# Relationship Between Asset Price and Monetary Policy

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With the development of capital market and the innovation of finance, asset prices have taken a more prominent role in financial economy. Meanwhile, financial crisis and economy turbulences arouse by abnormal assets price fluctuation appear in many countries. Currently, China is confronted with the reality of asset prices inflation. Asset prices rapidly fluctuation bought gigantic impact to monetary policy, therefore, study the relationship between asset price and monetary policy according to China's economy is significant.

This dissertation applies correlation analysis, unit root test, cointegration test and Granger causality test in the empirical analysis of the relationship between asset price and monetary policy, from the data analysis, we could conclude that asset prices and monetary policy have a long-term relationship. The central bank should focus on the role of asset price on the transmit mechanism of monetary policy.

## 1. Introduction & Motivation

With the development of modern capital market and financial innovation, the world economy has into the financial economy era, and disappears increasingly capitalization, virtualization trends. It is no doubt that modern capital market has provided a powerful lever for economic growth, but it's instability also cause macroeconomic fluctuations , and in particular the asset price bubbles, which is becoming a key factor for financial crisis and economic fluctuations.

So far, the most developed Western countries have experienced a long period of rapid growth, concern is that global asset price has increased sharply in recent years. In the late 1980s, the stock market and real estate in Japan as the representative of asset prices have greatly increased ,which also caused Japanese economy into the bubble economy, the credit crunch and economic recession arising from the bubble economy have serious negative effects so far. In 2006, the Dow Jones industrial average index in USA was beyond the highest point of network technology bubble expansion from 2000, the stock market of many other developed and emerging market countries generally strongly increased and was beyond history records. In addition to the security market , the global real estate, gold and oil market are also very active. In 2001-2005, real estate prices have nearly doubled in many developed countries, meanwhile, real estate price in many developing countries has also generally increased. In May 2006, the international spot gold price reached USD per ounce 718 score in New York City market since 1980. In mid-July 2006, the International crude oil futures price GE exceeded the highest record to reach 75 USD/barrel. But, inevitably brought the more serious financial crisis in 2007, which has caused huge economic fluctuations to the economy from 2007.

In China, securities market have established for ten years, the shares of negotiable securities in the structure of residents' capital portfolios continued to be increasing, in 1992, the total value of Chinese stock market is 1048 billion yuan, accounting for only 3.9% of gross domestic product (GDP) ratio . But in 2007, the stock market value is 327141 billion yuan, the ratio of market value in GDP has being greatly rising to 130% , which is 312 times growth compared with the total stock market value in 1992 . Not only a huge amount, but also the fluctuations of asset prices have become more frequent and intense. In 2006-2007, Chinese economy under the driven of stock market and real estate market has a certain degree of asset price bubbles. While in the same time , Chinese economy is actually facing the reality of asset price falling from the top digit, shanghai security market falls rapidly from the peak position in 2007, the stock market bubbles receive the extrusion, the real estate market similarly is also facing the similar situation, house price of major cities has falls obviously, the turnover falls into the valley.

All of these financial crisis constantly are reminding people that the worldwide economic fluctuations are characterized by the financial instability , and economic cyclical fluctuations, instead of disappearing, and to be getting worse, cause considerable economic depression, frequent asset price volatility and financial crisis and economic recession arising from the asset market collapse, hence , the government should focus on the asset prices on the role of macroeconomic fluctuations and the central bank in the world have to consider the information from asset price fluctuations. The Fed Chairman Alan Greenspan and Bernanke concern much about monetary policy and asset price volatility. Alan Greenspan proposed central banks should be more concerned about the issue of asset price bubbles in the anniversary meeting celebrating the establishment of the Bank of England in 1994 . Chairman Bernanke is an internationally recognized as the founder of monetary policy and asset price research. At their encouragement , the international academic community and the national central banks have recent research and debate whether the monetary authorities should intervene directly in asset price fluctuations. These research and debates are from the different backgrounds in different countries, in accordance with their different assumptions and premises, provide some significance policy advice.

Financial markets in particular capital market deepening and broad-based, and financial innovation enables financial institutions have diversity features. The boundaries of currency and other financial assets is blurring, money supply and real economic variables lost stability, the monetary policy impact on the real economy is no longer limited to traditional approaches, according to the traditional Keynesian theory, this impact on consumption and investment mainly through interest rate variable . But as the improvement of financial system and increase of financial assets stock , monetary policy can also use the wealth effect of asset prices and Tobin q to affect the consumption and investment, causing the changes of total demand, in stick price, the aggregate demand led to a change in the output , and cause the effect of the output changes on demand, if the aggregate demand exceeds the aggregate supply ,it can lead to inflation pressures. This series of transmission mechanism make the role of asset market on the real economy become more prominent, asset price has become a major transmission channel of monetary policy .

From the reality in China, the rapidly development of asset markets have a key role in our national economy, the impact of real estate market and stock market on economic and monetary policy are becoming increasingly apparent. In fact, the Chinese monetary authorities have also already begin to pay attention to the relationship of asset price and monetary policy . Xiaochuan, Zhou , as the Governor of Chinese central bank ,says that the central bank concerns about changes in asset prices and gives full attention to information from asset price when formulating and implementing monetary policy. Therefore, study the relationship between asset price and monetary policy in Chinese economy is significant.

This dissertation analyze the relationship of asset price and monetary policy

systematically, and will use econometric methodology to seek to explore the relationship between asset price and monetary policy in China by using quarterly statistics from 1998 to 2008. I will estimate the relationship between monetary policy variables and asset price variables , through correlation analysis, unit root test, cointergration test Granger Causality test to get the conclusion. This dissertation conclude the results that¼šmonetary policy and asset price have a long-term relationship, in a short time, the monetary policy aggravated the asset price fluctuations to some degree, the asset market appeared to be rapidly soared and shirked in a short period of time. The central bank should focus on the role of asset price on the transmit mechanism of monetary policy.

## 2. Literature Review

## 2.1 Empirical Studies from Western Economists

The research from western economists on relationship of asset price fluctuations and monetary policy mainly reflects the two views. First, asset price and monetary policy do not exist the causal relationship on behavior , and the only relationship is on the information that reflects the present and future output growth. Another view on research of asset price fluctuation and monetary policy is that asset price can affect consumption and investment through wealth effects , the change of capital cost and asset price fluctuations affect consumption and investment respectively through wealth effects and Tobin q, thus affect the financial institutions status of assets and liabilities , further affect the stability of the financial system. It is evidently that asset price has become the transmission mechanism of monetary policy.

Frank Smets (1997) is one of the economist who systematically analysis the optimal monetary policy that Central Bank should response on asset price change. He has proposed the following important viewpoint: how the central bank should respond to monetary policy reflecting the unexpected change of asset price, how this change affect the central bank's inflation forecast. There are two factors affecting the central bank forecast inflation. First is that the effect of asset price on transmitting mechanism of monetary policy. Second is that the unique information on asset price. He established a simple macro-economic model that contains equation of Phillips curve, aggregate demand on financial asset prices (as represented by the stock price) , arbitrage and dividend , he uses this model to examine the variety of ways that change of financial assets price affect the real economy, and analysis the optimal monetary policy of central bank response to financial asset price movements. He demonstrate that the optimal monetary policy of central bank response to financial asset price movements, that is, according to the structure of established model , weighted average of the short-term interest rate ( traditional intermediary target of monetary policy) and asset price as price index — monetary conditions index (MCI) , and regard this index as the target of monetary policy operations, therefore it can properly guide central bank make effectively response on monetary policy to change of financial assets price.

Gunnarsson and Lindqvist (1997) have discussed the role of asset price on monetary policy from the wealth effect on change of asset price and the effect of inflation. They conclude that the monetary policy should be given more attention on the change of asset price, although it is very difficult to explain. They believe that the change of asset price affect monetary policy as long as this change is long-term change, and in recent years, the impact of this change of asset prices on the economic has been more and more important, so the central bank should spend more energy to analyze the relationship of asset prices and monetary policy, although this relationship is hard to explain but indeed existing. They believe that asset price as an indicator for monetary policy might contribute to the inflation forecast.

B.Bemanke and M.Gcrtler (2000,2001) have provided that :In an particular assigned situation , the monetary policy respond to the change of asset price is determined by if existing of the inflation or the deflation pressure on real economy or not, if this change of asset price do not bring the inflation or the deflation pressure on the real economy, then the monetary policy does not need to respond to this kind change of asset price, but If this change of asset price indicates that it indeed brings the inflation or the deflation pressure on the real economy, then the monetary policy should make some response to alleviates this pressure. They propose the above policy seriously under the system frame of flexible currency inflation goal.

Cecchetti, Genberg, Lipsky and Wadhwani (2000) have noted that how the central bank respond to asset price fluctuation mainly depends on the nature of the asset price fluctuation. When only have the shock of financial aspect on macro-economy, the central bank's exchange rate policy should make an appropriate response, because doing so will avoid the financial shock on the stability of real economy . When the central bank's target is to minimize the fluctuations of Inflation ratio and economic output gap to their target value , the central bank take possible action to eliminate the negative effects of financial volatility is a very good thing.

## 2.2 Empirical Studies from Chinese Scholars

After the Asian financial crisis in 1997, the scholars in China have began to research the relationship between asset price and monetary policy. From a theoretical viewpoint, on the one hand, monetary policy have an impact on asset prices through the adopted operation tool, on the other hand, as a virtual asset relatively to physical asset , asset price fluctuations can also have some impact on people's consumption and investment behaviour, hence, affect economic development through consumer and investment , further transmit monetary policy purpose to the real economy.

Xiao'an Qian (1998) finds that change of asset price make a difficulty in monetary policy transmission mechanism, this will cause the certain effects on monetary quantity management, inflation control and financial risk avoidance. The increase of asset price has been made transmission role of the monetary policy in the currency market change and become a source of funds in the asset markets, causing short-term funds long-term occupation , so that the transmission mechanism of monetary policy to occur difficulty. Part of funds seperated from the bank system, directly to the virtual asset markets.

Wenjun Xun (2000) believes that the development of capital market increase the number of the emerging non-bank organization such as superannuation fund, the mutual fund, the Insurance company and so on, the bank also participates in the competition of capital market , the effect of the capital market on the real economy gradually highlight, the transmitted mechanism of monetary policy increases, economic subject and its behavior are diversity, uncertainty about the economical movement increases, therefore the transmitted mechanism of monetary policy is more complex. They thinks that Central Bank's monetary policy should control official interest rate through the market , thus indirectly influence the bond and the stock market price in capital market, further influence real economy, achieves the monetary policy goal.

Qiang Qu (2001) has found that it is difficult to put asset prices as the goal of direct control of monetary policy in the monetary policy operations, the possibilities and accuracy of establishment of general asset price index is very small, asset prices can only be used as an indirect reference, in short, to concern on it , but not target on it.

Gang Yi and Zhao Wang (2002) have considered that monetary policy have impact on financial asset prices (in particular the stock price), the relationship of currency quantity and inflation not only depends on the price of goods and services, and in a certain degree depends on the stock market.

Tianyong Guo(2006) has affirmed the role of asset price fluctuation on real economy , financial stability and monetary policy through analysis, at the same time, he also points out that the asset price as regulatory targets exist difficulties.

Chang Cui (2007) analysis the role of asset price on monetary policy through the model , in asset price inflation period, the central bank can take the measure of interest rate for a given period too control asset price fluctuation, and control the money supply when asset price bubbles exist will receive immediate effect. While in asset price downturn period , interest rate adjust asset price have obvious and relatively durable effect.

Yuanquan Yu (2008) obtains through the empirical analysis: the asset price has a certain influence on macroeconomic , particularly the effect of house price is more obvious. Therefore, the Central Bank must give the appropriate attention and control on asset price in the implement of monetary policy .

In an conclusion, the asset price fluctuations have an certain impact on the ultimate objective of monetary policy, we can not ignore the unique role of asset price on the transmission mechanism of monetary policy and the macro-economic activities. The central bank should concentrate on the effect of asset prices on monetary policy, particularly in asset prices fluctuations periods, the vast majority of economists believe that the central bank should take an certain monetary policy to address and reduce the negative effect of the economy. For most of research focuses on the study of asset price fluctuation and its relationship with monetary policy, the role of asset prices in the transmission mechanism of monetary policy , as well as the effect size issues ,this dissertation based on the domestic and foreign scholar research results , deeply analyzes the transmitted mechanism of monetary policy in asset price through the impact of monetary policy on asset price .

## 3. Data Description

This dissertation focus on the relationship between asset price and monetary policy in China according to the quarterly statistics during the year of 1998 to 2008.

This dissertation mainly use the stock price (index) and house price(hsp) as indicators of asset price , and use boarder money supply (m2), financial institution loan (loan), real rate (rate) as indicators of monetary policy for simplicity. Due to the amount of the data of these variables are really great, we take log of these variables to analyze. This dissertation get all needed data from China Economic Information Network, which is a professionals institution engage in the development of economic data resources and services, provide data support, data integration, and other business data analysis for government and research institutions. All the quarterly data we need from 1998 to 2008 is recorded in the China Economic Information Network.

## ã€€

## 3.1 Indicators for asset price in China

Asset prices generally including stock prices, bonds, prices, and even exchange rate, and other financial assets and house prices. However, the stock price and house price have a significant effect on real economy, and its fluctuations can have a key role in monetary policy decision-making, hence, in this dissertation , we will use the stock price and house price refer to the asset price. In particular, the Shanghai securities composite index is on behave of the stock price for data limitations, Shanghai securities composite index is established by the Shanghai stock market to reflect the Shanghai securities trading market overall trend. House price is on behave of the average house price in China. We can easily get these data from the China Economic Information Network.

## 3.2 Indicators for monetary policy in China

Monetary policy refers to the Government or the Central Bank influence economic activity, especially by money supply control and regulation of interest rates. To achieve a specific goal or maintain target — for example, curbing inflation ,achieving full employment and economic growth, directly or indirectly through open market operations and setting the minimum reserve rate. There are many factors needed to be consider in implementing monetary policy, for data restrictions, in this dissertation ,we mainly consider the variable of boarder money supply, financial institution loan and real rate.

First, boarder money supply (lnm2) indicates the change of aggregate supply and pressure condition of inflation in the future. In china, boarder money supply is narrow money supply plus the saving, foreign currency and fiduciary deposits of government, organizations, services, businesses and institutions in financial institution. Boarder money supply can be used as a medium and long-term equilibrium target to regulate of financial markets .It is usually the rate of boarder money supply increasing should be controlled at the sum rate of economic growth and price inflation, monetary movement.

Second, financial institution loan have some disadvantage as a indicators of monetary policy. First, it is closely associated with the monetary policy objective. Currency circulation and deposit currency caused by loan, the Central Bank control the size of the loan, which also mean to control the money supply. Second, financial institution loan is an accuracy an endogenous variable , loan size is positive correlation with loan demand. As a policy variables, loan size and the demand also have a positive correlation. Furthermore, data of financial institution loan is easily accessible .

Third, real rate refers to the real rate of interest return that the depositors and investors can get after eliminating of inflation rate, it is calculate as nominate rate minus CPI. Real rate can be used as the indicator of Central Bank's monetary policy due to following reasons : (1) real rate reflect the supply of money and credit, and able to show the relative supply and demand, it is correlation with nominal interest rate ,High level of interest rate is thought to be a tight, low interest rate level of convergence are considered monetary relaxation. (2) real rate belongs to the Central Bank , the Central Bank can use this tools to increase or decrease in interest rates.

Table 1: denotation for Variables

denotation

Variables

Implication

Lnindex

Shanghai securities composite index

Shanghai securities trading market overall trend

Lnhsp

House price

Real estate price

Lnloan

Loan

financial institution aggregation loan domestic

Lnm2

M2

boarder money supply: M2+M1

Rate

Real rate

nominate rate minus CPI.

## 4. Economic Theory and Econometric Model

The effectiveness of monetary policy depends not only on the sensitivity of economic subjects on policy signal , but also on numerous external factors of financial system. According to the traditional Keynesian theory, when implementing expansionary monetary policy, increase of money supply will lead to rate decline, i.e. capital costs decreasing, further increasing investment expenditure, hence increasing aggregate demand and aggregate output. Meanwhile, increase of money supply will lead to the bank reserve and deposit increase, thus enhanced bank to increase the loan quantity, the fund that the borrower attains increase, then the total quantity investment will increase, which also lead to the quantity of aggregate demand increase, hence, the total output also rise. We will use following econometric model to analyze the relationship of asset price and monetary policy.

## 4.1. Analyzing correlation coefficient

The correlation coefficient is a measure of two variables relate to each other and their close degree of effective tools. Its absolute value is close to 1 description of relevance, the stronger between variables, the more its relevance with 0. If the correlation coefficient is positive, then the variables presented to changes in the relationship, with one variable with another variable changes. But if the correlation coefficient is negative, then the variables are changes in the relationship in the opposite direction. Using correlation coefficient can be better measured variables and between monetary policy and asset price correlation between Extent its positive and negative symbol can indicate the variable ask changes direction. Generally used to be associated matrix said.

## 4.2. Testing for Nonstationary

In time series, stationary is a key concept, as it allows powerful techniques for modelling and forecasting to be developed. Stationary is generally regarded as some pattern of data stable or equilibrium. Stationary time series have constant mean and variance, but its covariance only determined by the time distance. However, when time series could not analyze as stationary, this types of time series always have a strong upwards or downward trend over time, we call it as nonstationary, and we can use differencing as an effective tool to transform a nonstationary time series into a stationary time series. Sometimes, Transforming a nonstationary time series into a stationary one needs more than once differencing operation. Generally speaking, if the differencing needs to be operated at least d times to achieve a stationary time series where d is the order of integration, then the time series is said to be integrated of order d, denoted by I(d). Hence, the I(1) time series also referred to have a unit root, while the I(0) time series are stationary.

Dickey and Fuller (1979) provided an effective method to test a time series is stationary or nonstationy time series, which is also called as Dickey-Fuller (DF) test. The elementary object is to test the null hypothesis that the time series have a unit root or not. The model the Dickey-Fuller (DF) test involves bellows

In this dissertation , indicates the variables on monetary policy and asset price at time t. α denotes unknown parameter and denotes the trend. denotes the first difference which . Also, the t-statistic for testing the null hypothesis that H0: =0 against the alternative hypothesis H1: <0. In this paper, since house price , boarder money supply and loan have a strong upward trend , so we test these time series under the model H0 : against H1:

While the index and real rate variable we consider under the model H0: against H1:

We also can identify the fittest lag k by running the ADF(k) test, choosing the fitted order k that gives the minimum AIC and BIC.

## 4.3 Cointegration

Formally, Engle and Granger (1987) defined the cointegration as if there exists a linear combination of two or more I(d) time series which is I(d') with d'<d. In most case, two cointegrated time series has a unit root ,as I(1),a their combination is stationary ,as I(0).

In practice, we usually use cointegration test to exam the long-run relationship among variables in economics. If times series have relationship between variables, and the trend of the two time series has been common, and thus there will be a linear combination of these time series give us an stationary time series.

In this dissertation , we test the long-run relationship between monetary policy variable and asset price variable by cointegration test .First, we consider the regression of two I (1) time series. The model is

To test { } and { } are cointegrated, we need to exam that the residuals term { } is stationary .If the residuals term is I (1), then this two times series do not have a cointegration, otherwise , if the residuals term is I (o), then this two times series are cointegrated. Under this case, to test the residuals for unit root ,we can conduct DF/ADF-statistic test.

In this paper, we denote that monetary policy variables as and we regress on a constant and one of the asset price variables as .

## 4.4. Causality Test

Granger (1969) provided that Granger causality test can apply generally for testing the causal relationship on two time series.Granger causality means that if { } Granger causes{ } then { } have a predict power of { } , given any other variables. More formally, it is said that { } Granger causes { }; when the forecast of given data on { } and { } outperforms the forecast of given data on { }only. Granger causality is only related to the predictability of { } using { } and is not concerned as to whether{ }causes { }, it could be that { } Granger causes { } but { } is not causal for { },and vice versa.

To test for Granger causality, we could estimate the regression by OLS

In this dissertation , denotes an indicator of asset price, i.e. Shanghai composite index (lnindex) , house price ( lnhsp) , Also,denotes the indicator of monetary policy, i.e. financial institutions aggregate loan (lnloan), broad money (lnm2), real interest rate (rate) .

Then conduct an F test on the null hypothesis against the alternative at least one of the is not zero. If we reject the null hypothesis, then

{ } has predictive power for { } and therefore, { } Granger causes { }, on the other hand, if we fail to reject the null hypothesis, then { } has no predictive power for { }, therefore, { } does not Granger causes { }.We usually test the two times series for Granger causality in pairs, that is, first test whether { } Granger causes { } and then test whether { } Granger causes { }.If two variables have Grange causality relationship in both directions, i.e. { } Granger causes { } and { } Granger causes { }, then we could regard these two varibles have causality relationship in both directions, that means these two variables are related. If two variables have Granger causality in one direction, e.g. { } Granger causes { } but { } does not Granger cause { }, then we can conclude that these two variable just have a one way causality relationship.

## 5. Presentation and Interpretation of Results

## 5.1. correlation coefficient between monetary policy variables and asset prices variables

We analysis the correlation between monetary policy variables and stock prices variable according to the data provided by China Economic Information Network, and the correlation coefficients are presented in Table 2 and Table 3.

Table 2 Correlation coefficient between lnindex and lnloan,lnm2,rate in 1998-2008

Lnindex

Lnloan

Lnm2

Rate

Lnindex

1.000000

Lnloan

0.4829

1.000000

Lnm2

0.4867

0.9980

1.000000

Rate

-0.4717

-0.9013

-0.9067

1.000000

As we can see, stock price (Lnindex) has correlation relationship with all monetary policy variables. With a higher stock price, loan and money supply will be increased, while real rate will be decreased. For monetary variables, loan and M2 have a strong positive correlation, and M2 have a strong negative correlaton with real rate. In conclusion , for the stock price variable, it has basically the positive correlation with the loan and money supply variables , and has negatively correlation with the real rate.

Table 3 Correlation coefficient between lnhsp and lnloan,lnm2,rate in 1998-2008

Lnhsp

Lnloan

Lnm2

Rate

Lnhsp

1.000000

Lnloan

0.9667

1.000000

Lnm2

0.9633

0.9980

1.000000

Rate

-0.8453

-0.9013

-0.9067

1.000000

From table 3, we can see house price (Lnhsp) has correlation relationship with all monetary policy variables. With a higher house price, loan and money supply will be increased, while real rate still will be decreased. In conclusion ,for the house price variable, it has basically the strong positive correlation with the loan and money supply variables , and has strong negatively correlation with the real rate.

## 5.2.Results for unit root test

We exam monetary policy variables and asset prices variables by Augmented Dickey-Fuller (ADF) to test the stationary of time series. First ,we choose the AIC and BIC to determine the fitted lag it suggest that the optimal lag for time series is lag k =1,Then we run ADF to test stationary of time series. Results are below:

Table 4¼šAugmented Dickey-Fuller Unit Root Test for Variables

Series

ADF

Test critical values

Results

5%

1%

Lnhsp

-1.685

-3.41

-3.96

have a unit root

Lnindex

-2.085

-2.86

-3.43

have a unit root

Lnm2

-1.992

-3.41

-3.96

have a unit root

Lnloan

-1.993

-3.41

-3.96

have a unit root

Rate

-1.185

-2.86

-3.43

have a unit root

We conserder Lnindex and Rate for unit root test under case which is constant without trend, and get the ADF values are -2.085, and -1.185.The critical values are from the asymptotic critical values of the ADF statistic table. From the ADF statistic table. We know the critical value at the 5% significant level is -2.86, while it is -3.43 at the 1% significant level. Since Lnindex ADF value is-2.085, which is greater than -2.86 and Rate ADF value is -2.400,which is also greater than -2.86, so we fail to reject the null hypothesis at the 5% significance level and conclude that we have evidence that both Lnindex and rate have a unit root, and also mean that the time series is nonstationary. Since Lnhsp Lnm2 and Lnloan of the data have a strong upwards or downward trend ,so we exam these three time series under case which is constant with trend,and get the ADF values are -1.1685, -1.992 and -1.993. From the ADF statistic table.We know the critical value at the 5% significant level is -3.41, while it is -3.96 at the 1% significant level. Since ADF value of Lnhsp, Lnm2 and Lnloan is -1.1685, -1.992 and -1.993 respectively , which are all greater than -3.41 at w at the 5% significance level , and which is also greater than-3.96, at the 1% significance level ,so we fail to reject the null hypothesis at the 5% and 1%significance level and conclude that we have evidence that Lnhsp Lnm2 and Lnloan have a unit root, and the time series is nonstationary.

To test the orders of integration of all the time series, hence, we do first differencing to all time series and add an initial D to each variables to indicate the new variables. We use ADF-test again to test all the first differencing variables as above. The fitted lag we consider is also lag=1, the results are shown in Table 4.

Table 5¼šResults from ADF-test with first difference variables

Series

ADF

Test critical values

Results

5%

1%

DLnindex

-3.525

-2.86

-3.43

I(0) Stationary

DLnhsp

-4.484

-3.41

-3.96

I(0) Stationary

DLnm2

-4.876

-3.41

-3.96

I(0) Stationary

DLnloan

-3.854

-3.41

-3.96

I(0) Stationary at 5%

Have a unit root at !%

DRate

-3.943

-2.86

-3.43

I(0) Stationary

From above table, the ADF values of all 5 variables are -3.525, -4.484 -4.876, -3.854 and -3.9459 which are smaller than the critical values at 5% significant level, so we reject the hypothesis at 5% significant level , and conclude that we have no evidence that all the first difference time series have unit roots. It suggests that, after first difference for each of the time series, data have been stationary. Moreover, it shows that the original series of lnindex,lnhsp,lnloan,lnm2 and rate are I (1); their orders of integration are 1.While at 1% significant level , the ADF value of Dlnloan is greater than critic level ,so we conclude that Dlnloan have a unit root.

## 5.3 Results for cointegration test

We need to test the long-run relationship of asset price variables and monetary policy variables by using cointegration test , so we use ADF-test to test the residual .From the statistic table ,we know the 5% critical values is -3.34.while , the 1% critical values is -3.9.

5.31 Cointergrate test of Lnm2 and Lnindex

Fitted regression model is Lnm2=7.64+0.62Lnindex+

For the residual, the ADF-test results is -4.61, Since -4.61 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residual is stationary, hence we conclude that the Lnm2 and Lnindex have cointegration relationship.

5.32 Cointergrate test of Lnm2 and Lnhsp

Fitted regression model is Lnm2=-3.58+2.02 Lnhsp +

For the residuals, the ADF-test results is -4.883, Since -4.883 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residuals is stationary, hence we conclude that the Lnm2 and Lnhsp have cointegration relationship.

5.33 Cointergrate test of Lnloan and Lnindex

Fitted regression model is Lnloan =8.02+0.52Lnindex+

For the residuals, the ADF-test results is -4.777, Since -4.777 is smaller than critical values, so we reject H0 at both 5% and 1%significance level and we have evidence that the residual is stationary, hence we conclude that the Lnloan and Lnindex have cointegration relationship.

5.34 Cointergrate test of Lnloan and Lnhsp

Fitted regression model is Lnloan =-1.61+1.72Ln Lnhsp +

For the residuals, the ADF-test results is -4.397, Since -4.397 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residuals have a unit root, hence we conclude that the Lnloan and Lnhsp have cointegration relationship.

5.35 Cointergrate test of Rate and Lnindex

Fitted regression model is Rate =21.08 -2.67Ln Lnhsp +

For the residuals, the ADF-test results is -4.473, Since -4.473 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residuals have a unit root, we conclude that Rate and Lnindex have cointegration relationship.

5.36 Cointergrate test of Rate and Lnhsp

Fitted regression model is Rate =63.29 -7.91Ln Lnhsp +

For the residuals, the ADF-test results is -4.4381, Since -4.4381 is smaller than critical values, so we reject H0 at the 5% significance level and we have evidence that the residuals have a unit root, hence we conclude that Rate and Lnhsp have cointegration relationship.

In sum, by the testing procedures as above, we can conclude that monetary policy variables and asset price variables have cointegration relationship, which means that they have long-run relationship based on quarterly data during the period from 1998 to 2008 in China. The central bank should focus on the long-run relationship of asset price and monetary policy.

## 5.4 Results from Granger Causality Test

We exam the short-run relationship between asset price variables and monetary policy variables using Grange Causality Test, and reveal the predict power of these variables. We proceed the Granger causality test of asset price variables and monetary policy variables in lag=1and 4. Results are shown below.

1. . Causality test between monetary policy variables and stock prices variable

Table 7: Lag=1 Granger causality test

Null Hypothesis

F-Values

Test Critical Values

Results

10%

5%

Lnloan Does Not Granger Cause Lnindex

F(1,40)=0.11

4.61

5.99

Accept Null

Lnindex Does Not Granger Cause Lnloan

F(1,40)=0.03

4.61

5.99

Accept Null

Lnm2 Does Not Granger Cause Lnindex

F(1,40)=0.14

4.61

5.99

Accept Null

Lnindex Does Not Granger Cause Lnm2

F(1,40)=0.07

4.61

5.99

Accept Null

Rate Does Not Granger Cause Lnindex

F(1,40)=0.01

4.61

5.99

Accept Null

Lnindex Does Not Granger Cause Rate

F(1,40)=0.46

4.61

5.99

Accept Null

If 2 times F-values greater than critical values , we reject null.

From table 7, we can conclude that in the lag of 1, at the 10% and 5% significance level ,stock price (Lnindex) variable and monetary policy variables have no Granger cause relationship, so stock price variable has no predict power to monetary policy variables, and also monetary variables has no predict power to stock price variables.

Table 8: Lag=4 Granger causality test

Null Hypothesis

F-Values

Test Critical Values

Results

10%

5%

Lnloan Does Not Granger Cause Lnindex

F(4,31)=1.16

7.78

9.49

Accept Null

Lnindex Does Not Granger Cause Lnloan

F(4,31)=0.03

7.78

9.49

Accept Null

Lnm2 Does Not Granger Cause Lnindex

F(4,31)=0.89

7.78

9.49

Accept Null

Lnindex Does Not Granger Cause Lnm2

F(4,31)=0.16

7.78

9.49

Accept Null

Rate Does Not Granger Cause Lnindex

F(4,31)=0.62

7.78

9.49

Accept Null

Lnindex Does Not Granger Cause Rate

F(4,31)=0.28

7.78

9.49

Accept Null

If 2 times F-values greater than critical values , we reject null.

As presented on Table 8, in the lag of 4, at the 10% and 5% significance level stock price (Lnindex) variable and monetary policy variables still have no Granger cause relationship. We can not use monetary policy variable to predit asset price variable , and vice versa. This also show that adjusting monetary policy variable affects stock market's price level is very difficult, even assuming it has the effect, it must pass through a very long time period, the effect can be appearance.

2. Causality test between monetary policy variables and house prices variable

I will analyze the causality relationship between house prices variable and monetary policy variables in the same way above. Results are shown as Table 9 and Table 10

Table 9: Lag=1 Granger causality test

Null Hypothesis

F-Values

Test Critical Values

Results

10%

5%

Lnloan Does Not Granger Cause Lnhsp

F(1,40)=3.58

4.61

5.99

Rejec Null

Lnhsp Does Not Granger Cause Lnloan

F(1,40)=0.13

4.61

5.99

Accept Null

Lnm2 Does Not Granger Cause Lnhsp

F(1,40)=2.74

4.61

5.99

Reject Null at 10% Accept Null at 5%

Lnhps Does Not Granger Cause Lnm2

F(1,40)=0.22

4.61

5.99

Accept Null

Rate Does Not Granger Cause Lnhsp

F(1,40)=1.57

4.61

5.99

Accept Null

Lnhsp Does Not Granger Cause Rate

F(1,40)=2.84

4.61

5.99

Reject Null at 10% Accept Null at 5%

If 2 times F-values greater than critical values , we reject null.

From table 9 , we can conclude : in the lag of one, at the 5% significance level, loan can Granger Cause house price, but house price does not Granger cause loan , these two variable just have uni-directional Granger cause relationship, house price and other monetary variable have no Granger cause relationship. While at the 10% significance level , loan and boarder money supply can Granger Cause house price, and also house price can Granger cause Rate. This also indicates that adjusting financial institution loan and boarder money supply can have certain effect on house price in short time, based on quarterly data, we can use financial institution loan and boarder money supply to predict the house price in a certain period.

Table 10: Lag=4 Granger causality test

Null Hypothesis

F-Values

Test Critical Values

Results

10%

5%

Lnloan Does Not Granger Cause Lnhsp

F(4,31)=2.32

7.78

9.49

Accept Null

Lnhsp Does Not Granger Cause Lnloan

F(4,31)=0.12

7.78

9.49

Accept Null

Lnm2 Does Not Granger Cause Lnhsp

F(4,31)=1.69

7.78

9.49

Accept Null

Lnhsp Does Not Granger Cause Lnm2

F(4,31)=0.36

7.78

9.49

Accept Null

Rate Does Not Granger Cause Lnhsp

F(4,31)=2.4

7.78

9.49

Accept Null

Lnhsp Does Not Granger Cause Rate

F(4,31)=2.44

7.78

9.49

Accept Null

If 2 times F-values greater than critical values , we reject null.

As presented on Table 10, in the lag of 4, we can see asset price variables and monetary variables have no Granger causality relationship at 10% and 5% significance level.

Synthesizes the above analysis, we can conclude that the stock price and monetary variables have no two-way causality relationship in short run, and monetary policy have no impact on stock price in short run . But in some certain degree, loan and boarder money supply can Granger cause house price, it means that loan and boarder money supply can predict house price in short time.

## 6. Conclusion

In this dissertation , we study the relationship between asset price and monetary policy in China by using quarterly statistics during the year of 1998 to 2008.

We use the stock price (index) and house price (hsp) as indicators of asset price , and use boarder money supply (m2), financial institution loan (loan), real rate (rate) as indicators of monetary policy. We expect those asset price variables would have some effect on the monetary policy. Hence, we process these time series through ADF-test, Cointegration-test and Granger causality test to reveal the long-term and short-term relationship among them.

Then, from the results of cointegration test, the results suggest that the indicators of asset price have cointegration with monetary policy base on the quarterly data during 1998 to 2008 in China, hence, it implies that there is long-term relationship between the fluctuations of asset price market and monetary policy decision-making in China.

Further, we use Granger causality test to exam the short term relationship. In the lag=1 and 4 , the results reveal that stock price variable and monetary policy variables have no causality relationship in short run, stock price variable has no predict power in monetary policy variables. But in some certain degree, that loan and boarder money supply can predict house price in short time.

In summary, from analysis in this dissertation , we can get some useful policy implications in China.

First, improving relevant conditions of asset prices and monetary policy transmission mechanism .As China's capital market development, asset price transmission channels to monetary policy gradually disappear. Although current credit market and money market funds directly or indirectly to concentrate on the asset market ,which indeed create opportunities for the monetary policy transmission mechanism , but do not form a valid investment and consumption demand, to some certain extent, resulting in distortion of the monetary policy transmission mechanism. As money supply increasing ,financial insistutions have sufficient funds, make loan enlarge, and also cause rapid increasing in stock price.

Dramatic fluctuations in asset prices, on the one hand resulted in financial system instability and, on the other hand, asset price transmission channels to monetary policy is also not very smooth. However, as the stock market, house price and other assets further development, the effect of traditional monetary policy transmission mechanism will gradually diminish, the role of asset price transimitted to monetary policy will play an important role, so now we must improve relevant conditions of asset prices and monetary policy transmission mechanism by Tobin q effect, the wealth effect, balance sheet effects and many other channels .

Second, Monetary policy should focus on asset price fluctuation. The asset price has not been able to take as the independent regulation target of the monetary policy ,but should take it as the auxiliary monitor target of currency regulation ,integrates to the field of the Central Bank monetary policy Central Bank .The central bank should establish indicator system relative to asset price monitoring, and make corresponding respond to estimate the impact of market movement and change of asset price on macro economic fluctuation, then determine the trend of monetary policy, implements the essential regulative behaviour, meanwhile , the central bank must clarify the shocks of asset price fluctuation.

Third, the central bank should focus on the impact of the real estate market on real economy. Empirical results show that housing price has played a certain role in the transmission mechanism of monetary policy in China, the correlation with monetary policy variable is extremely high. The central banks should focus on the change of real estate market because the real estate industry is pillar industries, the real estate market price fluctuation will affect our investments, further to fluctuations in related industries, to even worse, it may affect the whole macro economy development. Accordingly, the Government should concentrate on impact of the real estate market on macro economy.

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