1. Trang chủ
  2. » Luận Văn - Báo Cáo

The determinants of net interest margins in asean banks in the period 2008 – 2012

84 6 0

Đang tải... (xem toàn văn)

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

THÔNG TIN TÀI LIỆU

Thông tin cơ bản

Tiêu đề The Determinants Of Net Interest Margin In Asean Banks In The Period 2008 - 2012
Tác giả Van Thi Thanh Nhan
Người hướng dẫn Dr. Nguyen Trong Hoai
Trường học University of Economics
Chuyên ngành Development Economics
Thể loại Thesis
Năm xuất bản 2014
Thành phố Ho Chi Minh City
Định dạng
Số trang 84
Dung lượng 385,99 KB

Cấu trúc

  • HO CHI MINH CITY – DECEMBER 2014

  • VIETNAM – NETHERLANDS

  • VAN THI THANH NHAN

  • Dr. NGUYEN TRONG HOAI

  • CERTIFICATION

    • VAN THI THANH NHAN

  • ACKNOWLEDGEMENT

    • VAN THI THANH NHAN

  • LIST OF FIGURES

  • LIST OF TABLES

  • ABBREVIATIONS

  • ABSTRACT

  • CHAPTER 1: INTRODUCTION

    • 1.1 PROBLEM STATEMENTS:

    • 1.2 RESEARCH OBJECTIVES The goal of the study:

    • Specific objectives:

    • 1.3 RESEARCH QUESTIONS

    • 1.4 RESEARCH SCOPE

    • 1.5 RESEARCH STRUCTURE

  • CHAPTER 2: LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

    • 2.1 LITERATURE REVIEW FOR INTEREST MARGINS

    • 2.1.1 DEFINITION OF NET INTEREST MARGIN:

    • 2.1.2 DETERMINANTS OF NIM

    • 2.1.2.1 RELATED LITERATURE:

    • 2.1.2.2 THE MACROECONOMIC FACTORS:

    • 2.1.2.3 THE BANK SPECIFIC FACTORS:

    • Bank size

    • Credit risk

    • Capital adequacy

    • Liquidity risk

    • Operating cost

    • Implicit interest payments:

    • Managerial efficiency

    • 2.1.2.4 THE BANKING MARKET FACTOR:

      • Table A: the summary of main literatures review

    • 2.2 THE SUGGESTED RESEARCH APPROACH:

    • ?

    • + ? +

    • 1 ? (? )

    • ? (? )

    • + (? + ? )??

      • 2.3. THE CONCEPT FRAMEWORK:

      • SUMMARY CHAPTER 2

  • CHAPTER 3: RESEARCH METHODOLOGY AND

    • 3.1. IDENTIFICATION OF VARIABLES:

    • 3.1.1. THE DEPENDENT VARIABLE:

    • 3.1.2. THE INDEPENDENT VARIABLES AND HYPOTHESIS TESTING:

    • 3.1.2.1 THE MACROECONOMIC FACTORS:

      • Hypothesis 1: Economic growth (GDP) is expected a positive significant impact on NIM. The greater economic growth will have a higher net interest margins.

      • Hypothesis 2: Inflation rate is expected that there is a positive effect on bank interest margin.

    • 3.1.2.2 THE BANKING MARKET FACTOR:

      • Hypothesis 3: HHI is expected that there is a positive impact on net interest margins.

    • 3.1.2.3 THE BANKING SPECIFIC FACTORS:

      • Hypothesis 4: It is expected that scale of bank will effect on net margins significantly by a negative relationship.

      • Hypothesis 5: Author expects that liquidity risk will have a negative effect on net interest margin.

      • Hypothesis 6: It is expected that credit risk will have positive impact on bank margins.

      • Hypothesis 7: It is expected that capital adequacy will effect on NIM positive significantly.

      • Hypothesis 8: Operating cost is expected that there is a positive impact on net interest margins.

      • Hypothesis 9: It is expected that implicit payment will have positive effect on net margins.

      • Hypothesis 10: Managerial efficiency is expected that it can effect on NIM negative significant.

    • 3.2. DATA COLLECTION AND EXPECTED RESULTS:

    • 3.3. THE RESEARCH METHODOLOGY

    • 3.3.1. THE MODEL:

    • 3.3.2 THE ESTIMATION METHOD

    • 3.3.2.1 FIXED EFFECTS MODEL:

    • 3.3.2.2 RANDOM EFFECTS MODEL:

    • Where:

    • 3.3.2.3 SELECTING THE APPRIPEATE MODEL:

    • 3.4 THE OUTLINE OF ESTIMATION METHOD:

  • CHAPTER 4: DATA ANALYSIS AND DISCUSSION

    • 4 .1 DESCRIPTIVE STATISTIC ANALYSIS

    • 4.1.1. THE DATA DESCRIPTION:

    • 4.1.2. THE SUMMARY STATISTIC:

    • 4.1.3 TESTING FOR CORRELATION RELATIONSHIP

    • 4.1.4. CHECKING FOR MULTICOLLINEARITY:

    • 4.1.5 THE RELATIONSHIP BETWEEN INDEPENDENT VARIABLES AND NET INTEREST MARGINS:

    • Inflation rate and NIM

    • Market structure (HHI) and NIM

    • The scale of bank (SIZE) and NIM

    • Liquidity risk (LIQ) and NIM

    • The credit risk (CRD) and NIM

    • The capital adequacy (CAP) and NIM

    • The operating cost (OPE) and NIM

    • The implicit interest payment (IIP) and NIM

    • Managerial efficiency (MGE) and NIM

    • 4 .2 ECONOMETRIC ESTIMATION AND TESTING MODELS:

    • 4.2.1 WHETHER FEM OR REM IS MORE CONSISTENT:

    • Table 7 : Testing for selecting appropriate model

    • 4.2.2 FIXED EFFECTS MODEL:

    • THE NON - SIGNIFICANT FACTORS:

    • THE SIGNIFICANT FACTORS:

    • 4.3. EMPIRICAL FINDINGS

    • Inflation rate (INF)

    • Market structure (HHI)

    • Bank size (SIZE)

    • Liquidity risk (LIQ)

    • Credit risk (CRD)

    • Operating cost (OPE)

    • 4.3.2 HYPOTHESISES ACCEPTED:

    • Implicit interest payments (IIP)

    • Managerial efficiency (MGE)

    • SUMMARY CHAPTER 4

  • CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

    • 5.1 CONCLUSION

    • 5.2 POLICY RECOMMENDATIONS

    • 1. The capital structure:

    • 2. The implicit interest payment:

    • 3. The efficiency of banking management:

    • 5.3 LIMITATIONS AND FURTHER RESEARCH:

    • 5.3.2 FURTHER RESEARCH

    • REFERENCES

  • APPENDIX A

    • Table A -1 : The data description:

    • Table A -2: Description Statistics of variables

    • Table A- 3: The correlation relationship between variables

  • APPENDIX B

Nội dung

INTRODUCTION

Problem statements

Bank systems are vital to the modern economy, acting as intermediaries that significantly influence economic development, as evidenced by the cases in India and Hungary during their transitional periods The efficiency of financial intermediaries is crucial for economic stability, particularly in Europe, where the banking sector is integral to monetary policy through deposit and lending channels A bank's dependence on customers and its operational capabilities are key factors in shaping monetary policy for the European Monetary Union Efficient banking systems enhance economic resilience against shocks, while also serving as a bridge connecting various economic sectors and regions Consequently, the performance of the banking system is a critical concern for both national and global economies, with key activities such as deposits and lending being central to their profitability and overall function as financial intermediaries.

The traditional banking model revolves around the collection of customer deposits to fund loans, making the cost of funds a critical factor affecting bank profitability The net interest margin (NIM) serves as a key indicator of the relationship between deposits and loans, and it can be measured in two primary ways The first method calculates NIM as the difference between loan interest rates and deposit rates The second, more commonly used method, measures NIM by dividing the difference between interest income and interest expenses by total assets for the relevant period, with data sourced from financial statements Here, interest income reflects the bank's earnings after taxes, while interest expenses represent the costs associated with customer deposits This second method has been widely adopted in previous studies on NIM, including works by Ho and Saunders (1981), Angbazo (1997), and Saunders and Schumacher (2000), and is typically the basis for NIM figures published by banks.

As for the impact of NIM on banking operation, Demirgỹỗ-Kunt and Huizinga

In 1999, it was noted that banks relying heavily on deposits for funding tend to experience lower profitability due to rising costs of funds Research indicates that stable and efficient banks focus on maintaining optimal interest margins, with net interest margin (NIM) serving as a key indicator of banking cost and efficiency NIM, calculated as the difference between interest income and interest expense relative to total earning assets, significantly impacts a bank's efficiency A lower net intermediation cost reflects effective monetary policy and financial stability, whereas high costs can deter economic activity Raharjo et al (2014) demonstrated that a healthy banking sector is capable of generating profits, withstanding economic shocks, and contributing to overall financial system stability Given that the banking sector dominates the financial landscape in many countries, any failures within it can severely affect economic growth, potentially leading to bank runs and broader financial crises (Ongore and Kusa).

2013) Therefore, this research wants to analyze the determinants of net margin in the

World Developed countries European Union United States Japan Developing countries Brazil

Year financial period, so that can found significant factor and base on that to improve heath of bank through net interest margins.

Between 2008 and 2012, the global economy faced significant challenges due to the financial crisis that originated in the United States, impacting various sectors including industry, services, and finance The banking sector, in particular, experienced severe repercussions, highlighted by the bankruptcy of major American institutions such as Lehman Brothers, which was the fourth largest bank at the time.

Between 2008 and 2012, significant banking failures occurred, including the collapse of major institutions like the United States' banks in 2008 and Integra Bank Corp in 2011, alongside numerous smaller banks This instability within the largest banking systems had a profound impact on the global banking landscape, affecting banks in the ASEAN region as well.

The 2008-2009 economic crisis marked one of the most severe downturns in global history, significantly impacting major economies like the United States, Japan, and Europe During this period, GDP plummeted, unemployment rates soared, and numerous companies faced bankruptcy Notably, several countries experienced negative GDP growth, with the European Union declining by 0.5% and Germany by 0.8%.

States (-0.7%), Japan ( -0.2%)some countries like Russia and only 3 , 5% and China from

10% to 8% in 2008 (Figure 1) In addition, Figure 2 also show that the growth rate of worldwide industrial exports decreased remarkably from 2007 t0 2009.

Figure 1: GDP growth rate in main regions and countries, 2005 - 2009

Source: International Monetary Fund (IMF) and Author’s calculation

Figure 2: The growth rate of worldwide industrial exports, 2005 – 2009.

Source: Source: IMF and Author’s calculation

Grigor and Salikhov (2009) identified key factors contributing to the global crisis, including high economic growth rates since the early 2000s, significant savings imbalances, negative real interest rates in developed nations, and weakened financial sector regulation due to the increased use of new financial instruments Fidrmuc and Korhonen (2010) highlighted the substantial impact of the 2008 global crisis on business cycles in Asian developing countries, noting a significant decline in GDP growth rates and low business cycle levels in OECD countries as a result Additionally, Ivashina and Scharfstein (2010) and Aisen and Franken (2010) explored the crisis's detrimental effects on bank credit, which is central to banking operations By 2010-2012, the global economy began to recover from the lingering effects of the crisis.

The crisis developed and spread to other Asian countries, including the countries of the ASEAN region In ASEAN countries, Figure 3 showed that GDP growth rate

Between 2008 and 2012, significant economic fluctuations were observed, particularly during the GDP slump in 2008-2009 However, from 2010 to 2012, the GDP growth rate showed signs of recovery Concurrently, the inflation rate experienced a dramatic decline during 2008-2009 across most ASEAN countries, followed by a period of greater stability from 2010 to 2012.

Figure 3: GDP growth rate from 2008 to 2009 in Asean countries.

Source: Work Bank (WB) and Author’s calculation

Figure 4: Inflation rate from 2008 to 2012 in Asean countries

Source: WB and Author’s calculation

On the other hand, Figure 5 showed the trend of NIM in Asean banks from 2008 to

In 2012, the net interest margin (NIM) emerged as a key indicator of banking efficiency, with fluctuations in NIM directly impacting banks' profitability and operational effectiveness During the crisis period, ASEAN countries experienced a notable decline in NIM, as illustrated by the downward trend observed in the mean NIM of ASEAN banks from 2008 to 2010 This trend reflects the challenges faced during the economic downturn and the subsequent recovery phase Consequently, this study aims to investigate the factors contributing to the volatility of NIM in the aftermath of the global economic crisis.

Figure 5: Trend of Net Interest Margins in Asean banks from 2008 – 2012

:Source: Bankscope and Author’s calculation

Research objectives

The goal of the study:

This study aims to model and assess the key determinants of net interest margins in ASEAN banks, focusing on ten critical factors: Gross Domestic Product (GDP) growth rate, inflation rate, banking market structure (measured by the Herfindahl-Hirschman Index), bank size, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency The research seeks to provide empirical conclusions and offer policy recommendations for decision-makers in the banking sector.

To meet this goal, specific objectives are set out:

1 Determine the factors, magnitude, sign and significant level of determinants of NIM.

2 Inferring conclusions to suggest recommendations.

Research questions

To solve objective of this paper, the relevant questions are answered:

1 What factors influence on the bank interest margins in Asean banks?

2 How those factors impact on the bank interest margins?

3 To recommend general policies for managing bank interest margins of Asean banks Which policy recommendation to manage NIM?

Research scope

This study examines the factors influencing bank interest margins across nine ASEAN countries—Brunei, Cambodia, Malaysia, the Philippines, Laos, Vietnam, Singapore, Thailand, and Indonesia—during the period from 2008 to 2012 Although the ASEAN region comprises ten members, including Myanmar, the absence of data from Myanmar necessitates its exclusion from this analysis.

Research structure

This research is structured into several key chapters: Chapter 1 discusses the rationale behind the selected theme and the primary objectives of the study Chapter 2 provides a review of relevant literature and establishes the conceptual framework concerning the factors influencing net interest margins Chapter 3 details the research methodology and the data utilized Chapter 4 presents the principal findings from the analysis Finally, Chapter 5 concludes the study and offers policy recommendations.

LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

Literature review for interest margins

2.1.1DEFINITION OF NET INTEREST MARGIN:

Net Interest Margin (NIM) reflects the relationship between a bank's deposits and lending activities Banks attract depositor funds by offering interest rates on deposits, which they then invest by providing loans to borrowers at higher interest rates Analyzing NIM serves as a method to assess the cost of financial intermediation, highlighting the difference between the interest paid by borrowers and the interest income received by depositors (Brock and Suarez, 2000) Consequently, banks strategically establish their loan and deposit rates to optimize this margin.

RL : the rate on loans

RD : the rate on deposits

R : risk – free interest rate a : fees charged on loans b : fees charged on deposits

And the pure margin is:

The determinants of net interest margins (NIM) can be analyzed through two main approaches: the traditional and modern approaches The traditional approach focuses on examining bank balance sheets to identify variables influencing NIM, while the modern approach considers the demand and supply rates within the bank's microstructure Most prior research has leaned towards the modern approach NIM is defined as the ratio of net interest income to total earning assets, typically reported annually by banks Net interest income represents the difference between interest income and interest expenses A significant study by Ho and Saunders (1981) pioneered the analysis of NIM, positioning banks as intermediaries that channel funds between recipients Their model highlights the reinvestment risk banks face if short-term interest rates decline, indicating that optimal fees must be set to compensate for this risk, ultimately determining the optimal interest margin.

In which: s : the difference between lending and deposit rates

/ : bank’s risk neutral spread Q : size of bank transactions

 2 ; the instantaneous variance of the interest rate on deposits and loans R : the bank management’s coefficient of absolute risk aversion

Net Interest Margin (NIM) is defined as the difference between a bank's interest income and interest expenses, expressed as a percentage of average earning assets According to Ho and Saunder (1986), NIM reflects the spread between interest revenue on bank assets and interest expenses on liabilities Dietrich, Wanzenried, and Cole (2010) further emphasize that NIM is calculated as a percentage of interest-earning assets Raharjo et al (2014) measure NIM by the ratio of net interest income to average total earning assets, where net interest income is the difference between interest income and interest expenses Additionally, Brock and Suarez (2000) highlight that NIM represents the gap between the interest costs paid to borrowers and the interest income received from depositors This study will measure NIM using the ratio of net interest income to total earning assets, with net interest income derived from interest income minus interest expenses The data for this analysis is sourced from Bankscope, ensuring a consistent and homogeneous database of NIM across different countries.

Ho and Saunders (1981) were pioneers in analyzing the determinants of net interest margins (NIM) Their findings identified key factors influencing NIM, including interest rate volatility, transaction size, risk aversion, and market competition, utilizing a two-step regression approach In the first step, NIM was estimated based on bank-specific characteristics, while the second step focused on macroeconomic and market structure characteristics.

Since 1981, the bank has acted as a risk-averse entity, navigating the costs associated with loan and deposit markets Numerous studies have built upon the model proposed by Ho and Saunders (1981) to examine net interest margins from various perspectives.

Wong (1997) built upon the model proposed by Ho and Saunders (1981), demonstrating that credit and interest rate risk significantly influence net interest margin (NIM) within a theoretical framework of risk-averse banks This model highlighted a positive correlation between NIM and factors such as market power, operating costs, and credit risk, while also indicating that interest rate risk positively impacts bank interest margins By utilizing the dealership model established by Ho and Saunders, Saunders and Schumacher further explored these relationships.

In their 2000 study, researchers analyzed the impact of implicit interest rates, opportunity costs, and credit risk on the net interest margin (NIM) across Germany, Italy, Switzerland, the UK, Spain, France, and the US from 1988 to 1995 Similarly, Claeys and Vander Vennet (2008) utilized a random effects estimator to explore the determinants of NIM in Central and Eastern Europe compared to Western countries, finding that interest rate volatility and regulatory restrictions, such as minimum capital and liquid reserve requirements, significantly influenced NIM Angbazo (1997) further contributed to this discussion by applying the dealership model developed by Ho and Saunders.

Research by McShane and Sharpe (1985) and Allen (1988) examined the impact of interest rate risk, default risk, liquidity risk, and off-balance sheet factors on the fluctuations of Net Interest Margin (NIM) from 1989 to 1993, utilizing data from 286 commercial banks In contrast, Lin et al (2012) employed a switching regression model to analyze the influence of bank diversification on margins in several Asian countries, including China, India, and Japan, during the period from 1997 to 2005 Their findings indicated that NIM is sensitive to various bank risk factors such as liquidity risk, interest rate risk, and credit risk, alongside other elements derived from balance sheets and income statements Similarly, Dumičić and Ridzak (2012) investigated the determinants of NIM in Central and Eastern Europe, referencing the model established by Ho and Saunders (1981).

Between 2000 and 2010, research on Net Interest Margin (NIM) utilized fixed effect estimators, with notable contributions from Maudos and Guevara (2004), who expanded upon the dealership model by Ho and Saunders (1981) Their findings indicated that NIM is positively associated with market power and concentration, while negatively correlated with interest rate risk, credit risk, and operating costs Additionally, Kasman et al (2010) highlighted the influence of bank-specific factors, country-specific market characteristics, and macroeconomic conditions on NIM, noting that consolidation affects NIM in both new and old EU contexts The Ho and Saunders model is regarded as foundational for understanding NIM determinants, drawing from key literature by Claeys and Vander Vennet (2008) and Lin et al (2012) Most studies have employed panel data analyses, including Ho and Saunders (1981), which focused on American banks over several years.

From 1976 to 1979, various studies have explored the determinants of Net Interest Margin (NIM) in different banking systems For instance, Entrop, Memmel, Ruprecht, and Wilkens (2012) analyzed the factors influencing NIM in the Albanian banking sector from 2001 to 2007 Similarly, Fungacova and Poghosyan (2009) examined the influences on NIM using data from Russian banks between 1999 and 2007 Additionally, Williams (2007) utilized panel data from 1989 to 2001 to investigate the determinants of NIM in Australia Overall, many studies leverage panel data, which combines time series data across various countries during the same periods.

In detail, Saunders and Schumacher (2000) employed data from seven countries to prove the relationship between NIM and implicit interest rate, opportunity cost, credit risk in

Between 1988 and 1995, Claeys and Vander Vennet (2008) analyzed panel data from 36 Western and Eastern European countries, covering the years 1994 to 2001 Their study utilized this extensive panel data over a five-year period to draw meaningful conclusions.

Between 2008 and 2012, various studies (Claeys & Vander Vennet, 2008; Dumičić & Ridzak, 2012; Kasman et al., 2010) categorized influencing factors into three distinct groups: macroeconomic factors, banking market specifics, and bank-specific variables This paper similarly organizes independent variables into these three categories, emphasizing the importance of each in understanding the banking sector.

Macroeconomic variables play a crucial role in assessing the health of a national economy and its influence on Net Interest Margin (NIM) Key indicators such as GDP growth and inflation rate serve as vital proxies for understanding this relationship Numerous empirical studies have shown that GDP significantly impacts NIM, with research by Schwaiger & Liebeg (2008) and Ben Naceur & Goaied (2008) highlighting this connection Additionally, the effects of inflation on NIM are also noteworthy, further emphasizing the importance of these macroeconomic factors in financial analysis.

Research by Dumičić and Ridzak indicates a negative impact of inflation on net interest margins (NIM) in Central and Eastern Europe (CEE) Conversely, a study by Kasman et al (2010) revealed a contra-variant effect on NIM Aliaga-Díaz and Olivero (2005) found that high inflation correlates with increased costs and income for banks, suggesting that bank income rises more significantly than costs during inflationary periods This paper examines the effects of GDP growth rate and inflation on interest rate margins Demirgüç-Kunt and Huizinga (1999) argue that while inflation raises bank costs, it also leads to increased interest margins and profitability, albeit with a low positive coefficient Their findings also indicate that GDP growth rate has no significant effect on NIM or profitability Additionally, Claeys and Vander Vennet (2008) demonstrate that higher GDP growth rates correspond to greater margins in CEE, alongside a notable positive impact of inflation on those margins.

This study focuses on bank-specific factors that influence banking performance, particularly the Net Interest Margin (NIM) While most variables are typically derived from income statements, balance sheets, and other financial reports, this paper selectively utilizes certain variables grounded in empirical literature to analyze their effects.

The suggested research approach

The concept of Net Interest Margin (NIM) was initially explored by Ho and Saunders (1981), who proposed a dealership model where banks act as risk-averse financial intermediaries In this model, banks facilitate the flow of funds between depositors and borrowers by setting deposit and loan rates They mobilize funds from suppliers through deposit rates and provide loans to demanders at varying interest rates, which reflect market conditions A key challenge for banks is managing the asymmetric nature of loan demand and deposit supply, as deposits and loans often have different timeframes This necessitates careful calculation of funding costs and harmonization of lending and deposit operations to mitigate interest rate risk Typically, borrowers require long-term financing while deposits are generally short-term, making the balance of funds critical To attract capital, banks often adjust interest rates, and they must also reinvest idle funds to cover deposit costs Consequently, banks face multiple risks, including interest rate risk, default risk, and credit risk, which Angbazo (1997) further elaborated upon by incorporating additional risk factors into the NIM framework.

The dealership model of Ho and Saunders (1981) becomes the basic model for last researchers about net interest margins Angbazo(1997), Saunders and Schumacher

In their studies, Claeys and Vander Vennet (2008), along with Maudos and de Guevara (2004), developed the NIM model, while Angbazo (1997) and Maudos and de Guevara (2004) further expanded upon the model originally proposed by Ho and Saunders (1981).

+ ? : The structure of the market for loans and deposits.

?: The coefficient of absolute risk aversion

: the volatility of money market interest rates : the credit risk

 ? ?? : the covariance between interest rate risk and credit risk

 (? + 2? 0 ) : The total volume of credits

 (? + ? ) : The average size of the credit and deposit operations undertaken by thebank.

This model examines the factors influencing Net Interest Margin (NIM) by assessing the impact of the spread between lending and deposit rates in the money market Angbazo (1997) found that riskier loans and elevated interest rates lead to increased bank interest margins in U.S commercial banks from 1989 to 1993 Additionally, NIM in European banks from 1993 to 2000 is influenced by market power, interest rate risk, credit risk, and operating costs.

The NIM model developed by Ho and Saunders (1981) is widely utilized in empirical research across various contexts For instance, Kasman et al (2010) explored the link between consolidation and commercial bank net interest margins in both old and new EU member states and candidate countries Similarly, Aliaga-Díaz and Olivero (2005) examined the cyclical behavior of net interest margins in U.S banks using this model English (2002) identified a correlation between net interest margins and market interest rates, while Fungáčová and Poghosyan (2011) demonstrated the influence of ownership factors on NIM in Russia Additionally, Dietrich, Wanzenried, and Cole (2010) found that net interest margins vary across countries due to bank-specific factors and macroeconomic variables.

The concept framework

Macroeconomic factors Banking market specific characteristics

The framework for understanding interest margins identifies three key determinant groups: bank-specific characteristics, macroeconomic factors, and banking market characteristics Macroeconomic conditions primarily focus on GDP growth and inflation rate The bank-specific group encompasses seven performance-related factors: bank size, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency Lastly, the banking market characteristics are assessed using the Herfindahl index to evaluate its impact on bank margins.

Chapter 2 provides a comprehensive overview of the theoretical literature on net interest margins and their determinants, including factors such as GDP growth rate, inflation rate, market structure, bank scale, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency It outlines the computational methods for each variable and presents an empirical model for this research, drawing on the theoretical frameworks established by Ho and Saunders (1981) and Angbazo (1997) Additionally, the chapter establishes a conceptual framework based on the reviewed theoretical literature.

RESEARCH METHODOLOGY AND DATA COLLECTION

Identification of variables

Net interest margin (NIM) can be assessed through two primary methods The first method calculates NIM as the difference between the contractual interest rates on deposits and loans; however, this approach has limitations due to the diverse sources of deposits and loans that may not align The second method measures NIM by evaluating the difference between interest income and interest expenses during a specific period, although this too has drawbacks, as noted by Demirgüç-Kunt and Huizinga (1999), who highlighted that interest income and expenses often materialize in different periods This method relies on data derived from banks' financial statements.

This study defines net interest margin (NIM) as the cost of intermediation, represented by the difference between the interest paid by borrowers and the income received by depositors (Bernake, 1983; Brock & Suarez, 2000) Similar to various empirical studies, NIM is calculated as the difference between interest income and interest expenses relative to total earning assets (Claeys & Vander Vennet, 2008; Dietrich et al., 2010; Entrop et al., 2012) Data for calculating NIM will be sourced from Bankscope.

3.1.2.THE INDEPENDENT VARIABLES AND HYPOTHESIS TESTING:

The GDP growth rate, a key macroeconomic indicator measured by GDP per capita, reflects the economic growth of a country and influences prices, costs, and the business cycle due to changes in monetary policies This economic growth is often linked to the net interest margin (NIM), with numerous studies indicating a positive relationship between GDP and NIM For instance, Claeys and Vander Vennet (2008) found a positive association between the business cycle and NIM in Western European bank markets, suggesting that higher economic growth correlates with increased net interest margins Similarly, Dumičić and Ridzak (2012) noted that higher GDP growth leads to greater credit demand, enhancing bank margins However, contrasting findings by Ben Naceur and Goaied (2008) in Tunisia, as well as Ben-Kediri et al (2005), indicate no relationship between economic growth and NIM This research utilizes GDP growth rate data from ASEAN countries, sourced from the World Bank, covering the period from 2008 to 2012.

Hypothesis 1: Economic growth (GDP) is expected a positive significant impact on NIM The greater economic growth will have a higher net interest margins

The inflation rate (INF), determined by changes in the Consumer Price Index (CPI), significantly impacts market prices and purchasing power Rising inflation typically leads to increased interest rates on deposits and loans, influencing the net interest margin (NIM) According to Kasman et al (2010), higher inflation correlates with increased costs and income, establishing a positive relationship between inflation and NIM Similarly, research by Raharjo et al (2014) covering Indonesia from 2008 to 2012 indicates that inflation has a significant positive effect on NIM Thus, inflation can have both positive and negative implications in this context, with data sourced from the World Bank for GDP growth rates.

Hypothesis 2: Inflation rate is expected that there is a positive effect on bank interest margin

The Herfindahl-Hirschman Index (HHI) serves as a crucial measure of market structure in the banking sector, reflecting the size distribution of banks and their positions within the overall market In this study, HHI is utilized as a proxy for market structure, calculated as the square of each bank's asset share in the loan market Previous research, including studies by Claeys & Vander Vennet (2008), Fungacova & Poghosyan (2009), and Maudos & Guevara (2004), indicates a significant positive correlation between the Herfindahl index and Net Interest Margin (NIM), suggesting that an increase in HHI is likely associated with a higher NIM.

Hypothesis 3: HHI is expected that there is a positive impact on net interest margins

 Bank size (SIZE) measure the size of bank based on the logarithm of total assets.

The size of a bank significantly influences its operating scale in the market, with larger banks typically experiencing lower profit margins, while smaller banks tend to charge higher margins due to elevated interest rates for borrowers Research by Fungacova and Poghosyan (2009) supports this notion, indicating a negative impact of bank size on net interest margin (NIM) Additionally, Dumičić and Ridzak (2012) found that in Central and Eastern European (CEE) banks, larger institutions generally incur lower costs and achieve higher NIM Consequently, it is anticipated that the SIZE variable will have a positive correlation with NIM.

Hypothesis 4: It is expected that scale of bank will effect on net margins significantly by a negative relationship

Liquidity risk (LIQ) is assessed by the ratio of liquid assets to total liabilities, indicating a bank's ability to meet depositor withdrawal demands and new loan requests High liquidity risk is undesirable for banks, as it reflects insufficient liquid assets to cover liabilities A significant negative correlation exists between liquidity risk and net interest margin (NIM); thus, when a bank's demand liabilities are well-supported by liquid assets, its liquidity risk decreases, positively impacting its margins This trend was observed in Russian banks from 1999 to 2007 (Fungacova & Poghosyan, 2009).

In 1997, Angbazo expanded upon the dealership model established by Ho and Saunder (1981) by incorporating liquidity risks, revealing that increased liquid assets lead to greater liquidity risk and subsequently lower bank margins Furthermore, Aliaga-Díaz and Olivero (2005), in their study "The Cyclical Behavior of Net Interest Margins: Evidence from the United States Banking Sector," provided evidence that the counter-cyclicality of balance sheet liquidity arises from the fact that credit risk escalates more for risky and illiquid assets compared to their liquid counterparts Consequently, this research anticipates a significantly negative coefficient of liquidity risk in relation to net interest margins (NIM).

Hypothesis 5: Author expects that liquidity risk will have a negative effect on net interest margin

Credit risk (CRD) is a crucial factor influencing net interest margins (NIM), determined by the ratio of total loans to total assets Research, including Wong (1997), indicates a positive relationship between credit risk and bank interest margins As the percentage of total loans relative to total assets increases, the interest spread tends to rise This correlation highlights that higher loan volumes lead to increased credit risk, subsequently boosting bank interest margins.

Numerous studies, including those by 2000 and Hawtrey and Liang (2008), have demonstrated a positive correlation between credit risk and net interest margins (NIM) in OECD countries Empirical research consistently supports this relationship, indicating that credit risk significantly influences bank margins Consequently, this study anticipates a similar positive correlation between credit risk and bank margins.

Hypothesis 6: It is expected that credit risk will have positive impact on bank margins

Capital adequacy (CAP), defined by the ratio of equity to assets, indicates that a higher ratio reflects a greater reliance on equity financing, which can lead to increased capital costs and potentially shrinking Net Interest Margins (NIM) Empirical studies, including those by Lin et al (2012), highlight that as equity rises, the cost of capital increases, necessitating banks to achieve higher NIM to offset these costs Furthermore, capital adequacy serves as a measure of banks' creditworthiness, with research by Kasman et al (2010) demonstrating a positive relationship between capital adequacy and NIM in both old and new EU markets Additionally, Claeys and Vander Vennet (2008) emphasize that capital adequacy plays a crucial role in mitigating risks and ensuring the stability of banking operations.

On the other hand, they explored that capital adequacy influence NIM positive significantly in CEE.

Hypothesis 7: It is expected that capital adequacy will effect on NIM positive significantly

Operating cost (OPE), defined as the ratio of overhead to total assets, has been shown to positively correlate with net interest margin (NIM) Research by Dietrich, Wanzenried, and Cole (2010) indicates that higher operating costs necessitate an increased interest margin for banks, particularly in the banking systems of new EU member and candidate countries, as highlighted by Kasman et al (2010) Additionally, Maudos and de Guevara (2004) identified operating cost as a significant factor influencing NIM in the EU, suggesting that elevated operating costs compel banks to implement higher margins to offset overhead through credit interest rates and deposit rates Consequently, a positive coefficient is anticipated in the model of this study.

Hypothesis 8: Operating cost is expected that there is a positive impact on net interest margins

Implicit interest payments (IIP), defined as the difference between operating expenses and non-interest revenue divided by total assets, are a key determinant of net interest margin (NIM) according to Hawtrey and Liang (2008) Banks often provide free banking services instead of paying interest on deposits, which can lead to higher bank margins, as noted by Kasman et al (2010) A lower IIP is associated with a decline in NIM, while Angbazo (1997) found a positive relationship between IIP and NIM in US banks from 1989 to 1993, indicating that rising implicit interest payments lead to increased costs and larger margins Zhou and Wong (2008) further demonstrated that costs associated with IIP are ultimately passed on to bank margins Therefore, this research anticipates a positive correlation between IIP and the NIM model.

Hypothesis 9: It is expected that implicit payment will have positive effect on net margins

Managerial efficiency (MGE), defined as the ratio of operating costs to gross income, serves as a key indicator of management quality Effective management significantly influences interest margins, with research indicating that banks with subpar management experience lower interest margins.

Research indicates varying impacts of management efficiency (ME) on net interest margins (NIM) Angbazo (1997) suggests that effective management correlates with increased revenues and higher NIM, while Kasman et al (2010) argue that ME negatively affects NIM in both old and new EU contexts Consequently, banks may need to adjust their strategies by offering higher deposit rates and lower credit rates in response to rising ME This paper will examine ME's dual influence on NIM, acknowledging both positive and negative correlations, but ultimately anticipates a negative relationship, consistent with the findings of Angbazo (1997) and Vardar and Okan (2010).

Hypothesis 10: Managerial efficiency is expected that it can effect on NIM negative significant

Data collection and expected results

The study utilizes panel data from nine ASEAN countries—Brunei, Cambodia, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam—covering the period from 2008 to 2012, as detailed in Table 1 Due to limited data availability, Myanmar banks were excluded from the analysis Bank-specific variables were sourced from the Bankscope database, while macroeconomic indicators, including GDP and inflation, were obtained from the World Bank database Table 1 outlines the data sources and variables in detail The analysis focuses on banks with complete data over the survey period, resulting in a total of 1,010 observations across 202 banks from 2008 to 2012.

Table 1: Feature and source of variables

Sign Data source Dependent variable

Net interest margins – the difference between interest income and interest expenses as a proportion of total earning assets (in %)

1.GDP Gross Domestic Product growth rate (in %) + World Bank

2.INF Inflation rate – the annual inflation rate (in %) + World Bank

Herfindahl - Hirchman Index for assets

HHI – the sum of squares of individual bank asset shares in the total banking sector assets for given region.

4 SIZE Bank size – the logarithm of bank total asset + Calculation from

5 LIQ Liquidity risk – liquid assets/total liabilities - Calculation from

6 CRD Credit risk – total loans/ total assets + Calculation from

7 CAP Capital adequacy – total equity/assets + Bankscope

8 OPE Operating cost – the ratio of overhead to total assets + Calculation from

Implicit interest payments - The difference between operating expense and non – interest revenue divided by total assets

10 MGE Managerial efficiency: Operating cost/ Gross

The research methodology

This study employs linear models to analyze the factors influencing Net Interest Margin (NIM), building on insights from prior empirical research that identified relevant variables.

The panel data equation model as follows:

NIM i,j,t = β 0 +β 1 GDP j,t +β 2 INF j,t + β 3 HHI j,t + β 4 SIZE i,j,t + β 5 LIQ i,j,t + β 6 CRD i,j,t + β 7 CAP i,j,t + β 8 OPE i,j,t + β 9 IIP i,j,t + β 10 MGE i,j,t + ε i,j,t

- i,j,t are bank, country and time, respectively

- NIMi,j,t : net interest margin value of bank i at time t in country j

This study utilizes panel data to evaluate the effects of independent variables on the dependent variable, considering both Fixed Effects Model (FEM) and Random Effects Model (REM) Each model presents its own set of advantages and disadvantages for analyzing panel data.

FEM (Fixed Effects Model) is utilized to assess the impact of various time-varying variables while isolating the effects of stable characteristics, allowing for a clearer estimation of their net influence on the dependent variable This model highlights the relationship between predictor and outcome variables within distinct entities, each possessing unique characteristics that may influence the predictor variables differently (Oscar, 2007).

The equation for the FE:

Y i,j,t = β 1 GDP j,t +β 2 INF j,t + β 3 HHI j,t + β 4 SIZE i,j,t + β 5 LIQ i,j,t + β 6 CRD i,j,t + β 7 CAP i,j,t + β 8 OPE i,j,t + β 9 IIP i,j,t + β 10 MGE i,j,t + vi + ε i,j,t

 vi: the unknown intercept for each entity ( n entity – specific intercepts) – The component represents the unobservable factors differ between entities but does not change over the vary time.

 it : the error term – the unobserved factors differ between entities but changes over the vary time

 Yit: the dependent variable where i, = entity (i = 1…n) and t = time

 βi : The coefficient for the independent variables

In estimating parameters for the Fixed Effects Model (FEM), two primary methods are commonly used: Least Squares Dummy Variable (LSDV) and Fixed Effects Estimator (FE estimator) LSDV is typically favored for smaller datasets, as managing dummy variables becomes cumbersome with larger observations Given that this study includes 1,010 observations, which is relatively large, the FE estimator is deemed more suitable, provided that the FEM is validated for this analysis.

When the characteristics of an entity are considered random and uncorrelated with the explanatory variables, Random Effects Model (REM) should be employed In REM, the residuals of each entity serve as new explanatory variables The key difference between fixed and random effects lies in whether the unobserved individual effect is correlated with the regressors in the model, rather than its stochastic nature (Green, 2008, p.183) One advantage of using random effects is the ability to include time-invariant variables in the model, utilizing the Feasible Generalized Least Squares (FGLS) estimator for analysis.

The Random Effected model is:

Y i,j,t = β 1 GDP j,t +β 2 INF j,t + β 3 HHI j,t + β 4 SIZE i,j,t + β 5 LIQ i,j,t + β 6 CRD i,j,t + β 7 CAP i,j,t + β 8 OPE i,j,t + β 9 IIP i,j,t + β 10 MGE i,j,t +α+ vi + ε i,j,t

 vi: the unknown intercept for each entity ( n entity – specific intercepts) – The component represents the unobservable factors differ between entities but does not change over the vary time.

 it : the error term – the unobserved factors differ between entities but changes over the vary time

 Yit: the dependent variable where i, = entity (i = 1…n) and t = time

 βi : The coefficient for the independent variables

The Hausman test is utilized to determine the appropriate choice between fixed effects (FE) and random effects (RE) models, as outlined by Baltagi (2008, p.320) The null hypothesis (H0) posits no correlation between subjects and the explanatory variables in the model While RE provides a reasonable estimate under H0, it becomes inconsistent with alternative hypotheses Conversely, FE is suitable for both H0 and alternative hypotheses If H0 is rejected, fixed effects estimates are preferred over random effects estimates However, if H0 is not rejected, indicating a correlation between the residuals and the explanatory variables, fixed effects estimates remain the more suitable choice In large samples, using the Least Squares Dummy Variable (LSDV) method is impractical, making the FE estimator the most appropriate method for estimation in the fixed effects model.

The outline of estimation method

This chapter clearly defines the variables and calculation methods, along with the source data for each variable Building on the conceptual framework presented in Chapter 2, it outlines ten hypotheses related to independent variables and their dependent counterparts The study utilizes panel data collected from nine ASEAN countries—Brunei, Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand, and Vietnam—spanning five years from 2008 to 2012, comprising a total of 1,010 observations The analysis employs both fixed effect and random effect models, with the Hausman test applied to determine the most suitable model for the data.

DATA ANALYSIS AND DISCUSSION

The data description

The observation totaled 1010 observations corresponding 202 banks from 2008 to 2012 The data of this paper is described on as followed:

Variable name Variable measurements Variable label

GDP The annual Gross Domestic Product growth rate

INF The annual inflation rate (in %) Inflation rate

HHI The sum of squares of individual bank asset shares in the total banking sector assets for given region.

SIZE The logarithm of bank total asset Bank size

Liquidity risk is assessed through the LIQ ratio, which measures liquid assets against total liabilities Credit risk is evaluated using the CRD ratio, indicating the proportion of total loans to total assets Capital adequacy is determined by the CAP ratio, reflecting the ratio of total equity to total assets Lastly, operating cost is analyzed via the OPE ratio, which compares overhead expenses to total assets.

IIP The difference between operating expense and non

– interest revenue divided by total assets Operating cost MGE The ratio of operating cost to gross income Managerial efficiency id Bank name is put from 1 to 202 Bank name

The summary statistic

Table 3: Deterministic statistic of main variables

Variable Obs Mean Std Dev Min Max

Source: Bankscope and Author’s estimation with Stata

Table 2 presents the summary statistics for the Net Interest Margin (NIM) over a five-year period from 2008 to 2012, based on 1,010 observations The analysis reveals a significant fluctuation in NIM, with a mean value of 5.74% The minimum recorded NIM is -14.5%, while the maximum reaches an extraordinary 484.23% Additionally, the standard deviation is 19.72%, indicating considerable variability in the data.

Economic growth of the Asean countries (GDP) average for the period 2008 -

2012 reach 4.911736%, the lowest achieving -2.329849%, the highest 14.78079%,2.94025 standard deviations achieved.

The inflation rate of the Asean countries (INF) in 5-year study averaged 5.749471%; -0.8538899 % is the lowest value and the highest value is 24.99718%, standard deviation is 4.756319.

The average HHI index value of 14.587% indicates a high degree of market concentration, with a range from a low of 7.29% to a high of 96.33%, and a standard deviation of 9.41%.

Scale of operating (SIZE) in Asean banks average 6.165555, the banks have the lowest scale at 4.032128, large banks clicked with value 8.460281, and standard deviation is 0.8442338.

Between 2008 and 2012, the liquidity of banks, measured by the ratio of liquid assets to average demand liabilities, averaged 54.18% During this period, the liquidity ratio exhibited a minimum value of 0.07% and a maximum of 6550.50%, with a standard deviation of 2.68.

Between 2008 and 2012, the credit risk (CRD) of the ASEAN banking system was assessed using the ratio of loans to total assets, averaging 54.24% The lowest recorded value was -0.96%, while the highest reached 96.59%, with a standard deviation of 21.25% Additionally, the ratio of equity to total assets (CAP) averaged 18.72%, with the lowest value at -6.01% and the highest at 99.20%, accompanied by a standard deviation of 18.05%.

The ratio of overhead to total asset (OPE) as a proxy of operating cost has 4.07586% average value with the lowest value is 0.1034%, 31.19787% is the highest value, standard deviation is 4.58181.

The difference the between operating expense and non - revenue divided by total assets (IIP) task interest value 1.23444% average, the lowest value is -25.83798%, the highest value achieves 23.81817%, 3.20568 standard deviation get.

The ratio of operating cost to gross income (MGE) average 58.81804, the lowest value of 3.93, the highest value was 467.53 with a standard deviation of 30.55281.

Table 2 presents summary statistics for the Net Interest Margin (NIM) over a five-year period from 2008 to 2012, based on 1,010 observations The analysis reveals significant fluctuations in NIM, with a mean value of 5.74%, a minimum of -14.5%, and a maximum of 484.23% The standard deviation is reported at 19.72, indicating considerable variability in the data.

Economic growth of the Asean countries (GDP) average for the period 2008 -

2012 reach 4.91%, the lowest achieving -2.33%, the highest 14.78%, 2.94 standard deviations achieved.

Over a five-year study, the average inflation rate among ASEAN countries was 5.74%, with a minimum of 0.85% and a maximum of 24.99%, resulting in a standard deviation of 4.75 Additionally, the average Herfindahl-Hirschman Index (HHI), which measures market concentration, was 14.58%, indicating a high level of market concentration The HHI values ranged from a low of 7.29% to a high of 96.33%, with a standard deviation of 9.41.

Scale of operating (SIZE) in Asean banks average 6.16, the banks have the lowest scale at 4.03, large banks clicked with value 8.46, and standard deviation is 0.84.

Between 2008 and 2012, the liquidity of banks (LIQ) was assessed using the ratio of liquid assets to average demand liabilities, yielding an average value of 54.17% During this period, the lowest recorded liquidity value was 0.07%, while the highest peaked at an impressive 6550.49%, resulting in a standard deviation of 2.68.

Between 2008 and 2012, the credit risk (CRD) of the ASEAN banking system was assessed using the ratio of loans to total assets, which averaged 54.24% During this period, the lowest recorded value was -0.95%, while the highest reached 96.58%, resulting in a standard deviation of 21.24%.

The bank's equity to total assets ratio (CAP) averaged 18.72%, with a minimum of -6.01% and a maximum of 99.20%, while the standard deviation stood at 18.04 Additionally, the overhead to total assets ratio (OPE), which serves as a proxy for operating costs, averaged 4.07586%, with a low of 0.10% and a high of 31.19%, and a standard deviation of 4.58.

The difference the between operating expense and non - revenue divided by total assets (IIP) task interest value 1.23% average, the lowest value is -25.83%, the highest value achieves 23.81%, 3.20 standard deviation get.

The ratio of operating cost to gross income (MGE) average 58.81, the lowest value of 3.93, the highest value was 467.53 with a standard deviation of 30.55.

Testing for correlation relationship

Table 4: correlation coefficient of variables

NIM GDP INF HHI SIZE LIQ CRD CAP OPE IIP MGE

Source: Bankscope and Author’s estimation with Stata

The correlation analysis presented in Table 3 indicates that the correlation between the variables is relatively low, suggesting that multicollinearity does not significantly impact the model of this study Among the independent variables, LIQ and CAP exhibit the highest correlation at 0.3632, which is still considered low Additionally, the correlation with IIP demonstrates the strongest relationship with NIM at 0.4054, followed by OPE and CAP with correlations of 0.2462 and 0.0451, respectively.

Checking for multicollinearity

To check the multicollinearity, the author use Variance Inflation Factor (VIF) to test, compare with VIF>10, the model is considered that there is multicollinearity.

However, in this study the mean VIF = 1.32 (in table 5) is much smaller than comparable value so there is no multicollinearity phenomenon.

VIF = 1.32 => multicollinearity does not effect on model.

Source: Bankscope and Author’s estimation with Stata

The relationship between independent variables and

 GDP growth rate and NIM

The figure 6 gives information about the relationship between GDP and NIM

In 2008 -2009, GDP declined rapidly while there was a slight increase in NIM Similarly, Asean countries experienced a stead decrease in NIM over the period

2009 -2010, GDP increase remarkably And in 2010 – 2010, while GDP fluctuate dramatically NIM changed slightly.

Figure 6: The relationship between GDP and NIM

Source: WB and Author’s calculation

Figure 7 illustrates the connection between inflation and net interest margin (NIM), highlighting a significant GDP decline alongside a gradual NIM increase from 2008 to 2009 In the following years, despite rapid fluctuations in inflation, NIM exhibited consistent stability.

Figure 7: The relationship between INF and NIM

Source: WB and Author’s calculation

 Market structure (HHI) and NIM

In term of the relationship between structure market and NIM, the story was quite different There was a positive correlation between HHI and NIM over the period 2008 to 2012.

Figure 8: The relationship between HHI and NIM

Source: Bankscope and Author’s calculation

 The scale of bank (SIZE) and NIM

As figure 9 showed, the changing of SIZE and NIM had a same trend from

2008 to 2012 Hence, there was a positive relationship between SIZE and NIM

Figure 9: The relationship between SIZE and NIM

Source: Bankscope and Author’s calculation

 Liquidity risk (LIQ) and NIM

Figure 10 illustrates the correlation between liquidity risk (LIQ) and net interest margin (NIM) in the ASEAN region from 2008 to 2012, revealing a positive relationship between the two variables during this period.

Figure 10: The relationship between LIQ and NIM

Source: Bankscope and Author’s calculation

 The credit risk (CRD) and NIM

As is showed in figure 11, there were a negative trend of NIM and CRD. However, the fluctuations of NIM and NIM were not rapidly in this period 2008 - 2012.

Figure 11: The relationship between CRD and NIM

Source: Bankscope and Author’s calculation

 The capital adequacy (CAP) and NIM

The data illustrates the relationship between capital adequacy (CAP) and net interest margin (NIM) from 2008 to 2012 Overall, fluctuations in CAP corresponded with changes in NIM during this period, indicating a positive correlation between the two metrics.

Figure 12: The relationship between CAP and NIM

Source: Bankscope and Author’s calculation

 The operating cost (OPE) and NIM

In contrast to the relationship between capital adequacy and Net Interest Margin (NIM), the link between operating expenses (OPE) and NIM reveals a distinct narrative Notably, a negative trend emerged between OPE and NIM during the period of 2008-2012, indicating a significant shift in the banking sector's financial dynamics.

Figure 13: The relationship between OPE and NIM

Source: Bankscope and Author’s calculation

 The implicit interest payment (IIP) and NIM

The figure 14 gives information about the relationship between implicit interest payment and NIM As is shown, the NIM had negative trend compared with changing of IIP from 2008 to 2012.

Figure 14: The relationship between IIP and NIM

Source: Bankscope and Author’s calculation

 Managerial efficiency (MGE) and NIM

As figure 15 showed, the changing of MGE was positive with fluctuation of NIM from 2008 – 2010 However, there was a different trend of NIM and MGE in

Figure 15: The relationship between MGE and NIM

Source: Bankscope and Author’s calculation

4 2 ECONOMETRIC ESTIMATION AND TESTING MODELS:

Table 6 shows the results of regression by Random Effect and Fixed Effect, Hausman test is employed to choose the most appropriate model is that the fixed effect model.

Table 6: Comparison of regression result of FEM and REM

* denote statistical significance at 10% ;** denote statistical significance at 5%;

Source: Bankscope and Author’s estimation with Stata

Whether FEM or REM is more consistent

This paper use Fixed Effects Model and Random Effects Model to regression. After that, using Hausman test to choose the appreciate model.

Table 7 : Testing for selecting appropriate model

Panel test Test Signal Appropriate model

RE vs FE Hausman test

Source: Bankscope and Author’s estimation with Stata

H0: REM is consistent and efficient

H1: FEM is more consistent and efficient than REM

The Chi-square value obtained is 109.99, with a probability value of 0.0000, which is significantly lower than the alpha level of 5% Consequently, at the 5% significance level, the null hypothesis (H0) is rejected, indicating that the Fixed Effects Model (FEM) is more consistent than the Random Effects Model (REM) for this analysis.

Fixed Effects Model

Table 8 : Results of Fixed Effect Estimator

Source: Bankscope and Author’s estimation with Stata

Macroeconomic factors such as GDP and inflation (INF) influence net interest margin (NIM) in the same direction, aligning with the author's expectations and previous studies However, their impact on NIM is relatively low, with coefficients of 0.078 for GDP and 0.023 for inflation This indicates that fluctuations in economic growth rates directly affect NIM, meaning that as GDP increases, NIM tends to rise, and vice versa The effect of inflation mirrors the findings of Claeys and Vander Vennet (2008).

According to Dietrich, Wanzenried, and Cole (2010), rising inflation leads to increased costs for deposits and bank loans, resulting in a widening gap between higher loan rates and lower deposit rates, ultimately causing a rise in Net Interest Margin (NIM) Additionally, the statistical analysis shows that GDP and inflation (pGDP = 0.634 and pINF = 0.852) are not statistically significant variables.

The regression analysis indicates that for every one-unit increase in the Herfindahl-Hirschman Index (HHI), the Net Interest Margin (NIM) increases by 0.224379 units, suggesting that changes in market structure impact NIM in accordance with HHI fluctuations However, the statistical significance of HHI at 0.538 exceeds the acceptable level, leading to the rejection of hypothesis H3.

 In the group of banking factors, SIZE, LIQ, CRD and OPE were not significant effect on NIM at the significant level Consequently, hypothesis 4, 5,6 and 8 were rejected.

Three key banking-specific factors significantly influence Net Interest Margin (NIM): capital adequacy, implicit interest payment, and managerial efficiency Notably, both capital adequacy and implicit interest payment have a positive relationship with NIM, whereas managerial efficiency negatively impacts it These insights are valuable for bank managers seeking to adjust net interest margins in alignment with their development strategies over time.

Implicit interest payments significantly enhance net interest margins (NIM) by accounting for additional bank expenses beyond deposit interest Furthermore, a positive correlation exists between capital adequacy and NIM, suggesting that increasing capital to support business growth and mitigate potential risks can elevate a bank's cost of capital To offset this increased cost, banks may raise their net interest margins Conversely, high-quality management is associated with a negative impact on NIM, indicating that effective management leads to higher profitability with lower costs, allowing banks to offer more competitive interest rates.

Empirical findings

 Growth Domestic Product rate ( GDP)

The regression analysis revealed a positive relationship between GDP and Net Interest Margin (NIM), with a coefficient of β1 = 0.0781859; however, the p-value of 0.634 exceeds the significance level of 0.01, leading to the rejection of hypothesis H1 at α = 10% This indicates that GDP does not have a statistically significant relationship with NIM Supporting this finding, previous studies by Dietrich, Wanzenried, Cole (2010), and Claeys and Vander Vennet (2008) suggest that a more stable economic growth correlates with a higher NIM.

The relationship between inflation (INF) and net interest margin (NIM) shows that INF has a dimensional impact on NIM, with a coefficient of β2 = 0.0236975, as indicated by research from Kasman, Tunc, Vardar, and Okan (2010) and Claeys and Vander Vennet (2008) However, the p-value of 0.852 exceeds the significance level of 0.05, leading to the rejection of hypothesis H2, indicating that INF is not statistically significant in relation to NIM.

The analysis reveals that the Herfindahl-Hirschman Index (HHI) coefficient of β3 = 0.2243791 indicates a positive relationship between market structure and Net Interest Margin (NIM) However, with a p-value of 0.2243791, which exceeds the 0.05 threshold, we reject the hypothesis H3, suggesting that HHI does not have a statistically significant impact on NIM.

The regression analysis of market structure aligns with the findings of Claeys and Vander Vennet (2008) and Dumicic and Ridzak (2012), revealing a coefficient of β4 = -2.659778 This indicates that the total assets of the bank remain stable, reflecting a consistent marginal rate reduction.

= 0424> 0.05, H4 is rejected The scale of operations of the bank has no statistical significance in relation to the NIM.

Liquidity Risk of bank (LIQ) effect negatively on NIM with β 5 =-0.0017152;

However, LIQ do not have statically significant with NIM because of α = 0.415 and reject H5.

The regression analysis reveals a coefficient for CRD (β6) of -0.062357, indicating a positive impact on NIM, which contradicts prior predictions and empirical findings Furthermore, the p-value of 0.280 exceeds the significance level of α = 5%, leading to the rejection of hypothesis H6, suggesting that CRD does not have a statistically significant effect.

Regression equation give coefficient of OPE β 8 -0.2927425 but α = 0.268 so H8 is rejected Therefore, OPE is not statistically significant with NIM.

The regression analysis indicates that Capital Adequacy has a statistically significant negative impact on Net Interest Margin (NIM), with a coefficient of β7 = 0.2475961 and a significance level of α = 0.007, which is below 5% Thus, accepting hypothesis H7 suggests that, holding other factors constant, an increase in Capital Adequacy will result in a corresponding increase in NIM by 0.2475961, and conversely These findings align with the research conducted by Claeys and Vander Vennet (2008) as well as Dumicic and Ridzak (2012).

IIP have coefficient β 9 = 5.918933 and α = 0.000 < 5%, H9 is accepted This explain that all else equal when IIP increase 1%, the NIM will rise 5.91%, this finding is similar study of Hawtrey, K., & Liang, H (2008).

The regression equation showed that there is the negative relationship between

ME and NIM based on β 10 = -0.209689 In addition, α =0.000 < 5%, H10 is accepted soMGE has statically significant on NIM.

This chapter addresses the study's questions and objectives by summarizing the statistical analysis of variables related to net interest margins (NIM) and independent factors such as GDP, inflation (INF), the Herfindahl-Hirschman Index (HHI), size (SIZE), credit risk (CRD), capital adequacy (CAP), liquidity (LIQ), interest income percentage (IIP), operational efficiency (OPE), and managerial efficiency (MGE) across ASEAN countries from 2008 to 2012 The findings indicate that the Fixed Effects Model (FEM) is the appropriate model, as confirmed by the Hausman test Additionally, hypotheses 1, 2, 3, 4, 5, 6, and 8 were rejected, while hypotheses 7, 9, and 10 were accepted, highlighting that capital adequacy, implicit interest payments, and managerial efficiency significantly impact net interest margins.

CONCLUSION AND RECOMMENDATIONS

Conclusion

The banking system is crucial to a country's economy, functioning as a financial intermediary with the primary goal of profit maximization The efficiency of banks is often assessed through their Net Interest Margin (NIM) This study aims to identify the factors influencing NIM and evaluate their impact on the banking sector during the ASEAN economic crisis from 2008 to 2012 The findings are intended to serve as a foundation for developing effective bank management policies and enhancing operational monitoring.

This study examined panel data from nine ASEAN countries between 2008 and 2012 to identify the determinants of bank interest margins Utilizing Fixed Effect Model (FEM) and Random Effect Model (REM) for data analysis, the Hausman test confirmed the appropriateness of the FEM Out of ten hypotheses tested, seven were rejected, including those related to GDP growth rate, inflation rate, banking market, bank size, liquidity risk, credit risk, and operating cost Conversely, three hypotheses were accepted, highlighting the significance of capital adequacy, implicit interest payment, and managerial efficiency in influencing bank interest margins.

The theoretical model indicates that capital adequacy positively influences Net Interest Margin (NIM) An increase in equity leads to higher funding costs, as greater capital adequacy reflects a bank's strong capitalization relative to perceived risks, thus ensuring long-term solvency Consequently, banks require larger margins to offset these costs, aligning with the findings of Saunders and Schumacher.

Brock and Suarez (2000) identified implicit interest payments as a crucial factor influencing net interest margins (NIM) Their study revealed that these payments significantly affect NIM, supporting the notion that so-called free banking services are not truly free, as they are offset by higher interest margins This suggests that banks prefer to offer free services rather than explicitly paying interest on deposits, which contributes to increased interest margins Furthermore, the research indicated that a greater emphasis on explicit banking commissions correlates with reduced implicit interest payments, leading to lower NIM Additionally, a significant negative relationship was found between managerial efficiency and NIM; as management quality improves, NIM decreases due to lower operational costs These findings align with the conclusions of Angbazo (1997) and Maudos and de Guevara (2004).

Policy Recommendation

The author recommends that bank managers implement strategies aimed at effectively controlling and adjusting net interest margins to align with their organizational objectives, based on the findings of the study.

Capital adequacy positively influences net interest margins (NIM), highlighting the importance of capital structure recommendations While higher total equity typically leads to increased capital costs, economic instability shifts the focus to debt costs, as banks refrain from paying dividends to shareholders Although raising interest rates could enhance profits and NIM, competitive pressures and rising rates may dampen demand for capital Additionally, equity levels dictate the scope of bank operations and affect capital adequacy Therefore, bank managers must strategically evaluate their capital structure to optimize NIM and ensure the bank operates profitably and efficiently.

The study revealed that implicit interest payments significantly enhance bank margins, indicating that higher implicit payments lead to increased net interest margins (NIM) This suggests that the costs associated with implicit payments are reflected in the NIM, highlighting that banks' free services are not genuinely free Consequently, bank managers must carefully evaluate service fees; raising service fees for deposits instead of offering interest rates can decrease implicit interest payments, resulting in a decline in NIM, and vice versa.

3 The efficiency of banking management:

Research indicates an inverse relationship between effective management and Net Interest Margin (NIM), highlighting the need for banks to enhance management quality through stricter supervision and transparency regulations Implementing and monitoring good governance practices can help prevent unhealthy banking sector practices Effective bank management is crucial for fostering an efficient banking system by promoting good corporate governance and minimizing operational costs, which in turn can lower intermediation costs for the public To ensure profitable bank performance, management quality must focus on cost-effective operations; thus, reducing operating costs to the lowest possible level is essential for increasing NIM.

This study acknowledges several limitations that future research should address Firstly, the data collection was restricted to only nine out of ten ASEAN countries, and the chosen banks lacked a comprehensive range of factors necessary for the research, leading to a limited representation of the entire banking system in the region Secondly, while the research introduced a new model incorporating specific banking and macroeconomic factors, it did not account for other critical elements such as the compulsory reserve ratio, risk aversion, and regulatory frameworks Additionally, the increasing complexity of banking activities may necessitate further exploration of non-traditional factors.

Traditional banking activities, influenced by specific variables, play a crucial role in determining Net Interest Margin (NIM) Alongside the growth of banking businesses, non-traditional activities serve as a key indicator for generating fee-based income and potential interest revenue This study utilized Random Effects and Fixed Effects estimators for analysis, acknowledging the possibility of bias in the results Ultimately, while the research identifies the factors impacting NIM, it does not offer strategic solutions for banks to adjust these factors effectively.

To address the identified limitations, the authors recommend future research to broaden the scope and duration of studies Additionally, they suggest incorporating factors such as the compulsory reserve ratio, opportunity cost, branching regulations, and legal frameworks into the analytical models This approach aims to provide solutions that can be tailored to align with the varying objectives of banks over different periods.

1 Acharya, V V., & Richardson, M (2009) Causes of the financial crisis Critical

2 Aisen, A., & Franken, M (2010) Bank credit during the 2008 financial crisis: A cross-country comparison IMF Working Papers, 1-25.

3 Aliaga-Dıaz, R., & Olivero, M P (2005) The Cyclical Behavior of Net Interest Margins: Evidence from the United States Banking Sector Manuscript, North Carolina State University.

4 Allen, L (1988) The determinants of bank interest margins: A note Journal of Financial and Quantitative Analysis, 23(2), 231-235.

5 Angbazo, L (1997) Commercial bank net interest margins, default risk, interest- rate risk, and off-balance sheet banking Journal of Banking & Finance, 21(1), 55-

6 Athanasoglou, P., Delis, M., & Staikouras, C (2006) Determinants of bank profitability in the South Eastern European region.

7 Ben Naceur, S., & Goaied, M (2008) The determinants of commercial bank interest margin and profitability: evidence from Tunisia Frontiers in Finance and Economics, 5(1), 106-130.

8 Brissimis, S N., Delis, M D., & Papanikolaou, N I (2008) Exploring the nexus between banking sector reform and performance: Evidence from newly acceded EU countries Journal of Banking & Finance, 32(12), 2674-2683.

9 Brock, P L., & Rojas Suarez, L (2000) Understanding the behavior of bank spreads in Latin America Journal of development Economics, 63(1), 113-134.

10 Campello, M., Graham, J R., & Harvey, C R (2010) The real effects of financial constraints: Evidence from a financial crisis Journal of Financial Economics,97(3), 470-487.

11 Claeys, S., & Vander Vennet, R (2008) Determinants of bank interest margins in Central and Eastern Europe: A comparison with the West Economic Systems, 32(2), 197-216.

12 Demirgỹỗ-Kunt, A., & Huizinga, H (1999) Determinants of commercial bank interest margins and profitability: Some international evidence The World Bank Economic Review, 13(2), 379-408.

13 Dietrich, A., Wanzenried, G., & Cole, R (2010) Why are net-interest margins across countries so different? Available at SSRN 1542067.

14 Disyatat, P (2004) Currency crises and the real economy: The role of banks.European Economic Review, 48(1), 75-90.

15 Dumičić, M., & Ridzak (2012) T Determinants of Banks' Net Interest Margins in the CEE.

16 English, W B (2002) Interest rate risk and bank net interest margins BIS Quarterly Review, 12(02), 67-82.

17 Entrop, O., Memmel, C., Ruprecht, B., & Wilkens, M (2012) Determinants of bank interest margins: impact of maturity transformation Available at SSRN.

18 Estrada, D A., González, E G., & Hinojosa, I P O (2006) Determinants of interest margins in Colombia Banco de la República.

19 Fidrmuc, J., & Korhonen, I (2010) The impact of the global financial crisis on business cycles in Asian emerging economies Journal of Asian Economics,21(3), 293-303.

20 Fungacova, Z., & Poghosyan, T (2009) Determinants of bank interest margins in Russia: Does bank ownership matter?.

21 Fungáčová, Z., & Poghosyan, T (2011) Determinants of bank interest margins in Russia: Does bank ownership matter? Economic systems, 35(4), 481-495.

22 García-Herrero, A., Gavilá, S., & Santabárbara, D (2009) What explains the low profitability of Chinese banks? Journal of Banking & Finance, 33(11), 2080-2092.

23 Grigor′ ev, L., & Salikhov, M (2009) Financial Crisis 2008 Problems of

24 Hasan, I., & Marton, K (2003) Development and efficiency of the banking sector in a transitional economy: Hungarian experience Journal of Banking & Finance, 27(12), 2249-2271.

25 Hawtrey, K., & Liang, H (2008) Bank interest margins in OECD countries The North American Journal of Economics and Finance, 19(3), 249-260.

26 Ho, T S., & Saunders, A (1981) The determinants of bank interest margins: theory and empirical evidence Journal of Financial and Quantitative Analysis, 16(4), 581-600.

27 Ivashina, V., & Scharfstein, D (2010) Bank lending during the financial crisis of

28 Kannan, R., Narain, A., & Ghosh, S (2001) Determinants of net interest margin under regulatory requirements: an econometric study Economic and Political Weekly, 337-344.

29 Kashyap, A K., & Stein, J C (1997) The role of banks in monetary policy: A survey with implications for the European monetary union Economic Perspectives-

Federal Reserve Bank of Chicago, 21, 2-18.

30 Kasman, A., Tunc, G., Vardar, G., & Okan, B (2010) Consolidation and commercial bank net interest margins: Evidence from the old and new European Union members and candidate countries Economic Modelling, 27(3), 648-655.

31 Keeton, W (2003) The role of community banks in the US economy.Economic

32 Lin, J R., Chung, H., Hsieh, M H., & Wu, S (2012) The determinants of interest margins and their effect on bank diversification: Evidence from Asian banks.

33 Maudos, J and de Guevara J.F (2004): Factors Explaining the Interest Margin in the Banking Sectors of the European Union, Journal of Banking and Finance 28,

34 McShane, R W., & Sharpe, I G (1985) A time series/cross section analysis of the determinants of Australian trading bank loan/deposit interest margins: 1962–1981.

35 Raharjo, P G., Hakim, D B., Manurung, A H., & Maulana, T N (2014) The Determinant of Commercial Banks’ Interest Margin in Indonesia: An Analysis of Fixed Effect Panel Regression International Journal of Economics and Financial

36 Samuelson, P A (1945) The effect of interest rate increases on the banking system The American Economic Review, 35(1), 16-27.

37 Sathye, M (2003) Efficiency of banks in a developing economy: the case of India European Journal of Operational Research, 148(3), 662-671.

38 Saunders, A., & Schumacher, L (2000) The determinants of bank interest rate margins: an international study Journal of International Money and Finance, 19(6), 813-832.

39 Sbracia, M., & Zaghini, A (2003) The role of the banking system in the international transmission of shocks The World Economy, 26(5), 727-754.

40 Schwaiger, M S., & Liebeg, D (2008) Determinants of bank interest margins in Central and Eastern Europe OeNB Financial Stability Report, 208 .

41 Schwaiger, M S., & Liebeg, D (2008) Determinants of bank interest margins in Central and Eastern Europe OeNB Financial Stability Report, 208.

42 Sidabalok, L R (2012) The Determinants of Banks' Net Interest Margin in Indonesia: A Dynamic Approach Universitas Indonesia, Graduate School of

43 Tan, M T B P (2012) Determinants of Credit Growth and Interest Margins in the

Philippines and Asia (EPub) (No 12-123) International Monetary Fund.

44 Tarus, D K., Chekol, Y B., & Mutwol, M (2012) Determinants of Net Interest Margins of Commercial Banks in Kenya: A Panel Study Procedia Economics and

45 Williams, B (2007) Factors determining net interest margins in Australia: domestic and foreign banks Financial Markets, Institutions & Instruments, 16(3),

46 Wong, K P (1997) On the determinants of bank interest margins under credit and interest rate risks Journal of Banking & Finance, 21(2), 251-271.

47 Zhou, K., & Wong, M C (2008) The determinants of net interest margins of commercial banks in mainland China Emerging Markets Finance and Trade,44(5), 41-53.

The dataset comprises 1,010 observations and 14 variables, with a total size of 52,520 bytes, indicating that 99.9% of memory remains free The variables include various storage types such as float and int, each with specific display formats and labels Key variables in the dataset are ni, m, gd, p, inf, hhi, size, liq, crd, cap, ope, iip, mg, and float.

Note: dataset has changed since last saved

Table A -2: Description Statistics of variables

Ma x gdp 1010 4.911736 2.94025 -2.329849 14.7807 inf 1010 5.749471 4.756319 -.8538899 24.9971 hhi 1010 14.587 9.417242 7.299929 96.3330 size 1010 6.165555 8442338 4.032128 8.46028 liq 1010 54.17781 268.4126 0714965 6550.49 1 crd 1010 54.2423 21.2459 -.9556481 96.5864 cap 1010 18.72454 18.04645 -6.01182 99.2036 ope 1010 4.075863 4.581811 1033992 31.1978 iip 1010 1.234438 3.205682 -25.83798 23.8181 mg 1010 58.81804 30.55281 3.93 7 467.53 cc 1010 5.262376 2.657426 1 9 id 1010 101.5 58.34055 1 20 t 1010 3 1.414914 1 5

Table A- 3: The correlation relationship between variables

(obs10) nim gd p inf hhi size liq crd cap ope iip mg nim 1.0000 e gdp 0.0026 1.0000 inf -0.0196 0.2641 1.0000 hhi -0.0219 0.0603 -0.1521 1.0000 size -0.1029 -0.0594 0.0756 -0.1905 1.0000 liq 0.0096 -0.0161 -0.0452 0.0191 -0.1416 1.0000 crd -0.0253 0.0043 0.0498 -0.1758 0.1187 -0.1704 1.0000 cap 0.0451 -0.0829 -0.1462 0.0375 -0.4800 0.3632 -0.2887 1.0000 ope 0.2462 -0.0384 -0.0878 -0.0476 -0.3130 0.0229 -0.0798 0.3871 1.0000 iip 0.4054 0.0750 0.0338 0.0239 -0.1474 -0.0170 0.2262 -0.2203 0.0133 1.0000 mge 0.0105 -0.0061 -0.1202 0.0537 -0.2923 0.0043 -0.1352 0.1155 0.2825 0.2980 1.000 Table A – 4: Checking for multicollinearity

0.51610 size 1.57 0.63565 iip 1.33 0.75170 ope 1.31 0.76336 mge 1.31 0.76527 crd 1.22 0.82181 liq 1.20 0.83551 inf 1.14 0.87624 hhi 1.12 0.89110 gdp 1.10 0.90725

Random-effects GLS regression Group variable: id Number of obs Number of groups =

Obs per group: min = avg = max =

Random effects u_i ~ Gaussian Wald chi2(10) Prob > chi2 =

(Std Err adjusted for 202 clusters in id) rho 2624549 (fraction of variance due to u_i)

Fixed-effects (within) regression Group variable: id Number of obs Number of groups=

Obs per group: min = avg = max =

(Std Err adjusted for 202 clusters in id) rho 58627836 (fraction of variance due to u_i)

Table B – 1: The Random Effect Estimation Results nim Coef Robust

Std Err z P>|z| [95% Conf Interval] gdp 0248353 0836549 0.30 0.767 -.1391254 188796 inf -.0777133 0608697 -1.28 0.202 -.1970156 0415891 hhi -.0007359 0007719 -0.95 0.340 -.0022487 000777 size 1.231906 1.843951 0.67 0.504 -2.382172 4.845985 liq -.1746049 2891223 -0.60 0.546 -.7412743 3920644 crd -15.69101 14.49992 -1.08 0.279 -44.11034 12.72832 cap 14.61398 12.0284 1.21 0.224 -8.961262 38.18921 ope 93.60504 61.55348 1.52 0.128 -27.03756 214.2476 iip 417.9434 269.9556 1.55 0.122 -111.1598 947.0467 mge -.1798791 1107566 -1.62 0.104 -.3969581 0371999 _cons 7.022192 7.329871 0.96 0.338 -7.344091 21.38848 sigma_u 8.1172107 sigma_e 13.607357

Table B – 2: The Fixed Effect Estimation Results nim Coef Robust

Std Err t P>|t| [95% Conf Interval] gdp 0781859 093536 0.84 0.404 -.1062519 inf 0236975 0518213 0.46 0.648 -.0784857 hhi 2243791 3272776 0.69 0.494 -.4209587 size -2.659778 3.117644 -0.85 0.395 -8.807263 3.48770 liq -.0017152 0023966 -0.72 0.475 -.0064409 crd -.062357 0657297 -0.95 0.344 -.1919653 cap 2475961 1778566 1.39 0.165 -.103108 ope -.2927425 5887071 -0.50 0.620 -1.453577 iip 5.918933 3.747824 1.58 0.116 -1.471163 13.3090 mge -.209689 1292901 -1.62 0.106 -.4646278

Difference sqrt(diag(V_b-V_B)) S.E b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Table B – 3: The Hausman Test size -2.659777 1.231906 -3.891683 3.34207 liq -.1715223 -.1746049 0030827 crd -6.235703 -15.69101 9.455306 5.03031 cap 24.75962 14.61398 10.14564 8.26310 ope -29.27423 93.60504 -122.8793 22.464 iip 591.8933 417.9434 173.9499 18.0003 mge -.209689 -.1798791 -.0298099 inf 0236974 -.0777133 1014107 gdp 0781859 0248353 0533506 hhi 0022438 -.0007359 0029796

Ngày đăng: 19/10/2022, 00:44

Nguồn tham khảo

Tài liệu tham khảo Loại Chi tiết
1. Acharya, V. V., &amp; Richardson, M. (2009). Causes of the financial crisis. Critical Review, 21(2-3), 195-210 Sách, tạp chí
Tiêu đề: CriticalReview, 21
Tác giả: Acharya, V. V., &amp; Richardson, M
Năm: 2009
2. Aisen, A., &amp; Franken, M. (2010). Bank credit during the 2008 financial crisis: A cross-country comparison. IMF Working Papers, 1-25 Sách, tạp chí
Tiêu đề: IMF Working Papers
Tác giả: Aisen, A., &amp; Franken, M
Năm: 2010
4. Allen, L. (1988). The determinants of bank interest margins: A note. Journal of Financial and Quantitative Analysis, 23(2), 231-235 Sách, tạp chí
Tiêu đề: Journal ofFinancial and Quantitative Analysis, 23
Tác giả: Allen, L
Năm: 1988
5. Angbazo, L. (1997). Commercial bank net interest margins, default risk, interest- rate risk, and off-balance sheet banking. Journal of Banking &amp; Finance, 21(1), 55- 87 Sách, tạp chí
Tiêu đề: Journal of Banking & Finance, 21
Tác giả: Angbazo, L
Năm: 1997
7. Ben Naceur, S., &amp; Goaied, M. (2008). The determinants of commercial bank interest margin and profitability: evidence from Tunisia. Frontiers in Finance and Economics, 5(1), 106-130 Sách, tạp chí
Tiêu đề: Frontiers in Finance andEconomics, 5
Tác giả: Ben Naceur, S., &amp; Goaied, M
Năm: 2008
8. Brissimis, S. N., Delis, M. D., &amp; Papanikolaou, N. I. (2008). Exploring the nexus between banking sector reform and performance: Evidence from newly acceded EU countries. Journal of Banking &amp; Finance, 32(12), 2674-2683 Sách, tạp chí
Tiêu đề: Journal of Banking & Finance, 32
Tác giả: Brissimis, S. N., Delis, M. D., &amp; Papanikolaou, N. I
Năm: 2008
9. Brock, P. L., &amp; Rojas Suarez, L. (2000). Understanding the behavior of bank spreads in Latin America. Journal of development Economics, 63(1), 113-134 Sách, tạp chí
Tiêu đề: Journal of development Economics, 63
Tác giả: Brock, P. L., &amp; Rojas Suarez, L
Năm: 2000
10. Campello, M., Graham, J. R., &amp; Harvey, C. R. (2010). The real effects of financial constraints: Evidence from a financial crisis. Journal of Financial Economics, 97(3), 470-487 Sách, tạp chí
Tiêu đề: Journal of Financial Economics,97
Tác giả: Campello, M., Graham, J. R., &amp; Harvey, C. R
Năm: 2010
11. Claeys, S., &amp; Vander Vennet, R. (2008). Determinants of bank interest margins in Central and Eastern Europe: A comparison with the West. Economic Systems, 32(2), 197-216 Sách, tạp chí
Tiêu đề: Economic Systems,32
Tác giả: Claeys, S., &amp; Vander Vennet, R
Năm: 2008
12. Demirgỹỗ-Kunt, A., &amp; Huizinga, H. (1999). Determinants of commercial bank interest margins and profitability: Some international evidence. The World Bank Economic Review, 13(2), 379-408 Sách, tạp chí
Tiêu đề: The World BankEconomic Review, 13
Tác giả: Demirgỹỗ-Kunt, A., &amp; Huizinga, H
Năm: 1999
14. Disyatat, P. (2004). Currency crises and the real economy: The role of banks.European Economic Review, 48(1), 75-90 Sách, tạp chí
Tiêu đề: European Economic Review, 48
Tác giả: Disyatat, P
Năm: 2004
16. English, W. B. (2002). Interest rate risk and bank net interest margins. BIS Quarterly Review, 12(02), 67-82 Sách, tạp chí
Tiêu đề: BISQuarterly Review, 12
Tác giả: English, W. B
Năm: 2002
18. Estrada, D. A., González, E. G., &amp; Hinojosa, I. P. O. (2006). Determinants of interest margins in Colombia. Banco de la República Sách, tạp chí
Tiêu đề: Determinants ofinterest margins in Colombia
Tác giả: Estrada, D. A., González, E. G., &amp; Hinojosa, I. P. O
Năm: 2006
19. Fidrmuc, J., &amp; Korhonen, I. (2010). The impact of the global financial crisis on business cycles in Asian emerging economies. Journal of Asian Economics,21(3), 293-303 Sách, tạp chí
Tiêu đề: Journal of Asian Economics,21
Tác giả: Fidrmuc, J., &amp; Korhonen, I
Năm: 2010
21. Fungáčová, Z., &amp; Poghosyan, T. (2011). Determinants of bank interest margins in Russia: Does bank ownership matter?. Economic systems, 35(4), 481-495 Sách, tạp chí
Tiêu đề: Economic systems, 35
Tác giả: Fungáčová, Z., &amp; Poghosyan, T
Năm: 2011
22. García-Herrero, A., Gavilá, S., &amp; Santabárbara, D. (2009). What explains the low profitability of Chinese banks?. Journal of Banking &amp; Finance, 33(11), 2080-2092 Sách, tạp chí
Tiêu đề: Journal of Banking & Finance, 33
Tác giả: García-Herrero, A., Gavilá, S., &amp; Santabárbara, D
Năm: 2009
23. Grigor′ ev, L., &amp; Salikhov, M. (2009). Financial Crisis 2008. Problems of Economic Transition, 51(10), 35-62 Sách, tạp chí
Tiêu đề: Problems ofEconomic Transition, 51
Tác giả: Grigor′ ev, L., &amp; Salikhov, M
Năm: 2009
24. Hasan, I., &amp; Marton, K. (2003). Development and efficiency of the banking sector in a transitional economy: Hungarian experience. Journal of Banking &amp;Finance, 27(12), 2249-2271 Sách, tạp chí
Tiêu đề: Journal of Banking &"Finance, 27
Tác giả: Hasan, I., &amp; Marton, K
Năm: 2003
25. Hawtrey, K., &amp; Liang, H. (2008). Bank interest margins in OECD countries. The North American Journal of Economics and Finance, 19(3), 249-260 Sách, tạp chí
Tiêu đề: TheNorth American Journal of Economics and Finance, 19
Tác giả: Hawtrey, K., &amp; Liang, H
Năm: 2008
26. Ho, T. S., &amp; Saunders, A. (1981). The determinants of bank interest margins:theory and empirical evidence. Journal of Financial and Quantitative Analysis, 16(4), 581-600 Sách, tạp chí
Tiêu đề: Journal of Financial and Quantitative Analysis,16
Tác giả: Ho, T. S., &amp; Saunders, A
Năm: 1981

TÀI LIỆU CÙNG NGƯỜI DÙNG

TÀI LIỆU LIÊN QUAN

w