INTRODUCTION
RELEVANCE AND BACKGROUND OF STUDY
Since the financial reforms of the early 1990s, Vietnam has undergone significant restructuring of state-owned commercial banks (SOCBs) and the establishment of joint-stock banks (JSBs) This transformation has led to a deepening of the financial system, with the M2 to GDP ratio rising from approximately 25% in the mid-1990s to over 70% by 2004 In that same year, SOCBs accounted for 73% of total credit, highlighting their dominant role in the market The credit landscape has become segmented, with JSBs and smaller banks primarily serving the private sector, while SOCBs have provided loans more evenly across both public and private sectors (Camen, 2006).
Vietnam's entry into the World Trade Organization (WTO) sparked a significant influx of foreign direct investment and portfolio inflows, marking a pivotal moment in the country's globalization journey However, this rapid economic integration has brought considerable challenges, particularly an unfavorable balance of payments Since 2000, Vietnam's financial sector has experienced explosive growth, especially during the 2007-2008 period, when the credit market surged by approximately 50% in January 2008, contributing to a rise in inflation that reached 14% By March 2011, inflation accelerated to 13.89%, the highest in 25 months, while the trade gap widened to $1.15 billion, up from $1.11 billion in February.
PROBLEM STATEMENT
Economic models often simplify the complexities of the economy by assuming that changes influenced by financial conditions are primarily determined by a limited range of financial variables, such as short-term risk-free interest rates or long-term government bond rates (Hall, 2001).
As financial systems have advanced significantly in recent years, their influence on the economy has become more extensive and profound This complexity makes it challenging to identify the underlying issues within a developed economy, as certain variables may remain unrecognized A notable example is the 2008 global financial crisis, which stemmed from the credit sector, particularly the mortgage asset crisis in the U.S., as well as the capital flight from the Vietnamese securities market, driven by prior years' loose monetary policies.
Historically, economists like Pintinkin, Gurley, and Shaw highlighted the crucial role of financial intermediaries and credit markets Modigliani and Papademos (1977) acknowledged that traditional monetary theory overlooked the impact of these intermediaries and bank credit Research by Gurley and Shaw (1956) indicated that financial intermediaries exert a stronger influence on credit supply than on money supply Consequently, the credit channel plays a significant role in shaping policymakers' decisions.
Understanding the role of the credit channel in the financial market is essential for policymakers, as it plays a critical role in the transmission of monetary policy Identifying how the credit channel facilitates monetary transmission is vital for improving current policies, ultimately contributing to the achievement of national economic objectives.
This study aims to identify the role of credit channel in Vietnam’s monetary transmission mechanism, specify 1996-2010 period Following the main objective, the thesis:
- To analyze whether past value of credit helps predict the money supply;
- To examine the impact of credit shocks on money supply, also other macro economies;
- To test whether credit shock plays important role in forecasting the error of money supply.
RESEARCH QUESTION
To obtain the above objectives, this thesis will attempt to answer the following questions:
What is the role of credit channel in the monetary policy transmission in Vietnam case over the period 1996-2010?
- Does the past value of credit help predict money supply?
- How does money supply reaction to credit shock?
- Whether credit shocks plays important role in forecasting money supply’s error?
To carry out above objectives, this study uses quarterly data from 1996:Q1 to 2010: Q3 Econometric techniques and descriptive statistic will be employed as primary quantitative in this research
Descriptive statistical analysis provides an overview of all variables used in this thesis, including their distribution, variation, and central tendency for both original and modified data, allowing for an initial assessment of data quality To analyze time series data, vector autoregression (VAR) techniques will be employed to address key research questions A unit root test will first be conducted to check the stationarity of all variables, ensuring the validity of subsequent t-tests and F-tests Optimal lag lengths for the VAR model will be determined using various criteria to identify the best-fitting model The Granger causality test will investigate whether past values of credit can forecast money supply, while impulse response functions and variance decomposition will address the final two research questions The findings will ultimately shed light on the role of the credit channel in monetary transmission within the context of Vietnam.
STRUCTURE OF THESIS
The study is organized as following:
Chapter 1 introduces the importance of thesis, relevance and back ground of study, the objectives and research questions And the methodology is presented as briefly in this part
Chapter 2 demonstrates the literature review Firstly, credit channel theory is mentioned as a core of study Secondly, empirical studies about the role of credit channel in monetary policy transmission are presented In addition, the chapter gives overview the Vietnam’s monetary policy framework, in which focuses on the credit market
Chapter 3 presents analytical framework, then develop the model which helps us answer key question Finally, data description as well as steps of economic techniques will be mentioned in this chapter
Chapter 4 shows the empirical results and discussion Finally, results comparison is also presented in this part
Chapter 5 give conclusion, suggests some practical policy implications, and discusses the limitations and direction for further studies.
LITERATURE REVIEW
CREDIT CHANNEL THEORY
Credit channel theory highlights the external finance premium arising from the principal-agent problem between lenders and borrowers Bernanke and Gertler (1995) explored this concept in depth, identifying two key linkages: the bank lending channel and the balance-sheet channel This thesis will focus on these two linkages as they pertain to credit channel theory.
The bank lending channel: concentrates the variability of loan supply through deposit institutions caused by the effect of monetary policy actions
Banks play a crucial role as primary information sources in the economy, effectively addressing asymmetric information issues and other challenges in credit markets This is particularly important for small and medium-sized enterprises, which heavily rely on bank credit As long as this function persists, the significance of the bank lending channel in the transmission of monetary policy remains vital.
When the government implements expansionary monetary policy, it boosts bank deposits and reserves, resulting in an increase in available bank loans This surge in lending leads to higher investment levels, ultimately driving an increase in overall output.
Tightening monetary policy involves lowering bank reserves and customer deposits, resulting in a reduced availability of bank loans This reduction in loans leads to decreased investment spending and a subsequent decline in overall output.
Another side of credit view, when we mention to the impact of monetary policy to enterprises, small firms suffer bad effects on expenditure than large firms (Mishkin,
Small companies often rely heavily on bank loans for their financing needs, while larger companies have the advantage of accessing substantial capital through stock and bond markets, reducing their dependence on traditional banking channels.
Balance sheet channels: focuses influence of monetary policy changing on borrower’s balance sheets and income statement
The variability of monetary policy affects a company's worth, leading to increased adverse selection and moral hazard issues when granting loans Borrowers with lower collateral face a higher likelihood of moral hazard, particularly if they pursue risky projects due to declining equity prices As these firms undertake riskier ventures, the chances of defaulting on loans rise, potentially resulting in bank collapses and decreased lending and investment spending Consequently, banks require more collateral from borrowers, exacerbating adverse selection problems and further limiting available funds for investment.
Here is several ways which monetary policy acts upon on firm’s balance sheet:
Expansionary monetary policy contributes to the rise in equity stakes by mitigating issues of adverse selection and moral hazard As a result, firm valuations improve, leading to increased access to capital for investment, which ultimately boosts aggregate demand.
Expansionary monetary policy leads to lower interest rates, which positively impacts firms' balance sheets by improving cash flow This reduction in interest rates helps mitigate adverse selection and moral hazard issues, resulting in increased capital available for loans Consequently, this stimulates growth in investment spending and boosts aggregate output.
Monetary expansion leads to an unexpected rise in price levels, which boosts company net worth while reducing issues related to adverse selection and moral hazard This process encourages increased investment spending and enhances overall aggregate output.
Contractionary monetary policy leads to a decline in equity prices and reduced cash flow, resulting in lower net worth for businesses This situation exacerbates adverse selection and moral hazard issues, ultimately decreasing investment financing and consumer spending.
MONETARY POLICY FRAMEWORK OF VIETNAM
The State Bank of Vietnam (SBV) serves as the central bank and a governmental body of the Socialist Republic of Vietnam, as outlined in the “Law on the State Bank of Vietnam.” The National Assembly and the government are tasked with decision-making and oversight of monetary policy, including the preparation of annual inflation projections and determining liquidity levels in the economy The government must report on the implementation progress of monetary policy to the National Assembly The SBV is responsible for executing the monetary policy established by the government, managing state monetary and banking activities, and functioning as the currency issuer, bank for credit institutions, and the government’s bank These activities aim to stabilize currency value, preserve banking operations, and align with the country's socialist economic growth objectives.
According to SBV Law, the State Bank of Vietnam (SBV) functions as a government entity, with the National Assembly significantly influencing monetary policy decisions The government's strong involvement, alongside the National Assembly's role, highlights the limitations of SBV's independence in implementing monetary policy (Camen, 2006).
2.2.2 MONETARY POLICY STRATEGY AND INSTRUMENTS
Vietnam's monetary policy strategy is derived from the five-year Social and Economic Development Plan, with the government responsible for creating an actionable implementation plan This plan outlines specific targets for liquidity injection into the economy, as well as key metrics such as M2, credit, and deposits, which are essential components of the government's overall strategy (Camen, 2006).
The State Bank of Vietnam (SBV) plays a crucial role in shaping the country's monetary policy by setting annual targets for total liquidity and credit within the economy, aligned with macroeconomic objectives Each year, the SBV prepares a comprehensive report detailing the implementation of its monetary policy and forecasts for the upcoming year This report is then submitted to the government for review and approval, before being presented to the National Assembly, following consultations with the National Monetary Policy Advisory Board.
Regarding to monetary instruments, a number of indirect tools have been introduced include reserve requirement, refinancing, discount financing facilities, open market operation and foreign exchange interventions
SBV has applied reserve requirement in various forms since 1990s This instrument proves its important role on money market regulating in past Currently, required
The National Monetary Policy Advisory Board (NMPAD) comprises key figures such as the Governor of the State Bank, the Minister of Finance, and various experts Bank reserves are categorized based on deposit maturity, sectoral focus, and currency type (domestic or foreign) Typically, deposits with a maturity of less than a year yield higher returns compared to those exceeding a year, and banks receive interest subsidies when extending credit to the agricultural sector or the People's Credit Fund (Camen, 2006).
In 2008, the reserve requirement was implemented as a crucial measure to control inflation amid significant economic and monetary fluctuations both domestically and internationally In February of that year, the State Bank of Vietnam (SBV) increased the reserve requirement ratio by 1 percentage point for local and foreign currency deposits across most credit institutions However, to mitigate the risk of an economic downturn, the SBV subsequently reduced the required reserve for VND deposits under 12 months twice in 2009, decreasing it from 6% to 5% and then to 3% Notably, the Vietnam Bank for Agriculture and Development further lowered its rate from 3% to 2% and then to 1%.
In 2010, the State Bank of Vietnam (SBV) maintained a low reserve requirement ratio of 3% for VND deposits under 12 months and 1% for those exceeding 12 months Additionally, the SBV reduced the reserve ratio for foreign currency to enable credit institutions to boost their foreign currency funding.
Since July 2000, the State Bank has utilized Open Market Operations (OMOs) as a crucial monetary tool for managing liquidity Over the years, OMOs have proven to be the most significant instrument for stabilizing the money market, effectively aligning with other monetary policy measures In 2008, the State Bank issued compulsory bills with increased base interest rates of 7.5% for 182-day bills and 7.75% for 364-day bills to combat inflation during the first seven months Additionally, in the first half of 2009, the State Bank offered to purchase valuable papers maturing in 14 days, providing short-term capital to support credit institutions in fulfilling their capital needs for economic stimulus programs.
In 2009, the State Bank of Vietnam (SBV) attempted to stimulate the economy but faced challenges due to low demand for funds and an excess of financial resources at the time To support credit institutions during the first nine months of 2010, the SBV conducted open market operations (OMOs) by purchasing valuable papers However, by the end of the three-month period in 2010, rising inflation led to increased interest rates for these papers.
The State Bank of Vietnam (SBV) employs refinancing and rediscount facilities as part of its discount policy, with both rates being similar yet distinct The rediscount rate is determined by the SBV based on collateral such as drafts, promissory notes, and bonds, allowing commercial banks to access funds using these valuable papers as security In contrast, the refinancing rate is linked to loans provided to commercial banks, which then use these loans as collateral for borrowing from the SBV, typically resulting in a higher refinancing rate compared to the rediscount rate Since 2010, the SBV has utilized the refinancing rate to ensure short-term lending and liquidity for credit institutions, primarily offering 1 to 2-month refinancing to support economic liquidity Notably, in late 2010, the SBV implemented refinancing measures to address the increased demand for deposit withdrawals from economic organizations and individuals during the Lunar New Year.
Historically, Vietnam's financial system was predominantly served by the State Bank of Vietnam (SBV) and two state-owned commercial banks (SOCBs) Following the Doi Moi reforms, the SBV became an independent central bank in 1988, focusing on monetary policy and financial supervision The financial landscape evolved further with the establishment of joint-stock commercial banks and the entry of foreign banks in 1991 and 1992, respectively In 2000, the Development Assistance Fund was created to support financial policies Traditionally, Vietnam's financial market was closely linked to non-commercial lending, with a significant emphasis on the agriculture sector, as highlighted in a World Bank report from 2006.
According to the World Bank's 2006 report on Vietnam's capital market, the country's banking sector has experienced rapid growth, primarily driven by loans to the private sector State-owned commercial banks (SOCBs) continue to play a dominant role in providing credit to the economy However, despite ongoing sector reforms, the banking sector remains financially vulnerable and needs strengthening to improve its stability and lending capabilities.
Table 2.1: One decade and Vietnam’s credit
Source: Calculated from IMF-IFS and GSO data
In the banking sector credit, credit to the economy rose from VND 155 trillion in 2000
In 2010, Vietnam's credit supply surged to VND 2,690 trillion, representing 136 percent of its GDP, a remarkable increase from just 35 percent of GDP a decade earlier This rapid credit growth, which escalated dramatically by 50 percent since late 2007, can be attributed to significant capital inflows and the emergence of real estate price bubbles serving as collateral for loans (Vietnam Plus News, 2009).
Vietnam’s credit growth 2011 and orientation in 2012
In 2010, Vietnam's banking system experienced a 10.9 percent growth in total outstanding loans, marking the lowest credit growth rate in a decade, significantly below the revised target of 15-17 percent Meanwhile, deposit growth reached 8.89 percent, and the money supply expanded by an estimated 9.27 percent, according to the State Bank of Vietnam's monthly report.
Vietnam aims for a credit growth of 15-17% and a 14-16% increase in money supply this year, with the Central Bank implementing monetary policy to achieve these targets Nguyen Van Binh, the Governor of the State Bank of Vietnam (SBV), indicated that lending growth might remain below 15% The SBV is prioritizing the development of agriculture, rural sectors, export production, auxiliary industries, and support for small and medium-sized enterprises Agribank, the country's largest lender, allocates 75-89% of its loans to agriculture and rural development Additionally, the SBV has set annual credit growth targets for domestic banks, categorizing them into four groups with maximum loan growth rates of 17%, 15%, 8%, and 0%, respectively.
EMPIRICAL LITERATURE
Recent empirical studies have increasingly focused on the role of credit in the transmission of monetary policy, beginning with Bernanke and Blinder's seminal 1988 paper, "Credit, Money and Aggregate Demand." They utilized the IS/LM model to analyze the impact of money demand shocks and introduced the CC (commodities and credit) curve, which mirrors the IS/LM curve, to explore the credit channel via bank lending during economic shocks By dividing their analysis into two sub-samples (1974:1-1979:3 and 1979:4-1985:4), they found that the impact of money-demand shocks was significantly smaller than that of credit-demand shocks in the first period, while the importance of credit-demand shocks increased notably in the 1980s.
The role of the credit channel in monetary transmission has been a topic of debate since Ramey's 1993 study, which analyzed data from 1954 to 1991 to assess the relative importance of money and credit channels Utilizing a dynamic stochastic general equilibrium model with eight key variables, Ramey concluded that the money channel plays a more significant role than the credit channel in direct policy shock transmission In contrast, Bernanke and Gertler (1995) emphasized the importance of the credit channel as a critical component of the monetary transmission mechanism, detailing its two linkages: bank lending and the balance sheet channel Subsequent research has largely adopted their framework, employing vector autoregression methods to examine the responses to policy shocks and identifying significant costs associated with capital effects in the credit channel.
In his 1999 investigation of Korea's financial crisis, Kim examined the significance of the credit channel in the transmission of monetary policy Utilizing monthly data spanning from January 1993 to May 1998, he employed a combination of three methodologies, including a narrative approach and disaggregated bank analysis, to derive his findings.
Bernanke and Blinder characterized the monetary transmission mechanism as a "black box" in their journal, utilizing a disequilibrium model to examine the bank lending channel They employed standard vector autoregression for econometric specification to assess the significance of loan supply Their findings offered compelling evidence of the critical role of the credit channel in the aftermath of the financial crisis.
Numerous researchers have explored similar topics over the years, leading to diverse results due to varying country characteristics Warner and Georges (2001) introduced a novel approach to testing the credit view of monetary transmission by analyzing stock market returns, specifically estimating abnormal returns for daily stock market data of U.S manufacturing firms Their findings indicated no consistent relationship between abnormal stock returns and credit constraints during both recessionary (1990-1991) and expansionary (1993-1994) periods Similarly, Suzuki (2004) examined the lending view in Australia from 1985:Q1 to 2000:Q2, concluding that the lending channel was less influential due to specific behaviors of Australian banks Additionally, Lown and Morgan (2002) and Disyatat and Vongsinsirikul (2003) investigated credit effects in the U.S and Thailand economies, respectively, with Lown and Morgan utilizing bank commercial credit standards as a proxy for credit availability and employing a vector autoregression (VAR) model for their analysis.
This study categorizes the loan market into two distinct forms: the classical market and the augmented market Through market discrimination, the authors highlight the significant role of credit standards in the U.S economy The empirical findings reveal that the dynamics of commercial credit standards have a substantial impact on both loans and economic output Utilizing a typical econometric methodology, specifically the VAR model, researchers Disyatat and Vongsinsirikul effectively engaged with these concepts to derive their results.
3 The economists who supported the view: bank loans play important role in monetary policy transmission mechanism.
Lown and Morgan (2002) explored the enigmatic nature of the credit effect, analyzing key variables in the Thai economy, including real output, the Consumer Price Index (CPI), and the 14-day repurchase rate from Q1 1993 to Q4 2001 Their findings highlighted the significant impact of monetary shocks on investment, emphasizing the crucial role banks play as facilitators in the effective implementation of monetary policy.
Charoenseang and Manakit (2006) found that in Thailand, after the implementation of inflation targeting, the transmission of monetary policy was primarily driven by the credit channel rather than the interest rate channel during June.
From 2000 to July 2006, the Thai financial market heavily relied on bank lending as a primary source of capital for economic activities This period underscored the crucial role that commercial bank lending plays in supporting the Thai economy.
In the same year, Podpiera (2007) had employed commercial banks data to study the impact of monetary policy shocks on loan market in Czech case; meanwhile Kubo.A
In 2007, research focused on the credit channel as a key aspect of the monetary transmission mechanism in Thailand, emphasizing its significant role during the study period Empirical studies, including Podpiera's analysis based on the Kashyap and Stein model using balance sheet data from Czech banks between 1996 and 2001, demonstrated that changes in monetary policy influenced loan growth rates, particularly from 1999 to 2001 Kubo employed a structural vector autoregression (SVAR) approach to assess the effects of exogenous monetary policy shocks on domestic macroeconomic indicators, analyzing monthly data from May 2000 to December 2006 across five variables: consumer price index, industrial production, producer price index, inter-bank overnight lending rate, and private credit aggregates The findings indicated that the Bank of Thailand's success was largely attributed to the credit channel's contribution during that period, while also revealing that monetary policy shocks negatively impacted import demand in the context of international variables within the SVAR framework.
Balazs Egert (2009) explored the effectiveness of research within the monetary transmission mechanism, particularly in Central and Eastern Europe The study revealed that a decrease in the inflation rate significantly influenced the reduction of exchange rate transmission over time It identified the credit channel as a crucial element in monetary policy transmission, while the asset price channel lacked strength in stagnant stock and bond markets Similarly, the work of Balazs Egert, along with Fiorentini and Tamborini (2001), highlighted the critical role of credit supply in Italy's monetary policy, emphasizing its importance to policymakers based on research from the past decade.
This article explores the monetary policy transmission in India, highlighting the significance of the bank lending channel as identified by Abdul (2009) through a VAR model that analyzed macroeconomic variables such as bank rate, repo rate, and reserve repo rate The study emphasizes the necessity of including the Federal Reserve's rate when examining monetary shocks, given its considerable impact on emerging economies like India Additionally, Catão and Pagan (2010) utilized an expectation-augmented SVAR model to investigate monetary transmission in Brazil and Chile, drawing data from reputable sources like the IMF's International Financial Statistics and the Central Bank of Chile Their findings underscore the critical role of the bank-credit channel in emerging markets, revealing that typical credit shocks can have substantial effects on output and inflation, particularly in Chile, where the banking system has a higher penetration.
Research on the role of credit in monetary policy transmission in Vietnam is limited Notably, Hung and Pfau (2008) explored this topic by analyzing the monetary transmission mechanism using a vector autoregression (VAR) approach Their study highlighted the weak connections between monetary policy and various channels in Vietnam, revealing that the credit and exchange rate channels are more significant than the interest rate channel.
This chapter delves into the core thesis by exploring the credit channel, highlighting that credit theory is influenced by the external finance premium It discusses how monetary policy shocks can affect the loan market through both the bank lending channel and the balance-sheet channel In the context of Vietnam's monetary policy framework, the National Assembly holds the authority over monetary policy activities, while the State Bank of Vietnam (SBV) implements these policies Key monetary instruments in Vietnam include reserve requirements, open market operations (OMOs), and discount policies The empirical literature largely acknowledges the significant role of the credit channel, although some studies present findings that contradict this perspective.
CHAPTER 3: MODEL SPECIFICATION AND DATA
This section presents the analytical framework that outlines the methodology utilized in this thesis It introduces the VAR model as the primary tool for examining the impact of the credit channel on monetary policy transmission in Vietnam Additionally, it provides a concise overview of the estimation steps implemented throughout the research.
ANALYTICAL FRAMEWORK
Rely on theory and empirical studies, an analytical framework is conducted below
Vietnam's government employs new monetary policies through adjustments in the money supply, which impact the macroeconomy via four channels: interest rate, exchange rate, asset rate, and credit channels However, due to the inexperience of Vietnam's stock market, the asset channel is not analyzed in this study Historically, Vietnam has maintained strict capital mobility, with capital flows primarily influenced by interest rates, leading to an imperfect exchange rate channel This thesis focuses on the interest rate and credit channels, emphasizing the significance of the credit channel in monetary policy transmission Two market types are examined: a classical market without credit and an augmented market with credit, using a VAR model to conduct Granger causality, impulse response, and variance decomposition tests The findings reveal distinct differences between the two markets and highlight the critical role of the credit channel in the monetary policy transmission mechanism.
MODEL SPECIFICATION
To achieve the thesis objective, a Vector Autoregression (VAR) model will be developed to investigate the significance of the credit channel in the transmission of monetary policy in Vietnam.
According to Stock and Watson (2001), Vector Autoregressions (VARs) are classified into three types: reduced form, recursive, and structural This thesis employs the VAR approach, specifically emphasizing the reduced form as an effective method to achieve its objectives.
A reduced form represents each variable as a linear function of its own historical values and the historical values of all other variables This approach allows for the derivation of a reduced form of the Vector Autoregression (VAR) model.
Yt is an m-dimensional vector of endogenous variables
(L) denotes vector polynomial of lag operator with optimal lag order;
t is assumed to be vector white noise residual
The error term (εt) in regression represents the unpredictable fluctuations in variables after considering their past values Each equation is estimated individually using ordinary least squares, and the optimal number of lagged values is determined through various methods.
This thesis utilizes quarterly data from 1996:Q1 to 2010:Q3, due to the limited availability of historical data in Vietnam The selected macroeconomic variables include M2, customer price index, domestic credit, real industrial output, refinancing rate, and lending rate, which collectively provide a comprehensive overview of Vietnam's economy The rationale for choosing these variables is supported by the findings of Romer and Romer (1990), which indicate that a decrease in reserves can limit loan availability, often resulting from contractionary monetary policy M2 is emphasized as a key measure of money supply and policy shocks in the Vietnamese context, while the refinancing rate serves as an essential tool for the State Bank of Vietnam to implement tighter monetary policy Detailed definitions and data sources for these variables are presented in Table 3.1.
The customer price index plays a crucial role in understanding inflation, which is a key predictor of economic output To accurately assess the supply of loans in the domestic economy, it is essential to incorporate domestic credit variables into the model Additionally, the lending rate is a vital element in the policy transmission mechanism While GDP is commonly used to gauge economic growth, Vietnam's GDP data has only been available since 2000; therefore, real industrial output serves as a suitable proxy This approach was also utilized in the VAR model by Hung and Pfau (2008) to analyze monetary transmission in Vietnam It is important to control for spurious regression among all variables, as Asteriou and Hall (2007) noted that most macroeconomic time series exhibit trends and are non-stationary.
Based on the empirical study by Lown and Morgan (2002), which explored the role of the credit channel in the U.S., the loan market is classified into two scenarios: a classical market that considers quantity and price, and an augmented market that includes credit We will apply this framework to the Vietnamese context, analyzing a classical market that encompasses the customer price index, money-quasi, industrial output, refinancing rate, and lending rate, alongside an augmented market that incorporates credit as a proxy for loan supply or domestic credit to the economy.
DATA SOURCES
M2denoted broad of money stock and are defined by formula:
In 2009, IFS defined M2 as the total of money supply (M1) along with savings and time deposits in the national currency, as well as demand deposits in foreign currencies, excluding those held by the central government, and maintained with other depository corporations.
The Consumer Price Index (CPI) is calculated based on data from the 37 largest provinces in Vietnam, representing eight economic regions The weighting for this index is derived from the 2004 Vietnam Household Living Standard Survey, as noted by the IMF in 2009.
CREDIT denoted domestic credit It is the sum of credit to the nonfinancial public sector, credit to private sector and other account
OUTPUT denoted real industrial output As already represented, Vietnam’s industrial output is used as proxy for GDP, due to data limited
REFIN symbolized refinancing rate That is the rate charged by the State Bank of
Vietnam on its lending to facilities to all credit institution (IMF world and country noted, 2009)
M2 Broad money stock IFS-IMF
CPI Customer price index IFS-IMF
CREDIT Domestic credit to economy IFS-IMF
OUTPUT Real industrial output Vietnam GSO
REFIN Refinancing rate IFS-IMF
LR Lending Rate IFS-IMF
The LR, or lending rate, represents the average rates applied at the end of a period for short-term working capital loans issued by four major state-owned commercial banks, as noted by the IMF in 2009.
Excepting for output that is extracted from Vietnam General Statistic Office, these variables are taken from the International Monetary Fund’s (IMF) International Financial Statistic (IFS).
STEPS OF ESTIMATION
Stock and Watson (2001) stated that due to the complicated dynamics in the VAR, below statistics are more informative than estimated VAR regression coefficients or R 2 statistics
Stationary time series are crucial in econometric analysis, as non-stationary series can lead to misleading or "spurious" regression results, according to Gujarati (2003) Therefore, it is essential to conduct stationarity tests on all variables prior to applying a VAR model to ensure valid and meaningful interpretations of the data.
Several formal statistical tests address the unit-root problem, with the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests being particularly favored These tests incorporate additional lagged terms of the dependent variable to effectively eliminate autocorrelation.
The Granger causality test serves as the second step in the regression of a VAR model and is widely utilized in economic policy analysis This test is an effective method for assessing the significance of coefficients, allowing for accurate predictions of Xt based on its past values.
Yt variable rather than not using such past value, or causality represents the ability of one variable to predict another variable
Impulse response and variance decomposition are essential tools in econometrics that illustrate how current and future values of a variable respond to a one-unit increase in the current value of a VAR error, while keeping other errors constant This analysis includes ± 1 standard error bands, providing an approximate 66 percent confidence interval for each impulse response Additionally, the forecast error decomposition method reveals the percentage of variance in forecasting a variable attributed to specific shocks over a defined horizon (Stock and Watson, 2001).
FINDING AND DISCUSSION
DESCRIPTIVE STATISTIC
This part reports the descriptive statistic of all variables of original data, also changed
It summarizes the mean, median, max, min, standard deviation and count of each variable
Table 4.1: Description statistic of variables Variables Mean Median Maximum Minimum Std Dev Obs
Source: Calculated from IMF-IFS and GSO data
The high standard deviation of CPI, CREDIT, M2, and OUTPUT, along with the significant spread between their maximum and minimum values, indicates considerable volatility during this period (see Table 4.1) Consequently, the estimation results based on their original values may be unreliable However, by transforming these variables into logarithmic form and multiplying by 100, or by estimating them in percentage changes, the standard deviation significantly decreases, enhancing the reliability of the data.
Figure 1a and 1b in the appendix provide an overview of the original data presented in this thesis Both domestic credit supply and quasi-money exhibit a similar upward trend, particularly accelerating after 2007, while incremental output has shown little variation As discussed in Chapter 2, the significant increase in credit can be attributed to substantial capital inflows and the emergence of real estate price bubbles.
UNIT ROOT TESTS
Asteriou and Hall (2007) emphasize that macroeconomic variables typically exhibit trends The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests both indicate that all examined variables are non-stationary, as shown in Tables 4.2 and 4.3.
Table 4.2: Augment Dickey-Fuller test
Variables Exogenous t-statistic p-value Data in Level
Source: Calculated from IMF-IFS and GSO data
The data analyzed are stationary, except for the CL_CPI variable, which did not meet the criteria for stationarity in the ADF test but did satisfy the conditions in the PP test Consequently, these data can be utilized to derive key insights for the thesis.
Variables Exogenous t-statistic p-value Data in Level
Source: Calculated from IMF-IFS and GSO data
Concern to the optimal lag problem for VAR model, different criteria are used to determine
Table 4.4: Optimal lap-Classical market
Lag LogL LR FPE AIC SC HQ
Source: Self calculation by using Eviews
Table 4.5: Optimal lap-Augmented market
Lag LogL LR FPE AIC SC HQ
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
HQ: Hannan-Quinn information criterion
According to Tables 4.4 and 4.5, several statistical methods are available for selecting the optimal lag length in our model, with the Akaike Information Criterion (AIC) and Schwarz Criterion (SC) being the most significant To minimize the information criteria, the optimal number of lags determined is five for both markets, leading to the decision to use five lags when applying the Vector Autoregression (VAR) model.
VAR REGRESSION STATISTICS FOR CLASSICAL AND AUGMENTED
Table 4.6 presents regression statistics for VAR in a classical market, showing p-values from the VAR Granger Causality test with five lags The null hypothesis posits that the independent variable does not cause the dependent variable Notably, lagged values of most variables significantly predict output at the 5 percent level, except for the lending rate, which is significant at the 10 percent level While the lending rate does not predict money-quasi, it is useful for forecasting the consumer price index, output, and refinancing rate at the 10 percent level The refinancing rate offers limited predictive power for other macro variables, except for output Interestingly, money supply does not Granger cause the lending rate but is useful for predicting output, price level, and refinancing rate at the 10 percent level Historically, from the late 1980s to 2000, Vietnam's interest rate mechanism aimed to maintain a positive real interest rate to control inflation This objective was achieved, with instances where deposit rates exceeded lending rates Since August 2000, the SBV has implemented a base interest rate mechanism to regulate lending rates However, lending rates in Vietnam remain non-liberalized, failing to reflect true market demand and supply dynamics The lack of a strong correlation between lending rates and money supply indicates potential gaps in the classical market framework.
Table 4.6: VAR Regression Statistic- Classical market
Dependent Variable Independent vars CL_CPI CL_LR CL_M2 CL_OUTPUT CL_REFIN
Source: Calculated from IMF-IFS and GSO data
Table 4.7: VAR Regression Statistic- Augmented market Augmented
CL_CPI CL_CREDIT CL_LR CL_M2 CL_OUTPUT CL_REFIN
Source: Calculated from IMF-IFS and GSO data
Note: The reported are p-value
The augmented credit market reveals that lagged credit values are highly significant in predicting money supply, with a p-value of zero However, credit does not Granger cause output or price levels, indicated by p-values of 0.67 and 0.19, respectively This suggests that the State Bank of Vietnam primarily utilizes credit to inject liquidity into the money market Price levels are also effective in forecasting money supply at a 5 percent significance level Although the lending rate does not predict output or significantly Granger cause money policy shocks or credit (with a p-value of 0.1), this marks an improvement compared to previous findings, highlighting the importance of credit Additionally, price levels and refinancing rates together Granger cause output, while output and lending rates can predict credit at a 10 percent significance level; conversely, M2 does not effectively forecast either credit or output.
Regression statistics reveal valuable insights into monetary transmission and lending channels The VAR results suggest that traditional market models may inadequately explain these mechanisms, while an augmented model that includes credit significantly enhances the forecasting of money supply Additionally, while the lending rate appears less influential in models excluding credit, its impact is notably strengthened when credit is included Furthermore, price level and lending rate emerge as the second and third most important variables for predicting money supply, beyond the influence of credit.
IMPULSE RESPONES AND VARIANCE DECOMPOSITIONS
This analysis focuses on the impact of monetary policy shocks and credit variables on the macroeconomy, particularly in Vietnam By examining changes in monetary policy through shocks to the money supply, specifically M2, we can better understand these dynamics.
Figure 4.1: The impulse response functions for classical market
Response of CL_CPI to CL_CPI
Response of CL_CPI to CL_LR
Response of CL_CPI to CL_M2
Response of CL_CPI to CL_OUT PUT
Response of CL_CPI to CL_REFIN
Response of CL_LR to CL_CPI
Response of CL_LR to CL_LR
Response of CL_LR to CL_M2
Response of CL_LR to CL_OUT PUT
Response of CL_LR to CL_REFIN
Response of CL_M2 to CL_CPI
Response of CL_M2 to CL_LR
Response of CL_M2 to CL_M2
Response of CL_M2 to CL_OUT PUT
Response of CL_M2 to CL_REFIN
Response of CL_OUT PUT to CL_CPI
Response of CL_OUT PUT to CL_LR
Response of CL_OUT PUT to CL_M2
Response of CL_OUT PUT to CL_OUT PUT
Response of CL_OUT PUT to CL_REFIN
Response of CL_REFIN to CL_CPI
Response of CL_REFIN to CL_LR
Response of CL_REFIN to CL_M2
Response of CL_REFIN to CL_OUT PUT
Response of CL_REFIN to CL_REFIN Response to Cholesky One S.D Innov ations ± 2 S.E.
Source: Calculated from IMF-IFS and GSO data
The monetary policy shock shows a significant decline, dropping from 4.46 in the first quarter to 0.006 in the third quarter, indicating a substantial tightening of policy Output adjusts with a lag of one quarter, fluctuating more than M2 The findings suggest that output is sensitive to monetary shocks, although not in a one-to-one manner, with a trough reaching 1.32 percent below pre-shock levels before recovering growth in the fourth quarter The lending rate positively responds to the monetary shock after one quarter, peaking at 2.2 percent in the sixth quarter, but subsequently declines over the next three quarters while M2 remains tight Additionally, the price level reacts slowly to M2 shocks, responding only after the fifth quarter, and the refinancing rate also shows a one-quarter lag in response to monetary shocks, peaking at 3.8 percent.
The money supply shows a positive response to a decrease in lending rates after one quarter, albeit for a short duration According to economic theory, contractionary monetary policy leads to higher lending rates, which in turn restrains investment, ultimately reducing aggregate demand and output However, this expected response does not always occur consistently.
Historically, the relationship between two variables has shown abnormal patterns, particularly with M2 continuously increasing while lending rates remain stagnant Notably, in 1998, lending rates were constrained at 14.4% despite ongoing M2 growth This trend reemerged from Q3 2002 to Q3 2008, attributed to poor credit conditions in real estate and financial investments (Dung, 2010) The response of output to lending rate shocks occurs after the second quarter but lasts only one quarter Conversely, output shocks lead to a strong reaction in money supply after just one quarter.
These reactions are consistent with the regression statistic; output is highly sensitive to monetary shock; the impact of lending rate shock on money supply is somewhat weak
5 Response of all variables represent the cumulative percentage change following the shock
Figure 4.2: The impulse response functions for augmented market
Response of CL_CPI to CL_CPI
Response of CL_CPI to CL_CREDIT
Response of CL_CPI to CL_LR
Response of CL_CPI to CL_M2
Response of CL_CPI to CL_OUTPUT
Response of CL_CPI to CL_REFIN
Response of CL_CREDIT to CL_CPI
Res ponse of CL_CREDIT to CL_CREDIT
Response of CL_CREDIT to CL_LR
Response of CL_CREDIT to CL_M2
Res ponse of CL_CREDIT to CL_OUTPUT
Respons e of CL_CREDIT to CL_REFIN
Response of CL_LR to CL_CPI
Response of CL_LR to CL_CREDIT
Response of CL_LR to CL_LR
Response of CL_LR to CL_M2
Respons e of CL_LR to CL_OUTPUT
Response of CL_LR to CL_REFIN
Response of CL_M2 to CL_CPI
Response of CL_M2 to CL_CREDIT
Response of CL_M2 to CL_LR
Response of CL_M2 to CL_M2
Respons e of CL_M2 to CL_OUTPUT
Response of CL_M2 to CL_REFIN
Response of CL_OUTPUT to CL_CPI
Res ponse of CL_OUTPUT to CL_CREDIT
Respons e of CL_OUTPUT to CL_LR
Respons e of CL_OUTPUT to CL_M2
Res pons e of CL_OUTPUT to CL_OUTPUT
Res ponse of CL_OUTPUT to CL_REFIN
Response of CL_REFIN to CL_CPI
Respons e of CL_REFIN to CL_CREDIT
Response of CL_REFIN to CL_LR
Response of CL_REFIN to CL_M2
Res ponse of CL_REFIN to CL_OUTPUT
Respons e of CL_REFIN to CL_REFIN Response to Cholesky One S.D Innovations ± 2 S.E.
Source: Calculated from IMF-IFS and GSO data
In the augmented market, credit shocks lead to an initial expansion of 4.9, which quickly declines to (1.39) by the second quarter, with tightened credit persisting for twelve quarters The output reacts strongly after just one quarter, plummeting from 1.1% to (1.9%) by the fourth quarter following the credit shock, before recovering and fluctuating within a narrow range for the remainder of the year Additionally, M2 responds immediately and sharply to these credit shocks, dropping from 3% to (0.5%) within one quarter and maintaining this decline over the twelve-quarter period.
Lending rate is slow reactions to credit shocks, because of specific characteristic of Vietnam’s credit market as mentioned above
In response to money shocks, credit shows a minimal and persistent reaction as M2 shifts from easing to tightening during the first two quarters Output begins to respond to M2 shocks in the second quarter, reaching a low of 0.7 percent by the fourth quarter The refinancing rate reacts to monetary policy tightening within two quarters, while the price level's response is notably sluggish, taking five quarters to adjust.
Credit responses to lending rate shocks are slow, as indicated by the data in the second row and third column After four quarters, credit reacts to an increase in lending rates, but then shows a decline Meanwhile, output responds to lending rate changes within one quarter and continues to fluctuate over the next eight quarters.
In summary, credit shocks significantly impact monetary policy, leading to reduced output and prolonged effects on lending rates Conversely, monetary policy shocks result in decreased output, higher refinancing rates, and a slight decline in price levels.
In analyzing the variance decomposition of M2 across two markets, significant differences emerge, particularly in the magnitude of shocks In the classical market, M2 accounts for over 50% of the variance in its error, whereas the augmented market shows a different pattern This trend persists across various horizons, gradually diminishing over time At the 13-quarter horizon, credit shocks contribute 21.5% to the forecast error variance of M2, compared to 15.7% when considering M2 alone Notably, credit shocks dominate in the first quarter, accounting for over 50% of the error in predicting money-quasi Additionally, price level shocks play a crucial role in forecasting M2 within the credit market Following credit, refinancing rate shocks contribute 20% after nine quarters in the augmented market, while their impact is less pronounced in the classical model Although lending rate shocks have gained some importance in the augmented market, they remain weaker than other factors Furthermore, the output response is slightly lower in the credit market, registering 8.4% compared to 9.2% in the classical market after nine quarters.
The variance decomposition of output reveals that M2 shocks have a smaller impact on the credit market, accounting for 19.9 percent initially and decreasing to 5.1 percent after nine quarters, with the disparity growing over longer periods In an augmented market that includes the credit variable, the contribution of lending rates to output variance decreases from 20 percent to 14 percent after five quarters Notably, credit shocks represent 18.9 percent of the forecast error in output at thirteen quarters, highlighting the significant and continuous role of credit shocks in predicting output errors.
The study reveals that output and price level are the primary factors influencing credit forecast errors, with respective contributions of 15.2% and 14.8% over a 13-quarter horizon In contrast, M2 shocks have a minimal impact, accounting for only 3.5% of credit error predictions after the same period Notably, credit shocks contribute nearly half of the forecast error for credit itself across thirteen quarters Additionally, the presence of credit variables significantly alters the decomposition of lending rate shocks, with credit shocks responsible for approximately 30% of the errors in predicting lending rates, although this influence diminishes over longer horizons.
Table 4.8: Variance Decompositions for vector autoregression for Classical and Augmented Market
Sources: Calculated form IMF-IFS and GSO data
TIEU LUAN MOI download : skknchat@gmail.com
Empirical results reveal that in VAR regression analysis, neither the lagged lending rate nor the refinancing rate effectively predicts money supply, indicating potential deficiencies in classical market models Conversely, credit emerges as a significant predictor of money supply in augmented markets Additionally, impulse response and variance decomposition analyses show that the relationship between money supply and lending rate shocks can be inconsistent in markets lacking credit, while M2 responds quickly and strongly to credit shocks Overall, credit shocks are crucial for forecasting errors in money supply, with varying magnitudes and orders among macroeconomic variables when credit is present in augmented markets, highlighting the influence of credit and price levels on output and M2.
RESULTS COMPARISON
This study will compare the findings of the thesis with relevant research conducted in Vietnam and other countries, highlighting both similarities and differences By doing so, we aim to identify the unique contributions of this thesis to the existing body of knowledge.
Our findings validate the existence of the credit channel and its significance in the context of Vietnam, aligning with the research conducted by Bernanke and Gertler (1995) The empirical results of this study are consistent with the majority of previous studies that utilized the VAR model as a primary analytical approach, including the work of Kim.
(1999), Lown and Morgan (2002), Disyatat and Vongsinsirikul (2003), Abdul (2009); and structure vector autoregression model such as Kubo.A (2007), Catão and Pagan
(2010) that credit channel plays important role in monetary transmission mechanism Once again, this study is in line with Podpiera (2007), Charoenseang J and Manakit P
A study conducted in 2006 reaffirmed the significance of the credit channel in Thailand and Chile, despite the differences in countries and econometric methods used Similarly, research on Vietnam supports the findings of Hung and Pfau (2008), concluding that the credit channel is more crucial than the interest rate channel, making it the most significant channel in Vietnam's financial landscape.
However, the study’s finding conflicts with several researches, such as Ramey (1993), Suzuki (2004) when their empirical provides evidence the insignificant role of credit in transmission of monetary policy
In general, despite of different market conditions and Vietnam’s specific characteristic, the empirical finding still has same results to majority relevant studies.
CONCLUSION AND POLICY IMPLICATION
CONCLUSIONS
This thesis investigates the role of credit in the monetary transmission mechanism in Vietnam from Q1 1996 to Q3 2010, utilizing data primarily sourced from IFS-IMF, with the exception of industrial output Employing a VAR model with a reduced form, the study differentiates between a classical market (without credit) and an augmented market (with credit) to analyze the credit channel's impact Initial steps include conducting stationary and unit-root tests to determine suitable data for the model, followed by selecting optimal lags based on the minimum AIC criteria The VAR Granger-causality test assesses the causal relationships between money supply, credit variables, and other relevant factors Impulse response functions are utilized to observe the effects of shocks on each variable, while variance decomposition evaluates the contribution of each variable to forecast errors of monetary shocks over a specified horizon The estimated results provide insights into these dynamics.
In classical markets, there are no independent variables that exhibit Granger causality with money supply, which primarily predicts most dependent variables except for the lending rate This limitation suggests that VAR regression statistics may not adequately explain the monetary transmission mechanism in such markets Conversely, in augmented markets, domestic credit plays a significant role in predicting money supply, alongside price levels and lending rates, which gain predictive power in this context However, the lagged value of money supply does not forecast credit, output, or lending rates Thus, incorporating credit variables in augmented markets highlights the critical importance of the credit channel in the transmission of monetary policy.
In a classical market, the output and refinancing rates respond to monetary policy shocks, such as tightening, after a one-period lag Output is notably sensitive to monetary shocks, while the lending channel's reaction can be short-lived and inconsistent In credit markets, the output's response to credit shocks is robust and occurs after a lag, whereas M2 reacts immediately and strongly, aligning with the findings of Bernanke and Gertler Additionally, the correlation between the lending channel and the credit channel is relatively weak.
In the variance decomposition of M2, a significant distinction exists between two markets, with over fifty percent of the forecasting error attributed to credit shocks in the augmented market during the first quarter Both credit and price level shocks significantly influence the variance decomposition of M2, although their impact diminishes over longer time horizons Additionally, there is a notable difference in both the magnitude and the order of variance decomposition of M2 in markets characterized by credit.
Since those findings, the study agrees with Bernanke and Gertler view that credit channel played as important channel in monetary transmission mechanism in Vietnam case.
POLICY IMPLICATION
The study highlights significant implications for policymakers regarding the credit channel and monetary transmission mechanism The findings demonstrate the crucial role that the credit channel plays in the effectiveness of monetary policy transmission.
Vietnam; hence; in order to regulate the economy development through reasonable monetary policy at each period, some recommendations are given below:
The credit channel is vital for the monetary transmission mechanism in Vietnam, necessitating careful oversight of the credit sector during the implementation of new monetary policies Changes in government policy, whether shifting from a loose to a tight monetary stance or vice versa, have a direct and significant impact on this channel The State Bank of Vietnam (SBV) plays a crucial role in regulating credit flow within the economy through its various instruments As the central bank responsible for overseeing commercial banks, the SBV should provide informed recommendations and practical policies to the government To enhance the effectiveness of these tasks, it is essential to grant the SBV greater autonomy in decision-making.
Monetary policy tightening leads to immediate and significant responses in credit supply, causing a sharp decline in output, particularly after a single lag This process affects the credit channel through two main sub-channels: the bank lending channel and the balance sheet channel, ultimately resulting in reduced output due to credit shortages for manufacturing enterprises To mitigate excessive economic growth without negatively impacting the production sector, especially in an agriculture-focused country like Vietnam, it is crucial to implement support programs for these enterprises While the Vietnamese government has introduced various subsidy programs in recent years, there remain significant gaps and challenges in their execution Therefore, effective control and supervision by the government are essential to ensure the success of these policies.
The relationship between lending channels and monetary policy in Vietnam often deviates from theoretical expectations due to various factors, including the underdevelopment of the country’s financial market Despite some degree of self-regulation in credit operations and interest negotiations, commercial banks remain reliant on the State Bank of Vietnam (SBV) for lending decisions Therefore, it is crucial to explore alternative solutions for lending rates, emphasizing the need for a gradual marketization of the lending mechanism.
Variance decomposition analysis indicates that credit growth and price levels significantly influence the behavior of money-quasi, particularly in the short run This highlights the importance of managing credit growth effectively, as it is essential for financing development projects rather than speculative activities, which can lead to bubbles and bad debts.
Monetary policy must be implemented with caution and flexibility to improve the effectiveness of money stock control To fulfill this responsibility, the State Bank of Vietnam (SBV) should closely monitor and anticipate changes in both domestic and global financial markets to timely enforce suitable policies.
Understanding the critical role of credit channels in the monetary transmission mechanism allows us to mitigate the negative impacts of newly implemented policies Successful implementation of these measures is essential for significantly enhancing the health of Vietnam's financial market.
LIMITATION AND FURTHER STUDIES
Although, this study has answered all the key questions about the role of credit in monetary transmission mechanism in Vietnam case, it also contains some limitations
The availability of data resources in Vietnam is notably limited, restricting the study's access to longer time periods Since Vietnam's GDP data has only been available since 2000, the research utilizes industrial output value as a proxy for GDP Additionally, following Lown and Morgan's methodology, the study employs domestic credit value as a substitute for the standard variable representing net percentage tightening.
VAR models serve as essential tools for understanding the impact of credit on monetary transmission They have significantly enhanced the toolkit of macroeconometricians, enabling them to effectively describe data and produce reliable multivariable benchmark forecasts (Stock and Watson, 2001).
This study utilizes impulse response and variance decomposition within a VAR model to analyze the impact of credit and monetary transmission using quarterly data from 1996:Q1 to 2010:Q3 Future research could enhance the analysis by employing monthly data over an extended period to better understand the correlation and interactions between these variables Additionally, incorporating different models, such as the vector error correction (VECM) model, would be beneficial to determine if the initial conclusions remain valid in the context of Vietnam.
Besides that, the study could add several variables such as Federal Funds rate, exchange rate to exploit how the changes of estimated result with joined new variables
6 The number of banks tightening less the number easing, divided by the number reporting (Lown and Morgan,
The credit plays important role in monetary transmission Hence, potential study may expand by consider the determinants of domestic credit in Vietnam case.
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