Correlation Chart Between Inflation Fluctuation and Loan-to-Assets Dispersion

No. Country ARCH

(p)

GARCH

(q)

No. Country ARCH

(p)

GARCH

(q)

8 Laos 2 18 Taiwan 2

9 Malaysia 1 1 19 Indonesia 2

10 Sri Lanka 1

To visualize the relationship between inflation volatility and the variance in the loan-to-asset ratio. The graph in Figure 3.2 depicts the variation in inflation volatility and the dispersion of the loan-to-asset ratio. We see that there is a negative correlation between inflation volatility and the dispersion of the loan-to-asset ratio of banks. Although these figures provide an intuitive evidence of a negative correlation between inflation volatility and the cross-sectional dispersion of the loan-to-asset ratio of banks. However, banks in a country may be exposed to specific shocks with country-specific characteristics, so formal empirical research is needed before drawing conclusions about this correlation. Furthermore, in addition to country-level fixed effects, we must consider the impact of many other factors that may distort the observations.

Figure 3.2: Correlation chart between inflation fluctuations and dispersion of loan/total asset ratio

3.2.2.3 Control variables group

Control variables are variables that indicate a unique characteristic that are included in the model to reduce any impact that may confound other factors or the interpretation of the results of the study. Control variables also have a potential impact on the dependent variable like the independent variable, but that impact is not what we are interested in. In addition, the control variable is included in the analytical model because we cannot ignore its impact when considering the effects of the independent variable. Similar to Mustafa, Bing (2016), the group of control variables included in the model includes other factors that change the macro environment:

Inflation: collected from WorldBank, inflation is also expected to have a negative impact on the dispersion of loan to total assets ratio.

GDP growth rate, ∆ GDP: The impact of GDP growth ( ∆ GDP) on the dispersion of the loan/asset ratio is still unclear. For example, in a growing economy, if new credit increases steadily across all banks, the dispersion of the loan/asset ratio will not change. However, if new credit is expanded by certain banks, the impact on dispersion will be in the same direction because the GDP growth rate changes over time.

The dummy variable dumFC takes the value of 1 if year t is greater than 2007. The inclusion of this dummy variable in the model is intended to test whether the dispersion of loan-to-asset ratios has changed after the financial crisis. The intercept is expected to be positive because money was injected into financial markets after the financial crisis. Conversely, it can also be argued that the expectation is in the opposite direction, because banks restricted lending during the crisis despite the efforts of the central bank and the government.

Interaction variable between financial crisis dummy and inflation volatility

( dumFC*h ) shows that the correlation between inflation volatility has changed after the financial crisis. The negative (positive) coefficient shows that after the financial crisis, the negative effect of volatility on bank resource allocation has increased (weakened).

Stock market volatility ( Vol Stock ) and oil price volatility ( Vol Oil ) are also measured by applying the ARCH/GARCH method. To estimate stock market volatility, the study collects monthly data of representative stock market indices in each country from Datastream and oil price volatility is measured based on West Taxas Intermediate oil prices from the IMF. Although it can be expected that stock market volatility will have a positive impact on the dispersion of bank loans, this impact can also be the opposite. For example, if banks extend credit to companies with good quality investment opportunities even though these companies are unable to raise finance during the period of stock market volatility, the dispersion of banks' loan/fund ratio will widen. However, if stock market volatility is a signal of overall financial market instability, then loan dispersion will be reduced, as banks tend to behave conservatively in extending loans during volatile periods. The impact of oil price volatility on the dependent variable is expected to be in the opposite direction, as increased oil price volatility implies increased macroeconomic uncertainty leading to conservative lending behavior by banks.

The average risk of the banking sector is the volatility of the profitability of banks in the same country, which is calculated from the standard deviation of the ratio of net income to total assets of the banks in the data set. The paper expects the average risk of the bank to have a negative impact on the dispersion of the ratio of loans to total assets.

And finally, the average banking industry return is calculated as an average

for banks in the dataset and is expected to have a positive impact on banks' loan allocation efficiency.

Overall, the inclusion of control variables should not affect the expected sign and significance of the coefficient on inflation volatility, þ 1 , reflecting the reversible effects of inflation volatility on bank resource allocation. Note also that the extended model includes annual dummy variables, i.year , capturing residual shocks that may affect the correlation.

Table 3.3 presents a summary of variable descriptions, definitions of variables used in the empirical analysis, along with expected signs of independent variables affecting the dispersion of loan-to-total-assets ratio of banks.

29

Table 3.3: Variable description, definition and data source

Group Variable Name Definition Data Source Expected Sign

Dependent variable Disp ( Loans ) Variance of the ratio of loans to total assets of the firms

Bankscope and

j,t TA

banks in the same country

Datastream

ℎ j ,t Inflation volatility is measured by the conditional variance

from ARCH/GARCH estimates of the monthly consumer price index

IMF -

Inflation Worldbank

Indicators

∆ GDP GDP growth rate Worldbank

-

+/-

Independent variable

dumFC Equals 1 if year t is greater than 2007

Indicators

+/-

dumFC*h Interaction variable of dumFC and ℎ j ,t +/-

Vol Stock Stock market volatility is measured by the conditional variance from the ARCH/GARCH estimate of the stock market index

Vol Oil Oil price volatility measured by the conditional variance from the ARCH/GARCH estimate of the WTI oil price

Data stream +/-

IMF -

30

In addition, before estimating, we consider the phenomenon of multicollinearity between independent variables. Table 3.4 presents the pairwise correlation coefficients between the variables included in the model. The condition for assessing the absence of multicollinearity between independent variables is that the correlation coefficients must not exceed 0.8 and be statistically significant.

The results show that the banking industry risk variable and the banking industry profit variable are completely correlated at the 1% significance level. Therefore, when regressing using the FE model, the banking industry risk variable is eliminated. In addition, the other independent variables in the model do not have any correlation higher than 0.8, meaning that multicollinearity cannot occur.

Additionally, to reduce the impact of outliers, we excluded 2.5% of the outliers. The remaining observations in the study sample are the residual values.

31

Table 3.4: Correlation coefficient matrix

With the symbols * , ** , and *** representing the 10%, 5%, and 1% significance levels, respectively.

Variable name

1	2	3	4	5	6	7	8	9	10
1. Proportional dispersion for	1.0000
get a loan
2. Inflation fluctuations	-0.0881	1.0000
3. Inflation	-0.0782	0.0106	1.0000
4. GDP growth 5. Financial crisis	0.0232 -0.1049 *	0.0060 0.1519 ***	-0.1755 *** -0.1248 **	1.0000 -0.1527 ***	1.0000
6. Inflation fluctuations after	-0.0865	-0.0865	0.0932 ***	-0.0117	0.2061	1.0000
financial crisis 7. Market fluctuations	-0.1472 **	0.0277	0.1769 ***	-0.0441	0.0642	0.0155	1.0000
stock 8. Oil price fluctuations	-0.0449	0.0175	0.0931	-0.1197 **	0.1678 **	0.2607 ***	0.0054	1.0000
9. Banking industry risks	0.0143	-0.0056	-0.0170	-0.0335	0.0633	-0.0021	0.0296	0.0585	1.0000
10. Banking industry profits	-0.0143	0.0056	0.0170	0.0335	-0.0633	0.0021	-0.0296	-0.0585	-1.0000 ***	1.0000
row

Maybe you are interested!

• Correlation Chart Between Candidates' Ability and Exam Difficulty 02
• Pre-tax Profit of Bidv Tien Giang in the Period 2011-2015 zt2i3t4l5ee zt2a3gsnon-credit services, joint stock commercial bank zt2a3ge zc2o3n4t5e6n7ts At that time, the Branch had to set aside a provision for credit risks, which reduced the Branch's income. Chart 2.2. Pre-tax profit of BIDV Tien Giang in the period 2011-2015 Unit: Billion VND 140 120 100 80 60 40 20 0 63.3 80.34 89.29 110.08 131.99 2011 2012 2013 2014 2015 Profit before tax (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) However, through chart 2.2, it can be seen that BIDV Tien Giang's profit is still increasing continuously, and its operating efficiency is currently leaking. This is a contribution of non-credit services, and this service segment will be increasingly focused on growth by BIDV Tien Giang to ensure the highest profit safety because credit activities have many potential risks. At the same time, focusing on developing non-credit services is consistent with one of the contents of restructuring the financial activities of credit institutions in the project "Restructuring the system of credit institutions in the period 2011-2015" approved by the Prime Minister in Decision No. 254/QD-TTg dated March 1, 2012 [14]: "Gradually shifting the business model of commercial banks towards reducing dependence on credit activities and increasing income from non-credit services". 2.2. Current status of non-credit service development at BIDV Tien Giang. 2.2.1. BIDV Tien Giang has deployed the development of non-credit services in recent times. Along with the development of the Head Office, BIDV Tien Giang's products and services are constantly improved and deployed in a diverse manner to ensure provision for many different customer groups in the area: individual customers, corporate customers, and financial institutions. Typical services are as follows: Payment services, treasury services, guarantee services, card services, trade finance, other services: Western Union, insurance commissions, consulting services, foreign exchange derivatives trading, e-banking services,... 2.2.1.1. Payment services: In accordance with the Prime Minister's Project to promote non-cash payments in Vietnam [15], banks in Tien Giang province have continuously developed payment services to reduce customers' cash usage habits through card services and electronic banking services such as: salary payment through accounts, focusing on developing card acceptance points, developing multi-purpose cards, paying social insurance by transfer, paying bills through banks, etc. Chart 2.3. Net income from payment services in the period 2011-2015 Unit: Million VND 6000 5000 4000 3000 2000 1000 0 3922 4065 4720 5084 5324 2011 2012 2013 2014 2015 Net income from payment services (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) Along with the technological development of the entire system, BIDV Tien Giang has a payment system with a fairly stable transaction processing speed, bringing many conveniences to customers. The results of observing chart 2.3 show that the income from payment services that the Branch has achieved has grown over the years but the speed is not high and the products are not outstanding compared to other banks. Domestic payment products such as: Online bill payment, electricity bills, water bills, insurance premiums, cable TV bills, telecommunications fees, airline tickets, etc. bring many conveniences to customers. Regarding international payment, this is an indispensable activity for foreign economic activities, BIDV Tien Giang is providing international payment methods for small enterprises producing agriculture, aquatic food and seafood that have credit relationships with banks in industrial parks in Tien Giang province such as: money transfer, collection, L/C payment. 2.2.1.2. Treasury services: BIDV Tien Giang always focuses on ensuring treasury safety and currency security, always complies with legal regulations, and minimizes risks in operations such as: counting and collecting money from customers, receiving and delivering internal transactions, collecting from the State Bank (SBV) or other credit institutions, receiving ATM funds, bundling money, etc. BIDV Tien Giang's treasury service management department is always fully equipped with modern machinery and equipment such as: money transport vehicles, fire prevention tools, money counters, money detectors, magnifying glasses, etc. to ensure absolute safety in treasury operations, immediately identifying real and fake money and other risks that may affect people and assets of the bank and customers. In addition, implementing regulation 2480/QC dated October 28, 2008 between the State Bank of Tien Giang province and the Provincial Police on coordination in the fight against counterfeit money, in the 3-year review of implementation, BIDV Tien Giang discovered, seized and submitted to the State Bank of Tien Giang province 475 banknotes of various denominations and was commended by the Provincial Police and the State Bank of Tien Giang province [17]. Chart 2.4. Net income from treasury services in the period 2011-2015 Unit: Million VND 350 300 250 200 150 100 50 0 105 122 309 289 279 2011 2012 2013 2014 2015 Net income from treasury services (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) However, as shown in Figure 2.4, income from treasury operations is not high and fluctuates. Specifically, in the period 2011-2013, net income increased and increased most sharply in 2013, then in the period 2013-2015, there was a downward trend. This fluctuation is due to the fact that fees collected from treasury services are often very low and can even be waived to attract customers to use other services. 2.2.1.3. Guarantee and trade finance services: BIDV Tien Giang, thanks to the advantages of the province and the favorable location of the Branch, has continuously focused on developing income from guarantee services and trade finance. Chart 2.5. Net income from guarantee and trade finance services in the period 2011-2015 Unit: Million VND 14000 12000 10000 8000 6000 4000 2000 0 5193 5695 2742 3420 8889 3992 11604 12206 5143 5312 2011 2012 2013 2014 2015 Net income from guarantee services Net income from Trade Finance (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) Through chart 2.5, we can see that BIDV Tien Giang's income from guarantee services and trade finance has grown over the years. The reason is: Among BIDV Tien Giang's corporate customers, the construction industry is the industry with the highest proportion of customers after the trading industry, this is a group of customers with potential to develop guarantee services. The second group of customers is corporate customers in the fields of agricultural production, livestock and seafood processing with high import and export turnover in the area. are the target of trade finance development. In addition, BIDV Tien Giang also focuses on continuously developing these customer groups to increase revenue for many other products and services in the future. 2.2.1.4. Card and POS services: As a service that BIDV Tien Giang has recently developed strongly, it can be said that this is a very potential market and has the ability to develop even more strongly in the future. Card services with outstanding advantages such as fast payment time, wide payment range, quite safe, effective and suitable for the integration trend and the Project to promote non-cash payments in Vietnam. Cards have become a modern and popular payment tool. BIDV Tien Giang early identified that developing card services is to expand the market to people in society, create capital mobilized from card-opened accounts, contribute to diversifying banking activities, enhance the image of the bank, bring the BIDV Tien Giang brand to people as quickly and easily as possible. BIDV Tien Giang is currently providing card types such as: credit cards (BIDV MasterCard Platinum, BIDV Visa Gold Precious, BIDV Visa Manchester United, BIDV Visa Classic), international debit cards (BIDV Ready Card, BIDV Manu Debit Card), domestic debit cards (BIDV Harmony Card, BIDV eTrans Card, BIDV Moving Card, BIDV-Lingo Co-branded Card, BIDV-Co.opmart Co-branded Card). These cards can be paid via POS/EDC or on the ATM system. In addition, with debit cards, customers can not only withdraw money via ATMs but also perform utilities such as mobile top-up, online payment, money transfer,... through electronic banking services. In order to attract customers with card services, BIDV Tien Giang has continuously increased the installation of ATMs. As of December 31, 2015, BIDV Tien Giang has 23 ATMs combined with 7 ATMs in the same system of BIDV My Tho, so the number of ATMs is quite large, especially in the center of My Tho City, but is not yet fully present in the districts. Basic services on ATMs such as withdrawing money, checking balances, printing short statements,... BIDV ATMs accept cards from banks in the system. Banknetvn and Smartlink, cards branded by international card organizations Union Pay (CUP), VISA, MasterCard and cards of banks in the Asian Payment Network. From here, cardholders can make bill payments for themselves or others at ATMs, by simply entering the subscriber number or customer code, booking code that service providers notify and make bill payments. Chart 2.6. Net income from card services in the period 2011-2015 Unit: Million VND 3500 3000 2500 2000 1500 1000 500 0 687 1023 1547 2267 3104 2011 2012 2013 2014 2015 Net income from card services (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) Through chart 2.6, it can be seen that BIDV Tien Giang's card service income is constantly growing because the Branch focuses on developing businesses operating in industrial parks, which are the source of customers for salary payment products, ATMs, BSMS. Specifically, there are companies such as Freeview, Quang Viet, Dai Thanh, which are businesses with a large number of card openings at the Branch, contributing to the increase in card service fees [25]. Table 2.6. Number of ATMs and POS machines in 2015 of some banks in Tien Giang area. Unit: Machine STT Bank name Number of ATMs Cumulative number of ATM cards POS machine 1 BIDV Tien Giang 23 97,095 22 2 BIDV My Tho 7 21,325 0 3 Agribank Tien Giang 29 115,743 77 4 Vietinbank Tien Giang 16 100,052 54 5 Dong A Tien Giang 26 97,536 11 6 Sacombank Tien Giang 24 88,513 27 7 Vietcombank Tien Giang 15 61,607 96 8 Vietinbank - Tay Tien Giang Branch 6 46,042 38 (Source: 2015 Banking Activity Data Report of the General and Internal Control Department of the Provincial State Bank [21]) Through table 2.6, the author finds that the number of ATMs of BIDV Tien Giang is not much, ranking fourth after Agribank Tien Giang, Dong A Tien Giang, Sacombank Tien Giang. The number of POS machines of BIDV Tien Giang is very small, only higher than Dong A Tien Giang and BIDV My Tho in the initial stages of merging the BIDV system. Besides, BIDV Tien Giang has a high number of cards increasing over the years (table 2.7) but the cumulative number of cards issued up to December 31, 2015 is still relatively low compared to Agribank, Vietcombank, Dong A (table 2.6). div.maincontent .content_head3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s1 { color: black; font-family:"Courier New", monospace; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13.5pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; } div.maincontent .s9 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: -2pt; } div.maincontent .s10 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 5pt; } div.maincontent .s11 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: -5pt; } div.maincontent .s12 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: -3pt; } div.maincontent .s13 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: -4pt; } div.maincontent .s14 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 7.5pt; } div.maincontent .s15 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s16 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; } div.maincontent .s17 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9.5pt; } div.maincontent .s18 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -1pt; } div.maincontent .s19 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -5pt; } div.maincontent .s20 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -2pt; } div.maincontent .s21 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s22 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; } div.maincontent .s23 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -3pt; } div.maincontent .s24 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -5pt; } div.maincontent .s25 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; } div.maincontent .s26 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -4pt; } div.maincontent .s27 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -6pt; } div.maincontent .s28 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -1pt; } div.maincontent .s29 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11.5pt; } div.maincontent .s30 { color: black; font-family:Calibri, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11pt; } div.maincontent .s31 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11pt; } div.maincontent .s32 { color: black; font-family:.VnTime, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s33 { color: black; font-family:Cambria, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; } div.maincontent .s34 { color: black; font-family:Cambria, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10.5pt; vertical-align: -4pt; } div.maincontent .s35 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11.5pt; } div.maincontent .s36 { color: black; font-family:Arial, sans-serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s37 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 13pt; } div.maincontent .s38 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s39 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s40 { color: black; font-family:"Times New Roman", serif; font-style: normal; fo
• Organizational Chart of Lien Viet Post and Commercial Joint Stock Bank
• Growth Parameters of Plants in Strip No. 1 Growth Measurement Chart of Seedlings in Strip No. 1
• Chart Showing Growth in Deposits and Lending of Commercial Banks in Ho Chi Minh City in the First 7 Months of the Year.

CHAPTER 4: RESEARCH RESULTS

In this chapter, regression results will be presented using three estimation methods. The model is estimated using FEM, REM and then instrumental variable model with GMM, IV-GMM estimation techniques to overcome endogeneity. All models allow for country specific fixed effects and the most extensive model of adding year effects. Robust standard errors are also presented in all tables of results.

The results show that there is a negative correlation between inflation volatility and cross-sectional dispersion of loan-to-asset ratio. These findings provide support for the hypothesis that volatility influences the efficient allocation of scarce bank funds.

4.1 Regression results using REM

Table 4.1: Regression results using REM method

With the symbols * , ** , and *** representing the 10%, 5%, and 1% significance levels, respectively. The standard deviation is placed in parentheses.

(1)	(2)	(3)	(4)
h	-0.054621 ***	-0.0547329 ***	-0.0548081 ***	-0.048062 ***
	(0.0062821)	(0.0058209)	(0.0060634)	(0.0065763)
Inflation		-0.000074	-0.000059	0.0000207
		(0.003496)	(0.000349)	(0.003264)
GPD			0.0000789	0.000219
			(0.0004065)	(0.000432)
dumFC				0.0040325
				(0.0056153)
dumFC*h
Volume Stock
Oil Volume
Bank Risk
Bank Return
i.year
Country	Yes	Yes	Yes	Yes
Cons	0.0207991 ***	0.0210531 ***	0.0205542 ***	0.0170831 ***
	(0.0010182)	(0.0044867)	(0.0050174)	(0.0055164)
N	302	301	300	300
R 20.0078		0.00098	0.0099	0.0271

Column 1 describes the simplest model when inflation volatility is the only explanatory variable.