Download effects of the 2008 crisis on the volatility of returns on bank stocks in

Business and Management Review Vol. 3(08) pp. 01 – 13 June, 2014
Available online at http://www.businessjournalz.org/bmr
ISSN: 2047 - 0398
EFFECTS OF THE 2008 CRISIS ON THE VOLATILITY OF RETURNS ON BANK
STOCKS IN BRAZIL
Gabriel Rodrigo Gomes Pessanha (Corresponding Author)
Federal University of Lavras (UFLA-MG)
Federal University of Alfenas (Unifal-MG) - Campus Varginha-MG
Avenida Celina Ferreira Ottoni, 4000 - Padre Vitor - Varginha, Minas Gerais,
Brazil - Zip Code: 37048-395
E-mail: [email protected]
Leiziane Neves de Ázara
Federal University of Lavras (UFLA-MG)
DAE/UFLA - Campus Universitário - CEP 37200-000 Lavras, Minas Gerais, Brazil. Zip Code: 37200-000
E-mail: [email protected]
Cristina Lelis Leal Calegario
Federal University of Lavras (UFLA-MG)
DAE/UFLA - Campus Universitário - CEP 37200-000 Lavras, Minas Gerais, Brazil. Zip Code: 37200-000
E-mail: [email protected]
Thelma Safadi
Federal University of Lavras (UFLA-MG)
DAE/UFLA - Campus Universitário - CEP 37200-000 Lavras, Minas Gerais, Brazil. Zip Code: 37200-000
E-mail: [email protected]
ABSTRACT
This study aimed to analyze the stock returns of the Bank of Brazil, ItauUnibanco, Bradesco, Santander and
Nossa Caixa and the influence of the 2008 crisis on the volatility of all returns of each individual. It uses a
quantitative method of analysis of time series, ARCH modeling, in which there is the influence of the crisis on
the returns. It was analyzed a series of closing stock prices of the five largest banks in the period from
01/08/2007 to 16/10/2009, with a total of 546 observations. By the models, it was found that the volatility of
returns of all financial institutions analyzed changed by the existence of the crisis. Tests were conducted to
ensure the 95% confidence the accuracy of results, and only for the bank Nossa Caixa, the difference between
the returns before and after the crisis was not considered statistically representative.
Keywords: Volatility, return, subprime crisis, stocks, financial institutions
1. INTRODUCTION
The role of banks is of great importance to the economic development of a country. Besides being active and
assist the Central Bank in the monetary control policy, banks offer savers various opportunities to generate
wealth through investments that cater to diverse audiences, and help companies and individuals in need of funds
for financing projects or expenses (Marques et al, 2004).
In situations of financial slack or the reverse, banks are the first to be sought after by savers or borrowers. A
lack of credit in the banking market can trigger crises and provide extensive damage to other sectors of the
economy.
Goldsmith (1969) and Shaw (1973) showed that the financial system is important to all economic activities.
Thus, deficiencies in supply and quality of financial services would result in negative impacts on economic
growth. In the analysis of these authors, one of the causes of the differences in growth rates between countries is
due to differences in the conditions of financial services.
The decision of the field of study of this work considered the fact that the search for performance makes the
banking sector appears as one of the most developed in the Brazilian economy and the most profitable. The
pioneering technology deployment in the care and development of processes and services are allowing public
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Business and Management Review Vol. 3(08) pp. 01 – 13 June, 2014
Available online at http://www.businessjournalz.org/bmr
ISSN: 2047 - 0398
and private banks are increasingly efficient according to data from FEBRABAN Own (2004). Besides the
operational and financial efficiency, intangible items are occupying workspace in financial institutions. The use
of codes of ethics, channel investor relations, disclosure of financial information, ombudsmen, mapping of
operational and market risks and constant use of trademarks and slogans, allow institutions to differentiate
themselves from their competitors in a sustainable manner.
Like most financial institutions are present in the capital market, the shares traded on the stock exchange are
vulnerable to adverse economic conditions, whether internal or external. One way to quantify or witness this
influence is through the return earned on the shares of these institutions, which are subject of this work.
The mortgage crisis in the U.S. reverberated with serious restrictions on bank liquidity in institutions around the
world. Many major banks and centenarians were driven to bankruptcy or insolvency. Governments millionaire
offered aid to banks, but also to the production companies, since the lack of credit in the market affects the
economy as a whole.
In this scenario, the stock exchanges around the world witnessed high falls in the stock price of most companies.
The rationale for this work is the importance of this event in the stock market and the world economy, and the
analysis of the impact on national banks.
This study aims to analyze the effects of the subprime crisis that started in 2008 in the USA on the volatility of
stock returns of major Brazilian banks.
It is intended to specifically analyze the stock returns of the Bank of Brazil, Itauunibanco, Bradesco, Santander
and Nossa Caixa and the effect of the 2008 crisis on the volatility of the set of returns for each one individually.
2. THEORETICAL FRAMEWORK
2.1 Banking Sector
The Brazilian banking model began with the arrival of the Empire in 1808. Like the European model, the system
of the time was based substantially on deposits and loans. According Savoia (2004), the Brazilian banking
system remained without much change until the mid-twentieth century, as the economy revolved around the
primary and export sectors. During that time there has been efforts to tailor it to the needs of the market.
According Sochaczewski (1980), in 1930 established a national banking system against foreign agents with the
gradual closure of the sector. The banking system has expanded greatly with the aid of the industrialization
process. Even financial institutions have made many loans of dubious origin between 1939 and 1946, increasing
liquidity risk, the system as a whole managed to remain intact, despite the bankruptcy of some banks.
Superintendency of Currency and Credit, the institution responsible for regulating the banking sector with
functions of a central bank - the SUMOC was created in 1945. It was necessary that creation once the economy
has migrated from primary to an industrial economy and requiring greater specialization and greater amounts of
bank operations. Reserve requirements was created as a means of control of credit and money supply. But still,
the power of decision SUMOC was limited, being subordinate to the Ministry of Finance (Accorsi, 1990).
In 1964 the Banking Reform that was established by Law No. 4595 of 31/12/1964 occurred. With the law the
Central Bank of Brazil who came to exercise its functions in 1965 was created.'s Intention was to reform not
only the creation of the Central Bank, but the restructuring of the financial system, for both were created Credit
Societies , Finance and Investment, to finance short-term assets. The long-term loans have been carried out by
Investment Banks. The consolidation of this process occurred in 1988 with the creation of multiple banks
(Barbachan and Fonseca, 2004). CVM - even with the Reformation the Securities and Exchange Commission
was created. In addition, other aspects have been established as the regulation of transactions on stock
exchanges and incentives for the issue and purchase of securities, shares and debentures.
In the period from 1980 to 1994, the Brazilian economy was characterized by monetary and financial instability
due to the succession of the various economic plans. However, many private banks were able to extract the
advantages despite the failure of some banks adjustment policy. While the overall picture was of recession,
private banks posted net profits twice higher comparing the three years 1981 to 1983 and 1978 to 1980.
Lucrative This scenario would only be interrupted by arbitrary measures to constrain the evolution of the crisis.
However, the state banks were those who suffered with fiscal and financial imbalances of the recession and the
debt crisis, causing a profound mismatch on profitability and the balance sheets of these banks, including
closure of many (Belluzo and Almeida, 2002).
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Because of this weakening the banking system, the federal government sought to build mechanisms to
strengthen and restructure the sector, as PROES (Incentive Program Restructuring of the National Financial
System) and PROER (Incentive Program Restructuring and Strengthening of the Financial System) . Through
PROES, state banks began to federal control for further privatization, because they had problems. Already
PROER favored the change of control of private banks. These two programs strongly encouraged the change of
control of banks, constituting a gateway to international banks, mainly through mergers and acquisitions
(Carvalho, 2002).
Ceretta and Niederauer (2000) also claim that the Brazilian banking system recorded after 1994 is characterized
by the occurrence of several mergers and acquisitions, always seeking greater soundness of financial
institutions. Allied to this, the development of a broad process of expansion of modern management
technologies that provide greater satisfaction to customers, both internal and external organizations and induce
the assimilation and adaptation of new existing management paradigms in other countries. These technologies
aim to make the bank more competitive, with gains in efficiency and profitability in the long term, ranging from
minor operational adjustments to the redefinition of business strategy.
With the effect of stability and narrowing of the gains obtained before the float of inflation on demand deposits,
there was a race to increase productivity through heavy investments in new information technologies. This is
reflected in the evolution of the profile of CSBs. While the number of branches was reduced by 8% between
1994 and 1998, the number of electronic service posts quintupled between 1994 and 2001 (Barbachan and
Fonseca, 2004).
According Corazza (2000), macroeconomic stability installed in Brazil was related to the end of our revenues
from the float, which were the gains of unpaid as demand deposits on current accounts and resources in transit
liabilities. These capabilities when applied transactions in the overnight type - short-term - even higher yields
afforded to banks.
To Paula and Marques (2004), the Brazilian process of opening must be analyzed as part of a global
phenomenon of bank consolidation. One of the stimuli for the consolidation process is the economy of scale
generated. The trend of restructuring the banking sector is composed by a decrease in the number of institutions,
an increase in the average size of banks increasing bank concentration and reduction of personnel costs and
branch network.
The health of financial institutions operating in the country, especially the national property was enhanced by
factors such as accumulated force banks during the inflationary period; the Central Bank action to prevent the
occurrence of a crisis of major proportions and efforts for the modernization of financial supervision and risk
reduction, through adherence to the Basel Accord (Carvalho, 2009).
The Brazilian financial system has outstanding characteristics in relation to other countries' systems. In Brazil,
the model is diversified to meet the customers who increasingly have specific needs for products and services,
not limited only to financial intermediation attracting deposits and making loans.
In 2000, the Central Bank initiated a process that helped the banks to expand their area of expertise through the
establishment of correspondent banking. Banks conclude agreements with establishments shall perform basic
functions such as opening bank accounts, deposits, withdrawals, payments and receipts of charges, etc., which
greatly reduced the barriers in banking and reached previously unreachable customers by banks.
Banks can be great helpers in promoting economic growth. One of the positive impacts of the presence of
financial institutions in the market is can reduce the costs of the process of developing information and resource
allocation. Without the intermediation offered by banks, each investor has a large fixed cost associated with
searching for information, and it would be beneficial if a group of individuals constituted a financial
intermediary to manage information to other investors. Thus, banks assess, even before an operation to be
performed, investment opportunities and can accelerate economic growth. As a result of this information
management process, intermediaries help in the consolidation of companies and promising business (Levine,
2004).
2.2 Subprime Crisis 2008
Throughout the century, the interference of governments and the international coordination of policies tried to
tame the ghost of crises, but from time to time, sudden changes in the pattern of expectations have spread
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ISSN: 2047 - 0398
disorder and price volatility, disorganizing production and destroying accumulated wealth in an erratic and
unpredictable pattern (Motta, 2000).
In mid-September 2008, two of the more traditional investment banks on Wall Street caused instability in the
stock exchanges worldwide: Lehman Brothers, which filed for bankruptcy and Merrill Lynch, which, not to
follow the same path, was sold to Bank of America, the largest banking group in the USA.
One explanation for the crisis occurred in 2008, can be found in the uncontrolled expansion of credit arising
from U.S. policies for granting low-interest which led to the establishment, in real estate, a credit bubble. In this
sense, Martins (2008) explains that these consumers as the housing market could not pay their mortgages,
subprime securities, illiquid market, began to cause distrust and panic. Thus, the crisis in the U.S. mortgage
market led to the credit market crisis in general.
This fact can be explained by the presence of systemic risk, which occurs when there is fear that a financial
institution does not have sufficient resources to pay the other, causing a 'domino effect', ie, collapsing the entire
structure of banks and financial .
To Minski (2008) economic instability witnessed in 2008 is the result of a fragile financial system. Within the
Keynesian perspective, Minski hypothesizes that financial instability is fueled by social inequality, highlighting
the endogeneity of financial crises. Fragile financial systems are more susceptible to concussions.
After the attacks of September 11, 2001, the U.S. economy suffered various consequences which led to the
Federal Reserve - Fed, the U.S. central bank - a review of its policies paying attention to the reduction of
interest rates to facilitate the resumption of economy after the incident, which encouraged the credit (Torres
Filho, 2008).
Numerous people that were not considered suitable for obtaining credit pay higher rents than the mortgage they
could not hire. For many renters, the barrier to home ownership was not the amount of the monthly payment,
but the need for a down payment of 10% to 20% of the cost of the property. In the 1990s, lenders started to deal
with this problem through loans that did not require immediate payment, or required a low value, even using
electronic means for approving credit lines, which gave a more scientific treatment the condition of the subject
who was standing for a loan (Wharton School, 2009).
The final factor was flowering in the 90's, the "securitization", ie the combination of loans for home ownership
in securities similar to shares that can be bought and sold on the secondary market. The banks were granting
mortgages and to get rid of the risk inherent in the transaction, such transformed mortgages into securities to be
traded in the market and purchased by individuals or corporations, banks, funds, etc.. This allows lenders to get
loans that are in the pipeline, which allows them to borrow more (Wharton School, 2009).
The problem of the crisis was caused by financial structure. The transformation of mortgages, especially
subprime mortgages in securities traded in the secondary market, shifted the responsibility of lenders and
borrowers which allowed the spread of risk. Thus, a difficulty or failure in the process would affect all ends of
the system, triggering an overall effect on the financial channels and not only focused on the main actors that
would be involved in the initial operation.
The Fannie Mae and Freddie Mac, the quasi-governmental lenders, long work with the marketing of securities
backed by mortgages, but were limited to the prime lending rate. At a time when other lenders realized that they
could earn money by hiring subprime loans, they began bundling loans into securities.
According to Torres Filho (2008) the mortgage payment difficulties surfaced in the second half of 2007, when
banks began to have their results sensitized by the effects of funds linked to the mortgage. Thus, many investors
began an exchange of assets linked to mortgages for assets with greater liquidity. This situation affected
including interbank transactions, since no one knew for sure which were the banks that owned the junk bonds,
interest rates on these types of transactions rose.
In the Brazilian context, the cost of credit started to increase, because of the difficulty of external funding due to
the crisis. The banks, which previously raised resources abroad at lower cost, with the worsening crisis in the
United States began to tap the resources domestically.
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With this, the BC has taken steps to try to increase the liquidity (available resources in the economy), as reduced
reserve requirements (money banks are required to leave deposited in BC) totaling U.S. $ 99.8 billion during the
crisis, incentives for banks to purchase large portfolios of medium and small, use of international reserves for
lines of credit, authorization for state banks to purchase shares of private financial institutions without bidding,
selling dollars in international reserves, among other actions (leaf Region , 2009).
For the economics professor Alcides Leite, Trevisan Business School, the restructuring of the financial system,
the accumulation of foreign exchange reserves, fiscal adjustment undertaken in recent years and other features
made the Brazilian economy less exposed to international financial crisis, and this helped Brazil weather the
crisis more easily than other countries (Brazil Agency, 2009).
2.3 Volatility of Stock Returns
Stock prices are formed by the interaction of demand and supply forces of each. The greater or lesser supply and
demand for particular action is directly related to the behavior of several factors such as historical prices and,
above all, by the prospects of the issuing company and its dividend policy. Also vary due to market conditions
and the risk perception of investors. As investors act differently, stocks tend to move according to their
expectations, generating vice versa largest buyer force the seller, or, which tends to generate a movement of
high or low of the shares. Changes in share market movements are natural, that depend on the macroeconomic
scenario and also the perception the market has of a particular stock and its behavior in this scenario (GENEC,
2008).
In Brazil, recently, the stock market has received much attention from investors and companies, given that it has
presented itself as an opportunity for foreign investors aiming to diversify their portfolios. After the
implementation of the Real Plan, the Brazilian financial market showed a sudden development, so that through
the capitalization of the stock market grew both in terms of turnover as in allocative efficiency (Nunes et al,
2005).
For Teixeira and Choi (2005), the return performance of banking firms can depend on a number of issues:
economic factors that influence the entire market to produce greater profitability, legal factors, which also affect
the political economy, and the types of strategies chosen that maintains marketing relationships with the bank
market.
To War (2002), the cost of raising funds via stock market in Brazil is high due to two components: the interest
rate of the economy and the risk premium of the shares. To the extent that the interest rate of the economy is
maintained at high levels, investors do not see stimuli applied in equity securities. It is best to apply in
government bonds (in principle, lower-risk assets in the market), since they offer low risk and good return, at
rates fixed in advance. That is, the investor, to have full knowledge of the return on your investment, by
anchoring ends safer harbor.
However, through the capitalization of the stock market in Brazil has shown a high degree of risk due to
uncertainty about macroeconomic conditions and its financial structure. As a result, shares traded on the
Brazilian stock exchange were vulnerable to adverse economic conditions, whether internal or external. This can
cause different perceptions of risk by investors and provide a display of the national currency to speculative
attacks and require frequent market interventions by the government, which deviates from its main function is to
provide the necessary conditions for viability of the process economic development of the country (Nunes et al,
2005).
Blanchard cited in Nunes et al (2005) concluded that the increase in production is not caused by the stock
market. Both are the result of changes in economic policies under certain conditions in the economy, since
usually a listing of certain economic policy leads to changes in discount rates and anticipated profits which, in
turn, lead to changes in asset prices .
Capital gain or return of the share is the profit on the sale of shares, ie, the difference between the purchase price
paid and the price received for the sale. Thus, the return on a stock is the difference between the obtained value
of the stock on the date of sale and the amount paid on the date of purchase (GENEC, 2008).
The variation in stock returns over time is known as volatility. There are many ways to measure the volatility of
the return on a stock. One of the most common ways is the variance, which uses the squares of the differences
of the return of a security relative to its expected return. The standard deviation of the returns is also regarded as
a measure since it is the square root of the variance (Ross, Jaffe & Westerfield, 2002) root.
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The estimation and calculation of volatility of financial returns as well as its application to the determination of
the value at risk, adopt as a basis the daily variations in the prices of traded assets (Goodhart and O'Hara, 1997).
The volatility of stock returns reflects market uncertainty facing crises or other exogenous events that cause
greater variations in prices and returns, implying potential for large gains or large losses. Thus, the correct risk
management of a portfolio of investments involves estimates of price fluctuations in the market (Morais and
Portugal, 1999).
The high profits recorded in the financial sector (Table 1) can demonstrate that banks are in a high degree of
efficiency to obtain increased results. And this behavior in the sector is also influencing factor of investments in
bank shares, subject of this work.
3. METHODOLOGY
3.1 Model Search
The method of quantitative research for the development of this work will be used. Quantitative research allows
a more precise error and test hypotheses control more conclusive. Thus, a quantitative study is based mainly on
mathematical treatment of information obtained through a statistically significant sample for hypothesis testing.
There are different for parametric estimation of the variance of returns of financial time series methods. There
is, for example, volatility deterministic models and stochastic volatility models. In addition to these there is a
third method, nonparametric, where the volatility determination is performed using neural networks (Portugal
and Mitchell, 1999).
Statistical models can be exemplified by the models AR (Auto Regressive), MA (Moving Average), ARMA
(Auto Regressive - Moving Average), ARIMA (Auto Regressive Integrated Moving Average), SARIMA
(Seasonal Auto Regressive Integrated Moving Average), ARCH ( Autoregressive Conditional
heteroscedasticity), GARCH (Generalized Autoregressive Conditional heteroscedasticity). Artificial intelligence
based models can be exemplified by Neural Networks (ANN) and MDN (Mixture Density Network). These
forecasting techniques to assist decision making in activities that require planning and reducing uncertainty, thus
making possible and future risks more visible and therefore more manageable (Makridakis et al., 1983).
According to Morais and Portugal (1999), the ARCH models, introduced by Engle (1982), using information
from past prices to update the values for the current asset. With this model, various specifications and
assumptions can be made. One can assume that the distribution is normal or non-normal, introducing the
influence of exogenous variables such as trading volume of assets, verify the influence of the volatility of
returns in the determination or the mere confirmation of the existence of facts.
3.2 Selection of Data
For Levine et al. (2000), the methods of quantitative forecasting using historical aiming to study past events to
better understand the basic structure of the database data, hence providing enough to predict future occurrences
means. The study of time series works with the data behavior in the past and present, so that they provide the
idea of possible variations that may occur in the data in the future. The forecast is the main purpose of using
time series (Sáfadi, 2004).
The choice of companies to participate in this study was due to its representation on the national scene. For this
purpose, the value of the total assets of each financial institution, ie, companies that had greater assets in 2008 year of the crisis in question - are shown in Table 2.
The five largest banks were chosen based on their assets so that the work does not extend, but at the same time
is possible an analysis that portrays the Brazilian banking reality, since the chosen banks are present throughout
the country and are traditional the financial movements of the population.
To study the closing price of the institutions on the BOVESPA were used. Because the prices of Banco
Santander BR does not correspond to the entire period of analysis, it was removed from the modeling phase,
leaving then four banks: Brazil, Itauunibanco, Bradesco and Nossa Caixa.
The series of stock prices were obtained by Economática software, which provides time series of prices of
financial assets, corporate structure, indexers, and events of shares. The period of data collection comprises
between 01/08/07 to 16/10/09. Values were captured on a daily basis. This period comprises suppresses crisis
triggered in September 2008, the object of analysis of this work.
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ISSN: 2047 - 0398
3.3 METHODS FOR THE EXTRACTION OF VOLATILE
3.3.1 Arima Models
The ARIMA model is a general case of the models proposed by Box and Jenkins (1976), which is suitable for
describing non-stationary series, ie, series that do not have constant mean over the period of analysis, in which
the parameters are almost always small (Sáfadi, 2004).
The systematics of ARIMA models considers the trend of time series with order (p, d, q) is represented by:
φ(B)(1-B)dYt = θ(B)at
Being:
φ(B) = 1 - φ1B - φ2B2 - ... - φpBp the autoregressive polynomial of order p;
θ(B) = 1 - θ1B - θ2B2 - ... - θqBq the moving average polynomial of order q;
B the delay operator such that BjYt = Yt-j and d is the number of differences required to remove the trend of the
series and turn it into a stationary (Sáfadi, 2004).
However in economic series returns are stationary since the use of ARIMA models being unusual. Most
financial series exhibit conditional variance evolving in time. Thus, the linear ARIMA-type models are not
adequate to describe such behavior. According to Bollerslev et al (1992) volatility is a key variable that
permeates most financial instruments, which plays a central role in various areas of finance. For such series, the
most widely used models are those of ARCH introduced by Engle (1982) and developed by Bollerslev (1986),
Nelson (1991) and Glosten et al. al (1993), described in the next section.
3.3.2 Arch Class of Models
For a good fit of the model, it is necessary to use techniques wherein the residual structure is a white noise, ie
the residue is an independent and identically distributed random variable (Sáfadi, 2004).
The verification of stationarity of a number of assumptions are made in limiting the generality of the problem. A
stochastic process ARCH (1,1), is stationary if (α + β) <1, α and β are the coefficients of the squared innovation
and variance in the previous period, respectively, of the variance in the model equation. This model is often
employed to represent series for the purpose of verification of stationarity.
The modeling of a process which allows the first and second moments of the return of a {Rt} depends on past
values was proposed by Engle (1982) as the template below:
εt / ψt-1 ∼ N(0,ht)
ht = α0 + α1ε2t-1 + ... + αqε2t-q
εt = Rt - xtb
Where ψt-1 is observed all the information up to t-1, xtb is the average of {Rt}, where xt can include exogenous
variables and lagged dependent. For the variance is not negative it is assumed that α0>0 e α1≥0, i=1, ..., q com q
>0.
To test for conditional heterocedasticity autoregressive can use the Lagrange multiplier proposed by Engle
(1982). This test statistic has chi-square distribution. Thus, comparing the calculated this statistic with the
appropriate input of a chi-square distribution value, one can test the null hypothesis of no evidence of
conditional heteroscedasticity.
A stock index can be viewed as a financial asset that is traded both in the spot market and in the futures market,
often being used in the composition of investment portfolios. For the ARCH model, generally used the
difference of the logarithm of the price of the asset,
Rt = ln( Pt )
Pt-1
Where Rt is the return on the time t and Pt is the asset price at time t. You now have a series of daily returns.
As Tsay (2002) there are two main reasons for working with returns instead of prices. The first is that for
investors (producers) average, the return of an asset is a complete and independent summary of the scale of the
investment opportunity. Second, the series of returns are easier to handle than the price series as the first
statistical properties are more tractable.
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4. ANALYSIS OF RESULTS
Closing share prices of four of the five largest banks in the period from 08/01/2007 to 10/16/2009 series were
analyzed, with a total of 546 observations. Figures 1 and 2 illustrate the behavior of prices and returns of
banking stocks in the series analyzed period. One perceives clearly that the price series are non-stationary.
For analysis the returns transformed values of the natural logarithm so that the series were considered stationary
were used. For data analysis two softwares were used, Gretl and Minitab. Arch model for all variables was used.
The dependent variables are represented by Ln of the return and the independent variable is the existence of
dummy subprime crisis triggered in 2008. Models were significant with 95% confidence. This means that the
crisis affected stock returns in the period analyzed.
According to the coefficient of R2, the effect of the crisis explains 9.3% of returns Itauunibanco, 5.5% of the
Bank of Brazil, 5.2% of Bradesco and the lowest coefficient of Nossa Caixa was the 4 3% (Table 3).
It can be observed in Figure 3 that all prediction models were able to model the data do not suffer instabilities
due to wastes have normal distribution. The good fit is evidenced by the absence of autocorrelation and partial
autocorrelation of residues, ie, white noise are considered as not affecting the results of the models (Figures 4
and 5). The adjustment of the predictive models can be seen in Figures 6 and 7 showing the original series of
the log returns and the number predicted using the models work.
To demonstrate the influence of the effect of the crisis on the volatility of stock returns, made a final analysis
comparing the standard deviations of returns series before 08/15/09 which is the starting point of the crisis, and
the standard deviations the series returns thereafter (Figure 8).
The volatility of the returns of the Bank of Brazil, Bradesco and Itauunibanco series is statistically different for
the two study periods, before and after the outbreak of the subprime crisis. However, for our case, the variance
of the data series diverges between the two periods, however, is not considered statistically different at a
confidence level of 95%.
5. CONCLUSION
This work aimed to make an analysis of the effects that the subprime crisis of 2008 caused the return of the
shares of major Brazilian banks. Data were collected at pre-and post-crisis periods, and the returns were
loagaritmizados so that the series become stationary. The data were subjected to a Type ARCH modeling. The
models for the four banks in question were significant at 95%, and the presence of crisis can explain the
variations in the volatility of returns.
For errors the distribution was normal, showing that the data were not affected by errors, and verified the
absence of autocorrelation and partial autocorrelation of residues, characterizing them as white noise.
For banks in Brazil, Bradesco and Itauunibanco, the difference in volatility of returns in the period before and
after the crisis was statistically significant. The same can not be said for the Nossa Caixa, which showed
divergence in volatility, however was not statistically different.
At work, it is perceived as an event can influence trading in the equity of companies and their returns. Brazilian
banks were not directly affected by the crisis. Even, one can say that they were the one responsible for
sustaining the economy during this period, and yet the impact was significant as seen in this work.
As a suggestion for future work, it is recommended to use other databases for analysis. This work was limited to
the Brazilian scenario, however, the field is more extensive search both internally and externally. Moreover, the
estimation of statistics and forecasting models in view of the stock market is not limited to financial institutions.
The data of quotes from virtually every sector of the economy constitutes an invaluable database for analysis. In
addition, it is recommended deepening the theory of forecasting models and extraction of volatility. The ARCH
class of models have several other applications that were not mentioned in this work. The family of ARCH
models is huge and with the use of several tests can find what is the ideal model to be used.
REFERENCES
Accorsi, A.(1990) Automação: bancos e bancários. Dissertação de mestrado. FEA-USP. São Paulo: 1990. 127 p.
Agência Brasil (2009). Empresa Brasil de Comunicação. Disponível em: <http://www.agenciabrasil.gov.br>.
Acesso em 20 out 2009.
Barbachan, J. S. F.; Fonseca, M. M. (2004) Concentração Bancária Brasileira: uma análise micoreconômica.
Finacelab Working Paper. IBMEC. São Paulo: 2004.
8
Business and Management Review Vol. 3(08) pp. 01 – 13 June, 2014
Available online at http://www.businessjournalz.org/bmr
ISSN: 2047 - 0398
Belluzzo, L. G. de M.; Almeida, J. G. de (2002). Depois da queda: a economia brasileira da crise da dívida aos
impasses do Real. Rio de Janeiro: Civilização Brasileira.
Bollerslev, T.; Chou, R. Y.; Kroner, K. F. (1992) ARCH modeling in finance: a review of theory and empirical
evidence. Journal of Econometrics, v.52.
Box, G.E.P., Jenkins, G. (1976) Time Series Analysis: Forecasting and Control. Holden-Day, pp.28-32.
Carvalho, F. J. C. (2002) The recent expansion of foreign banks in Brazil: first results. Latin American Business
Review. Nova York, v. 3, n. 4, p. 93-119.
_________________________.
(2009)
Resenha
sobre
Sistema
Financeiro.
Disponível
em:
<http://www.mre.gov.br>. Acesso em: 25 de outubro de 2009.
Ceretta, P. S.; Niederauer, C. A. P.(2000) Rentabilidade do setor bancário brasileiro. XXIV ENANPAD –
Encontro da Associação Nacional de Programas de Pós Graduação em Administração. Florianópolis,
10-13 set., 2000.
Coelho, M. Histórico Crises Mundiais. InvestBolsa. Disponível em:
https://www.investbolsa.com.br/Acoes/post/2008/09/29/historico-de-crises-mundiais.
Acesso em 07/11/09.
Corazza, G. (2000) Crise e Reestruturação Bancária no Brasil. Encontro de Economia da Região Sul. Porto
Alegre: ANPEC-Sul.
Dieese. Departamento Intersindical de Estatística e de Estudos Sócio-econômicos. Informativo Anote. São
Paulo, Ano 2, n. 21, dez/2001.
Engle, R. E. (1982) Autoregressive Conditional Heteroscedasticity with estimates of the variances of UK
inflation. Econometrics, v. 50, n. 4, p. 897-1008.
Estadão. As grandes crises econômicas. Disponível em: http://www.estadao.com.br/especiais/as-grandes-criseseconomicas,15167.htm. Acesso em 08/11/09.
Genec. (2008) Cartilha Mercado de Ações. Universidade Corporativa Caixa. GENEC/GEMEF: Brasília.
Goldsmith, R. W. (1969) Financial Structure and Development. Yale University Press. New Haven.
Gomes, G. M. (1986) Crises econômicas e crises políticas: algumas reflexões à luz da experiência brasileira.
Revista de Economia Política. Vol 6, n.1, jan/abr 1986.
Goodhart, C. A. E. O’hara, M. (1997) High Frequency Data in Financial Markets: Issues and applicatons.
Journal of Empirical Finance 4, 73-114.
Guerra, L. F. (2002) O Mercado Acionário no Brasil: posição relativa, potencial de crescimento e prioridades de
ação. Dissertação de Mestrado em Administração, EAESP, FGV, São Paulo.
Kremer, R. L. (2003) Estratégias de Fusões e Aquisições no Varejo Bancário Brasileiro no período de 1994 a
1998. Dissertação de Mestrado. UFRGS, Rio Grande do Sul.
Levine, D. M.; Berenson, M. L. Stephan, D. (2000) Estatística: Teoria e Aplicações. usando o Microsoft Excel
em português. Rio de Janeiro: LTC.
Levine, R. (2004) Finance and Growth: Theory and Evidence. Cambridge, MA: National Bureau of Economic
Research, NBER Working Paper no. 10766.
Makridakis, S. G.; Wheelwright, S. C.; McGee, V. E. (1983) Fundamentals of Quantitative Forecasting. New
York: John Wiley & Sons.
Marques, F. T.; Matias, A. B.; Camargo Junior, A.S. (2004) Desempenho dos Bancos Comerciais e Múltiplos de
Grande Porte no Brasil. CLADEA..
Marques, M. B. L.; P., L. F. R. (2004) Tendências Recentes da Consolidação Bancária no mundo e no Brasil. IX
Encontro Nacional de Economia Política, Uberlândia, junho.
Martins, I. (2008) Atalho para o futuro. Época, Rio de Janeiro, n. 544, p.48-50, 13 out 2008.
Minski, H. P. (2008) Stabilizing an Unstable Economy. New York: Mc Graw Hill.
Morais, I. A. C. Portugal, M. S. (1999) Modelagem e previsão de volatilidade determinística e estocástica para a
série do Ibovespa. Estudos Econômicos: São Paulo. Vol 29, n.3, p 303-341, jul/set 1999.
Motta, J. R. S. T. (2000) As crises econômicas mundiais. Câmara dos Deputados. Consultoria Legislativa:
Brasília.
Nunes, M. S.; Costa JR., N. C. A.; Meurer, R. (2005) A relação entre o mercado de ações e as variáveis
macroeconômicas: um análise econométrica para o Brasil. RBE. Rio de Janeiro, out/dez 2005.
Ross, S. A., Westerfield, R. W., Jaffe, J. F. (2002) Administração Financeira. São Paulo: Editora Atlas.
Safadi, T. (2004) Uso de séries temporais na análise de vazão de água na represa de Furnas. Ciência
Agrotécnica. Lavras, v.28, n.1, p. 142-148.
Savoia, J. (2004) Visão Geral do Mercado de Capitais. Apostila Mercados de Capitais, FEA-USP. São Paulo.
Shaw, E. S. (1973) Financial Deepening in Economic Development.Oxford University Press. New York: 1973.
Sochaczewski, A. C. (1980) Desenvolvimento Econômico e Financeiro do Brasil: 1952-1968. São Paulo:
Trajetória Cultural.
9
Business and Management Review Vol. 3(08) pp. 01 – 13 June, 2014
Available online at http://www.businessjournalz.org/bmr
ISSN: 2047 - 0398
Teixeira, F. L. C.; Cavalcante, L. R. M. T. (2005) Relações entre tecnologia, padrões organizacionais e
produtividade no setor bancário no Brasil. In: Revista de Administração. São Paulo, v.40, n.3, p.213224.
Torres Filho, E. T. (2008) Entendendo a crise do subprime. Visão do desenvolvimento. BNDES, n.44.
Tsay, R. S. (2002) The analysis of financial time series. New York: J. WILEY.
Wharton School. Tremores no mercado hipotecário de crédito subprime. Universia Knowlegde Wharton.
Disponível em: <http://www.wharton.universia.net/index.Article1304>. Acesso em 08/11/09.
TABLE ANNEX
Table 1 - Net income in the Finance and Insurance sector
Sector Finance and
Insurance
Abc Brasil
Alfa Invest
Banrisul
BMF Bovespa
Bradesco
Brasil
Cetip
Cielo
ItauUnibanco
Nossa Caixa
Panamericano
Porto Seguro
Redecard
Santander BR
Sudameris
Sul America
Source: Economática (2010)
dec/08
150.088
100.321
590.873
645.596
7.620.238
8.802.869
40.967
1.393.843
7.803.483
646.537
95.575
290.175
1.196.120
1.580.613
415.941
Net Profit (thouseand reais)
dec/07
dec/06
97.511
61.000
133.985
87.309
916.381
361.659
8.009.724
5.054.040
5.058.119
6.043.777
883.937
657.921
8.473.604
4.308.927
303.127
453.472
130.042
72.418
419.864
460.160
700.765
293.663
1.845.396
803.619
303.619
321.013
-
dec/05
78.059
351.947
5.514.074
4.153.602
5.251.334
765.569
248.657
176.318
201.341
-
Table 2 - Banks in order of total assets in Dec/2008
Bank
Brasil
ItauUnibanco
Bradesco
Santander BR
Nossa Caixa
Total Assets (million reais)
685.684
612.399
485.686
334.755
64.990
Source: Economática (2010)
Time Series Plot of Brasil; BB_Retorno
35
Time Series Plot of ItauUnibanco; ITAU_Retorno
Variable
Brasil
BB_Retorno
30
35
30
25
25
20
20
15
15
10
10
5
5
0
0
/0
01
2
8/
7
00
0/
/1
18
07
20
1/
/0
14
08
20
/0
04
2
4/
8
00
2
/2
06
5/
8
00
9/
/0
11
08
20
1/
/1
28
08
20
/0
19
0
20
2/
9
/0
14
2
5/
9
00
8
/0
03
9
00
/2
Variable
ItauUnibanco
ITAU_Retorno
/0
01
2
8/
7
00
/
18
/
10
07
20
1/
/0
14
08
20
/0
04
2
4/
8
00
/0
25
2
6/
8
00
9
/0
11
8
00
/2
/
28
/
11
08
20
/
19
/
02
09
20
/0
14
09
20
5/
/0
03
2
8/
9
00
Figure 1 - Price and stock returns of the Bank of Brazil and Itauunibanco, 08/01/07 to 10/16/09.
Source: Research data.
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Business and Management Review Vol. 3(08) pp. 01 – 13 June, 2014
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Time Series Plot of Nossa Caixa; N.Caixa_Retorno
Time Series Plot of Bradesco; Bradesco_Retorno
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80
Variable
Bradesco
Bradesco_Retorno
Variable
Nossa Caixa
N.Caixa_Retorno
70
60
30
50
40
20
30
20
10
10
0
0
-10
/0
01
2
8/
7
00
/
18
/
10
07
20
/
14
/
01
08
20
4/
/0
04
08
20
/0
25
0
20
6/
8
/0
11
2
9/
8
00
/1
28
2
1/
8
00
19
2
2/
/0
9
00
5
/0
14
9
00
/2
/
03
0
/2
08
09
01
2
8/
/0
7
00
/1
18
2
0/
7
00
/
14
/
01
08
20
/0
04
2
4/
8
00
/
25
/
06
08
20
/0
11
2
9/
8
00
/
28
/
11
08
20
19
2
2/
/0
9
00
5/
/0
14
09
20
2
8/
/0
03
9
00
Figure 2 - Price and stock returns Bradesco and Nossa Caixa, 08/01/07 to 10/16/09.
Source: Research data.
Table 3 - Basic Statistics of models
Statistics
Bank of Brazil
ItauUnibanco
Bradesco
Nossa Caixa
0,0546176
0,0924877
0,0520656
0,0431524
0,000562861
0,000336756
0,000182784
0,00220388
0,279739
0,295805
0,387944
0,0316647
0,0321603
0,0381179
443,424
283,626
469,347
2
R adjusted
Variable Mean
dependent
Sum of squared residuals 0,447441
Standard error of waste 0,0414841
318,718
Chi-square test
p-value = 6,18444e-070 p-value = 5,1478e-097 p-value = 2,57878e-062 p-value = 1,20917e-102
(normality of residuals)
Source: Research data.
20
25
uhat10
N(-0,0012977 0,031625)
Estatística de teste para normalidade:
Qui-quadrado(2) = 443,424 p-valor = 0,00000
uhat5
N(-0,000513 0,032154)
Estatística de teste para normalidade:
Qui-quadrado(2) = 283,626 p-valor = 0,00000
18
16
20
Densidade
Densidade
14
12
10
15
8
10
6
4
5
2
0
0
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
-0.15
-0.1
-0.05
uhat10
0
0.05
0.1
uhat5
25
18
uhat3
N(3,4893e-005 0,038118)
Estatística de teste para normalidade:
Qui-quadrado(2) = 469,347 p-valor = 0,00000
uhat37
N(-0,00058429 0,041478)
Estatística de teste para normalidade:
Qui-quadrado(2) = 318,718 p-valor = 0,00000
16
20
14
Densidade
Densidade
12
15
10
8
10
6
4
5
2
0
-0.15
-0.1
-0.05
0
0.05
uhat3
0.1
0.15
0.2
0.25
0
-0.2
-0.15
Figure 3 - Test of normality of the residuals for the studied banks
Source: Research data.
-0.1
-0.05
0
0.05
0.1
0.15
0.2
uhat37
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ISSN: 2047 - 0398
Resíduo ACF
Resíduo ACF
1
1
+- 1,96/T^0,5
0.5
0.5
0
0
-0.5
-0.5
+- 1,96/T^0,5
-1
-1
0
5
10
15
20
25
30
35
0
40
5
10
15
20
25
30
35
40
defasagem
defasagem
Resíduo PACF
Resíduo PACF
1
1
+- 1,96/T^0,5
+- 1,96/T^0,5
0.5
0.5
0
0
-0.5
-0.5
-1
-1
0
5
10
15
20
25
30
35
0
40
5
10
15
20
25
30
35
40
defasagem
defasagem
Figure 4 - Autocorrelation and partial autocorrelation waste to the model of the Bank of Brazil and Itauunibanco
Source: Research data.
Resíduo ACF
Resíduo ACF
1
1
+- 1,96/T^0,5
0.5
+- 1,96/T^0,5
0.5
0
0
-0.5
-0.5
-1
-1
0
5
10
15
20
25
30
35
40
0
5
10
15
20
defasagem
25
30
35
40
defasagem
Resíduo PACF
Resíduo PACF
1
1
+- 1,96/T^0,5
0.5
0.5
0
0
-0.5
-0.5
-1
+- 1,96/T^0,5
-1
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
defasagem
Figure 5 - Autocorrelationdefasagem
and partial autocorrelation waste to the model of Bradesco
and Nossa Caixa
Source: Research data.
0.2
0.15
v1
predição
0.15
v1
predição
0.1
0.1
0.05
0.05
0
0
-0.05
-0.05
-0.1
-0.1
-0.15
-0.15
-0.2
-0.2
2008
2008.5
2009
2009.5
2008
2008.5
2009
2009.5
Figure 6 - Original and expected returns of the Bank of Brazil and Itauunibanco Series
Source: Research data.
12
Business and Management Review Vol. 3(08) pp. 01 – 13 June, 2014
Available online at http://www.businessjournalz.org/bmr
0.2
0.3
v1
predição
ISSN: 2047 - 0398
v1
predição
0.25
0.15
0.2
0.1
0.15
0.1
0.05
0.05
0
0
-0.05
-0.05
-0.1
-0.1
-0.15
-0.15
2008
2008.5
2009
2009.5
-0.2
2008
2008.5
2009
2009.5
Figure 7 - Original and expected returns of Bradesco and Our Box Series
Source: Research data.
Figure 8 - Dispersion of returns of banks studied before and after the crisis
Source: Research data.
13