoretical analysis of the
international transmission mechanism of monetary policy in the United States
demonstrates that the implementation of quantitative easing monetary policy in the
United States may affect the stock prices in China.

The MFD model has for many years been at the forefront of analysis of policy making,
and rightfully so. However, while it captures turning points in monetary policy extremely
well, an underlying issue with the model that should not be overlooked is the fact that the
model fails to capture exchange rate swings caused by factors other than policy making.
(Rogoff, 2002). The implications of this are difficult to grasp, as drastic changes in
exchange rates can come from many different factors. In the case of the financial crisis
of 2008, it is likely that there would be factors outside of monetary policies that could
play a role in affecting exchange rates that the MFD model would then fail to capture.

4. Hypothesis

Based on the previous literature analyzing the impacts of QE on stock prices, a hypothesis
can be made on the impacts of US QE on stock prices in China. The previous works were
unanimous in the fact that QE does have an impact on stock prices, and in addition, that
it impacted the stock prices positively. Hence, the hypothesis that we can draw are the
following:
H1: US QE has an impact on stock prices in China
H2: Increases in M2 of the US through QE has a positive relationship with Chinese stock
prices.

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5. Empirical research

5.1 Methodology

In order to determine the extent to which the application of Quantitative Easing influences
stock prices, the statistical VAR model will be implemented. VAR denotes a multivariate
forecasting algorithm that measures how two or more time-series influence each other.
This is accomplished through modeling each time series as a function of past values. VAR
is bidirectional, meaning it measures how the two variables influence each other rather
than the effect of one variable on the other, which distinguish it from other autoregression
models, which are usually unidirectional (Juselius, 2009).

The empirical research methodology is based on a quantitative analysis of the extent to
which quantitative easing of US monetary policy variables impacts Chinese stock prices
through the construction of a VAR model with impulse response function analysis.
Impulse response analysis is mainly set to describe changes in one variable as it affects
other variables. The variance decomposition is subsequently utilized. Variance
decomposition in the VAR is an analysis of the contribution of structural shocks
impacting the endogenous variables. Financial variables are often time series data and if
the time series are not smoothed, a "pseudo-regression" may occur when building a VAR
model, i.e. no cointegration relationship exists between the smoothed time series. Thus,
the data should be assessed for smoothness and after performing the smoothness test, a
VAR model can be built for causality testing and impulse response analysis.

5.2 Data Selection

Since the focus of this paper is to determine whether QE in the United States has had an
impact on stock prices in China, in this study, data for each month from November 2008
to October 2014 were selected. Monthly data is selected to provide a more vivid picture
of data changes and it was the first QE introduced in the US since November 2008 to deal
with the financial crisis (Blinder, 2010). The following variables are selected namely: the
money supply in the US, the money supply M2 in China, the closing price of the S&P
500, the closing price of the Shanghai Composite Index (SCI), and the US Federal Funds
Rate. The M2 of the US and China, as well as the Federal Funds rate, is extracted from

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the St. Louis Fed economic database, and the data on the stock prices for the S&P 500 as
well as the SCI is extracted from Yahoo Finance.

 Table 2: Variables used
 Variable Abbreviation Explanation
 US Money Supply M2 US A measure of US
 money stock.
 China Money Supply M2 CN A measure of China´s
 money stock.
 SSE Composite Index SCI One of the largest stock
 indices in China.
 Standard & Poor’s 500 S&P 500 One of the largest stock
 indices in the US.
 Federal Funds Rate FED Rate Interest rates in the U.S.
 interbank market.

5.3 VAR Model

The VAR model was initially presented by Christopher Sims in 1980 and represents a multivariate
forecasting algorithm that is applicable when multiple time series influence each other. This is essential for
this article because our goal is to determine whether the money supply has affected the stock market over
a period of time. The autoregressive element comes from the fact that each individual time series is
modelled based on previous values, therefore the essence of an optimal lag length.
A typical VAR model can be mathematically presented using the following formula:

 1. = + 2 −2 + … + − + 
Here, α is the intercept, 2 till p are coefficients of the lags of Y till order p, and is
the error term.

If there were to have two-time series at a given time t, for example, 1 and 2 , the VAR
model would use the past values from Y1 as well as Y2 in order to calculate 1 , and vice
versa for the calculation of 2 (Juselius, 2009).

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6. Results

6.1 Stationarity

For the purpose of verifying the smoothness of the time series variables we utilize a ADF
unit root test. When the ADF test is performed on the time series we can conclude that
the time series of all the data is rejecting the null hypothesis at the first difference level at
the 1%, 5% and 10% confidence intervals. Hence, none of the five time series have unit
roots and therefore it could be proven that all the data is smooth and there is no risk of
spurious regression. (Dickey & Fuller, 1979). Granger causality tests can therefore be
performed on this data.

 Table 3: ADF-test results
 1% level 5% level 10% level P-value Result
 FED Rate -3.527 -2.904 -2.589 0.0000 Stable
 M2 CN -3.529 -2.904 -2.589 0.0000 Stable
 M2 US -3.527 -2.904 -2.589 0.0000 Stable
 S&P 500 -3.527 -2.904 -2.589 0.0000 Stable
 SCI -3.527 -2.904 -2.589 0.0000 Stable

6.2 Inverse Roots of AR Characteristic Polynomial

When performing the inverse roots of an AR polynomial, it is readily observable that all
roots of the VAR model lie within the unit circle. Thus, the data is stable. This is essential
because assuming the VAR is not stable, the impulse response test (subsequently
conducted in this section) will be invalid.

 Figure 3 Inverse Roots of AR Characteristic Polynomial

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6.3 Granger Causality Test

Subsequently, a Granger causality test is performed to consider whether and which of our
time series are relevant in predicting the future values of other time series. A probability
below 0.05 indicates that Granger causality exists and the hypothesis that "X does not
Granger cause Y" could be rejected.

The results of using the Granger causality test are depicted in Table 4, demonstrating the
correlation between the US stock market and the China’s stock market, the US money
supply and the Federal Funds Rate. From the graph it is observed that at the 5% level of
significance all p-values are greater than 0.05 therefore it can be surmised that significant
correlation exists between the US stock market, in the years 2008 to 2014.

 Table 4: Granger Causality test, period 2008-2014: M2 CN, FED Rate, M2 US, S&P
 500, SCI.
 Null Hypothesis: Observation F-Statistic Probability

 M2 CN does not Granger Cause FED Rate 68 1.092 0.369
 Fed Rate does not Granger Cause M2 CN 0.640 0.636
 M2 US does not Granger Cause FED Rate 68 1.055 0.387
 FED Rate does not Granger Cause M2 US 0.758 0.557
 S&P 500 does not Granger Cause FED Rate 68 1.461 0.226
 FED Rate does not Granger Cause S&P 500 0.275 0.893
 SCI does not Granger Cause FED Rate 68 0.749 0.562
 FED Rate does not Granger Cause SCI 2.948 0.027

Table 4 depicts the impact of the federal funds rate on other factors and the impact of
other factors on the federal funds rate. The federal funds rate as depicted in the graph has
a p-value of 0.027 < 0.05 at the 5% significance level against SCI, thus the original
hypothesis that the federal funds rate does not Granger cause SCI in China is rejected and
thus it could be stated that the federal funds rate has an impact on the China’s stock
market. For the other factors the effects can be seen from the p-values which are all greater
than 0.05 and therefore all accept the original hypothesis.

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Table 5: Granger Causality test, period 2008-2014: M2 US, M2 CN, S&P 500, SCI.
 Null Hypothesis: Observation F-Statistic Probability

 M2 US does not Granger Cause M2 CN 68 1.860 0.129
 M2 CN does not Granger Cause M2 US 1.289 0.285
 S&P 500 does not Granger Cause M2 CN 68 0.475 0.754
 M2 CN does not Granger Cause S&P 500 1.506 0.212
 SCI does not Granger Cause M2 CN 68 1.361 0.258
 M2 CN does not Granger Cause SCI 3.372 0.015

Table 5 depicts the correlation between China's money supply and other variables. From
the table, it can be observed that at a 5% confidence interval, the Granger test for China's
money supply and China's SCI is 0.015

subsequently surmise that other factors cannot influence the US money supply. The
Granger test for the SCI and SP500 is equally greater than 0.05 and thus the SCI and
SP500 are not significantly related to each other.

 Table 7: Granger Causality test, period 2003-2008: M2 US, SCI.
 Null Hypothesis: Observations F-Statistic Probability

 SCI does not Granger Cause M2 US 57 0.394 0.812
 M2 US does not Granger Cause SCI 1.306 0.281

Furthermore, when conducting the Granger causality test on the control period between
2003 and 2008, it is observed that the value for “M2 US does not Cause SCI” is
significantly greater than the confidence interval of 5%. This implies that the null
hypothesis cannot be rejected, suggesting that it is not feasible for Granger's causality test
to conclude that there is a statistically significant relationship between the variables
during this time period.

6.4 Impulse Response Analysis

The first graph depicts that China's stock market is mainly subject to a large own shock
and that China's money supply, M2, has a positive shock to China's stock market in the
third period reaching a maximum in the second period. A negative shock is generated
after period 3. The impact of the US federal fund rate on China's stock market is positive
up to period 6. Second, federal fund rates have consistently impacted China's money
supply positively. This is brought about as a result of excessive inflationary pressures on
China, which in the subsequent period led to a negative shock.

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Figure: 4 Impulse Response Analysis

6.5 Variance Decomposition

 Table 8: Variance Decomposition of SCI, period 2008 - 2014
 Period S.E. S&P 500 SCI M2 US M2 CN FED Rate
 1 162.591 0.000 99.876 0.124 0.000 0.000
 2 202.715 1.683 94.472 2.859 0.000 0.987
 3 224.401 4.198 89.168 5.612 0.005 1.018
 4 236.640 5.736 85.835 7.437 0.036 0.956
 5 243.968 6.683 83.817 8.421 0.131 0.948
 6 248.871 7.385 82.406 8.949 0.271 0.980
 7 252.333 7.929 81.327 9.269 0.421 1.053
 8 254.777 8.329 80.496 9.484 0.565 1.125
 9 256.493 8.609 79.863 9.631 0.700 1.197
 10 257.702 8.786 79.379 9.733 0.825 1.266

The variance decomposition of the VAR model is used to analyse the extent to which
each structural shock contributes to changes in endogenous variables, further capturing
the extent to which one variable is influenced by several other variables (Lütkepohl,
2005). As can be observed from table 8, US money supply has the most significant impact

 17

on China's stock market prices (SCI) during the 10th period, accompanied by the S&P
500, and China's money supply was observed to have the least impact in this period.

 Table 9. Variance decomposition of SCI, period 2003 - 2008

 Period S.E. S&P 500 SCI M2 US M2 CN FED Rate
 1 275.737 17.235 82.765 0.000 0.000 0.000
 2 367.622 18.189 75.387 0.041 1.692 4.691
 3 467.359 25.455 66.579 0.086 1.077 6.803
 4 558.543 32.110 58.590 0.143 0.759 8.397
 5 651.792 38.690 51.214 0.168 0.611 9.317
 6 745.827 44.441 44.787 0.152 0.663 9.957
 7 841.882 49.327 39.198 0.122 0.928 10.424
 8 939.999 53.369 34.335 0.099 1.385 10.811
 9 1040.417 56.661 30.076 0.089 2.016 11.158
 10 1143.188 59.295 26.328 0.094 2.797 11.485

When comparing it to the variance decomposition of Chinese stock prices over the period
2003 to 2008, it is easy to see that there are certain significant differences. At period 1,
the largest influence on the SCI is the S&P 500, and other influential factors were 0. At
period 10, the U.S. M2 has the least influence, or the least explanatory power for changes
in the SCI. In this period, the S&P 500 has the greatest impact, as shown in Table 9.

7. Discussion

Through comparison, it can be seen that from 2008 to 2014, the quantitative easing policy
of the United States has a strong correlation with China's stock market, and the impact of
the federal funds rate on China's stock market also exceeds the impact of China's money
supply. According to the data from 2003 to 2008, during the period when quantitative
easing was not implemented in the United States, the American stock market had the
greatest impact on China's stock market, mainly because the decline or rise of the
American stock market would affect the international flow of capital, thus affecting
China's stock market.

 18

Figure 5 SCI Composite Index, 2008-2014; Source Yahoo.com, own calculations.

 Shanghai Composite Index
 4000

 3500

 3000

 2500
 Index

 2000

 1500

 1000

 500

 0
 01/02/2010

 01/05/2014
 01/11/2008
 01/02/2009
 01/05/2009
 01/08/2009
 01/11/2009

 01/05/2010
 01/08/2010
 01/11/2010
 01/02/2011
 01/05/2011
 01/08/2011
 01/11/2011
 01/02/2012
 01/05/2012
 01/08/2012
 01/11/2012
 01/02/2013
 01/05/2013
 01/08/2013
 01/11/2013
 01/02/2014

 01/08/2014
 Date

From the figure 5, during the period of QE, there is an observable rise of the China's stock
prices as the US conducts a round of QE, then there is an equally obvious drop as the
round ends. Hence, every time the United States conducts this type of unconventional
monetary policy, stock prices are promoted in the beginning, but begin to decline as the
round of QE comes to an end.

Regarding the second point, the issuance of dollars is the main tool of quantitative easing,
subsequently enhancing the number of funds available in capital markets following the
application of quantitative easing in the United States, as well as the reduction in the
federal funds rate. This has deepened the impact on the China's equity market.

The last point is China's money supply, which has influenced financial markets around
the world due to the US quantitative easing policy and the US dollar as the base currency
in the foreign exchange market. For China, to addressing the domestic impact of the US
quantitative easing policy, the central bank issued 4 trillion yuan to enhance domestic
demand (Wong, 2011). The massive issuance of base currencies in China has also had an
impact on the Chinese stock market. This fueled the advent of the China's stock market.

 19

Figure 6: Foreign direct investment of China, 2000-2020: Source World Bank, own
 calculations.

 Foreign direct investment of China
 350,000,000,000.00

 300,000,000,000.00

 250,000,000,000.00

 200,000,000,000.00
 US$

 150,000,000,000.00

 100,000,000,000.00

 50,000,000,000.00

 0.00
 2000

 2006

 2012

 2018
 2001
 2002
 2003
 2004
 2005

 2007
 2008
 2009
 2010
 2011

 2013
 2014
 2015
 2016
 2017

 2019
 2020
 Year

Figure 6 shows the changes in China's outbound investment in the last 20 years, as
previously mentioned in this paper, Chari & Kehoe (1997) proposed the concept of "hot
money", which shows that China's foreign investment rose significantly after 2009, when
the quantitative easing policy in the U.S. started to be implemented. After 2009, China
received a significant increase in foreign investment, when quantitative easing began in
the US. This confirms the conclusion of this paper that due to the impact of quantitative
easing in the US, a large amount of capital chose to leave the US in search of more
profitable investment opportunities, thus affecting the Chinese stock market. The US
government ceases the implementation of quantitative easing, it will result in an extension
of the US balance of payments deficit and massive capital outflows, posing a considerable
risk to the financial system. This is the reason every country should be prepared to address
sudden changes in international markets.

8. Conclusion

According to the comparative results the current study, the quantitative easing policy of
the United States has a positive impact on China's stock market, which is in line with
previous studies such as Bhattarai et al. (2020), which proved that US QE has a significant
positive effect on stock prices in emerging economies. Interestingly, the results from our
control period, 2003 to 2008, where QE had not yet been conducted, show that US money

 20

supply does not have a significant relationship to stock prices in China. This further
suggest that the main impacting variable is indeed QE. According to MFD theory, if the
quantitative easing monetary policy implemented by the US leads to a decrease in the
exchange rate of the US dollar, thus increasing exports, China needs to reduce the impact
on its exports by increasing the supply of money in China so that the exchange rate in
China also decreases.

The series of quantitative easing policies implemented by the US central bank for
stimulating the economy after the 2008 financial crisis had a considerable impact not only
on the US but likewise on the global financial system. Therefore, when the US decided
to start tapering its bond purchases in 2014, it marked the completion of quantitative
easing in the US (Board of Governors of the Federal Reserve System, 2013).

Furthermore, while QE proved to be an effective tool in stimulating the economy and
boosting stock prices, there are risks involved that should not be overlooked. For instance,
the timing of the end of quantitative easing is very important, as each time it has been
implemented in the US it has caused equity prices to fall after a period of time. If the US
were to abandon the use of quantitative easing too soon, it would slow down or hinder
the recovery of the world economy from the 2008 financial crisis. This is because
abandoning the use of quantitative easing would lead to a reduction in market liquidity
and an increase in market interest rates, which would affect the desire of individuals to
consume and invest. On the other hand, if the US government abandons the use of
quantitative easing too late, it may exacerbate global inflation.

Each country should formulate reasonable policies in advance to deal with the
quantitative easing of the United States, as the analysis results of the thesis mentioned the
quantitative easing policy of the United States has promoted the prosperity of the Chinese
stock market, but this is short-lived, so for the healthy development of the capital markets
of other countries put forward the following suggestions, the first point is that at this stage
the US dollar is the main currency of the capital market, the US government's massive
increase in dollar issuance will lead to a devaluation of the dollar, but the transfer of the
dollar to its debt is beneficial and has a huge impact on countries that hold the dollar as
their main foreign exchange reserve. Therefore, to counter this, we should promote the
internationalization of the Chinese currency and allow more countries to use it. Achieving

 21

the internationalization of China's currency requires reducing the Chinese government's
control over the capital markets and increasing their openness, strengthen economic
cooperation with other countries and increase the convertibility of China's currency.

The second point is that the essence of quantitative easing in the US is to put a large
amount of money into the market, resulting in the depreciation of the US dollar and the
appreciation of China's currency. The appreciation of the Chinese currency has had an
impact on China's export sector, so in order to reduce the impact of US quantitative easing
on China's export sector, China should expand domestic demand and transform the way
China's economy grows, for example, the Chinese government can encourage the
development of high-tech industries and give certain subsidies to this industry to enhance
international competitiveness. Thus, to ensure the development of the country’s capital
market is consistently maintained, it is necessary for the government to execute effective
policies and measures, which will promote steady economic growth.

Quantitative Easing is relevant for future research as it is starting to see more use around
the world today, both by advanced economies as well as emerging economies. For
example, as a response to the Covid-19 pandemic, a total of 166 central banks around the
world resorted to QE. Out of these countries, sixty-five of them are developing countries
that had never previously used QE. (Cavallino et al., 2021). This improves conditions for
future research on this topic, since with a larger pool of data, a more precise analysis of
the effects on QE on stock prices can be conducted. This also improves the possibility of
establishing whether there is a long-term relation between these variables.

 22

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Appendix

1. Stationarity

In order to check for stationarity in our time series, the Augmented Dickey-Fuller test,
or ADF is applied. Checking for stationarity is of significant importance since any non-
stationarity can lead to unreliable or spurious results. the ADF test is implemented for
assessing whether our variables have a unit root, which would notify us whether a non-
stationary problem exists. This paper implements the ADF to test the null hypothesis
that a given time series has a root of unity at the first difference levels for the 1%, 5%,
and 10% significance levels.

The ADF test can be represented mathematically. Where is the error term, ∆ is the
first difference of , is the constant term, is the number of lags. and are the
coefficients. (Dickey and Fuller 1979).

 . ∆ = + − + ∑ ∆ − + 
 = 

 : = 
 : < 

2. Granger Causality Test
Another statistical concept applied in this study is the Granger Causality test. It is a
hypothesis test assessing whether a given time series is practical in providing a
prediction of another value. It is important for the purposes of this paper as a tool for
investigating the correlation patterns between QE and stock prices and could help us in
establishing whether a causal relationship between the variables exists.

The main function of the Granger causality test is to focus on the causal relationship
existing between two-time series, X and Y. Regarding its application, it is basically
utilized for determining the extent to which variable Y can be explained using variable
X. Y is asserted to be caused by X Granger if the variable X is beneficial in the
prediction of Y or when the correlation coefficient between X and Y as a statistical
significance. Below is the equation for the Granger causality test (Granger, 1969).

 27

 

 . = ∑ − + ∑ − + 
 = − 
 
 . = ∑ − + ∑ − + 
 = = 

regarding the first formula, the null hypothesis for the test is
 0 = 1 = 2 = 3……0
For the second formula, the null hypothesis for the test is:
 0 = 1 = 2 = 3……0
There are four possible scenarios for the Granger test exist, which are:
1. When X has an effect on Y, the parameter α is not statistically significant overall in the
first equation, while λ is statistically significant overall in the second equation.
2. When Y has an effect on X, the second formula has a statistically significant overall
non-zero λ. The first formula has a statistically significant overall zero α.
3. If X and Y influence each other, then neither α nor λ is 0
4. if there is no influence between X and Y, then both α and λ are 0
(Granger 1969)

3. Inverse Roots of AR Characteristic Polynomial

To further assess the stability of our VAR model, the inverse roots of the AR
characteristics polynomial will be examined. The VAR is stationary assuming all the
roots lay within the unit circle. This is assessed using EVIEWS (Lütkepohl, 1993).

4. Impulse Response Analysis
Impulse response analysis applied for indicating the extent to which a variable is
shocked and affected by another variable, and to see how this changes over time. The
horizontal axis of an impulse response analysis graph shows the periods between the
effects of shocks, the vertical axis shows the change in the explanatory variable, the red
line in the middle shows the impulse response function, and the blue lines on either side
show the plus or minus two standard deviation (Pesaran, 2015).

 28

 ( , ∆, − )
= ( + | = ∆, + = , … . , + = ; − )
= ( + | = , + = , … . , + = ; − )
(Pesaran, 2022)
 :vector of variables being shocked
Δ: vector of shocks
 t: Information set at time

5. Variance decomposition of VAR model

The variance decomposition of the VAR model is used to analyze the extent to which
each structural shock contributes to changes in endogenous variables, further capturing
the extent to which one variable is influenced by several other variables (Lütkepohl,
2005).

 29