Modeling Default Probabilities: the case of · PDF fileModeling Default Probabilities: The Case of Brazil Benjamin M. Tabak1, ... value of the probability, but to movements in the

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Modeling Default Probabilities: The Case of

Brazil

Benjamin M. Tabak 1 , Daniel O Cajueiro 2 , A. Luduvice 3

The Working Papers should not be reported as representing the viewsof the Banco Central do Brasil. The views expressed in the papers arethose of the author(s) and not necessarily reflect those of the BancoCentral do Brasil.

Abstract

Using disaggregated data from the Brazilian stock market, we calculate defaultprobabilities for 30 different economic sectors. Empirical results suggest that do-mestic macroeconomic factors can explain these default probabilities. In addition,we construct the Minimum Spanning Tree (MST) and the ultrametric hierarchi-cal tree with the MST based on default probabilities to disclose common trends,which reveals that some sectors form clusters. The results of this paper imply thatmacroeconomic variables have distinct effects on default probabilities, which is im-portant to take into account in credit risk modeling and the generation of stresstest scenarios.

Key words: Probabilities of default, default risk, Capital Asset Pricing Model(CAPM), Minimal Spanning Tree (MST).

1 Research Department, Banco Central do Brasil2 Universidade de Brasilia3 Universidade de Brasilia

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1 Introduction

One of the primary goals of financial regulation is to maintain economic sta-bility, e.g., to avoid crises and sudden adverse changes in the financial system.Historically, banking crises have proven to generate significant costs to the realeconomy (Hoggarth et al., 2002), which are usually less severe in countries withregulated banking systems (Angkinand, 2009).

We have recently seen that the 2008 subprime crisis brought us the need of a ro-bust risk management culture (Ackermann, 2008). For investors and financialinstitutions, detecting risk trends and making comparisons across countriesare important to minimize risks. Financial instability not only diminishes thewelfare of the economy with losses in the GDP, but affects consumption andaggravates uncertainty (Barrell et al., 2006). Thus, considering the inherentrisk of the economic agents, as well as the possibility of contagion effects, pol-icy actions from authorities are necessary to accomplish stability and avoiddamages. Therefore, predicting crises and assessing the degree of risk of theinstitution/country concerned provides important information to regulators.

Probabilities of default are valuable pieces of information for supervisors whenassessing the health of the financial system. They are usually calculated withstock market data and used to identify and predict upcoming crises as early aspossible, as an attempt to minimize its negative effects. Furthermore, as a rule,authorities and regulators must primarily remain watchful not to the actualvalue of the probability, but to movements in the probabilities of failure, as todetect upward trends and avoid failure (Clare, 1995).

In this context, one important branch of the recent financial literature hasfocused on the determinants of the probabilities of default. For instance, prob-abilities of default were found to be influenced by domestic macroeconomicfactors (such as inflation, production and interest) as well as a market portfolioand the international risk (Clare and Priestley, 1998), by financial deregulation(Clare and Priestley, 2002), by the size of the company (Dietsch and Petey,2004), by a variety of capital markets factors (such as interest and exchangerates and credit spreads) (Berardi et al., 2004) and by the institutional envi-ronment of the country (such as the quality of governance, the degree of lawand order) (Bystrom, 2004). Furthermore, Ammer and Packer (2000), in theirturn, point out that although the risk is usually assigned in accordance withthe issuer (sovereigns, municipal governments, industrial firms, and financialinstitutions located in many countries), the default determinants can differalso across industrial sector and geographical localization, so that maintain-ing the consistency across sectors usually is not easy.

Another important branch of the financial literature has tried to detect the

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existence of contagion effects among countries. We know that the Russian crisis(1998) increased the probability of Brazilian domestic bank failure, whereasthe Argentinean crisis (2001) did not, providing evidence that contagion hasdecreased since 1998, due to the introduction of a floating exchange rate regimeand the inflation-targeting framework (Tabak and Staub, 2007). In the UnitedStates, while geographic distance of the solvent banks’ head offices from thehead offices of the failed banks and capital adequacy are found to be negativelycorrelated to the magnitude of the contagion effect, size is positively related,supporting the existence of information-based contagion (Aharony and Swary,1996). Bystrom et al. (2005) analyze Thai firms and banks and observe asignificant increase in market based default probabilities around the Asiancrisis (1997-1998), but with a slow return to pre-crisis levels.

Parallel to to this literature that is devoted to understand the determinants ofprobabilities of default and the contagion among countries, a literature basedon complex networks analysis has been developed as an intersection of severalfields from graph theory to statistical physics to provide a unified view ofdynamic systems that may be described by complex web-like structures andnon-parametric statistics (Albert and Barabasi, 2002; Boccaletti et al., 2006;Costa et al., 2007). The modeling of financial networks using tools providedby the theory of complex networks can provide important insights on theunderstanding of financial links between banks and for the development ofbetter financial regulation (Boss et al., 2004; Iori et al., 2006; Nier et al., 2007;Cajueiro and Tabak, 2008). In this paper, we are particularly interested inidentifying the hierarchy present in the network formed with correlations ofthe probabilities of default.

We construct the Minimum Spanning Tree (MST) and the ultrametric hierar-chical tree associated with the MST for this purpose (Mantegna, 1999). Thenetworks property of hierarchy is useful because it allow us to observe that thenetworks often have structure in which vertices cluster together into groupsthat then join to form groups of groups, from the lowest levels of organizationup to the level of the entire network. Furthermore, the use of MST analy-sis is adequate for extracting relevant information when a large number ofmarkets are being studied as it provides a parsimonious representation of thenetwork of all possible interconnectedness and can greatly reduce complexityby showing only the most important non-redundant connections in a graphicalmanner (Coelho et al., 2007). One may note that that there is a large bodyof literature that have studied the emergence of complex patterns in networksformed by correlations of stocks (Onnela et al., 2002; Coelho et al., 2006),interest rates (Matteo et al., 2004, 2005; Tabak et al., 2009).

Our paper derives implied default probabilities for different economic sectorsfollowing the work of Bystrom (2004) and contribute to the discussion aboutthe determinants of the probabilities of default in two different ways. First,

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we show that the implied default probabilities for different sectors presentcommon trends using recent econometric and the above-mentioned clusteringmethods. To the best of our knowledge this is the first paper that studies thenetwork formed by correlations of default probabilities, which may prove usefulfor credit risk management. Second, we present important evidence linkingdefault probabilities to macroeconomic variables, which may be used to stressrisk within these sectors. Therefore, we provide new evidence suggesting thatmacro-financial variables (Clare and Priestley, 1998; Berardi et al., 2004) maybe used as determinants of probabilities of default, using disaggregated data.One also should note that we focus our analysis on one of the most importantmarkets in Latin America, Brazil. Brazil ranks as one of the most importantstock markets in Latin America both by size of the market and liquidity. Wefocus on economic sectors that comprise the Brazilian domestic traded firmswithin the Brazilian stock market. Many of these shares are also traded inthe New York Stock Exchange as American Depositary Receipts (ADRs) andmay be seen as an important source for international diversification. Despitethe economic significance of the Brazilian stock market the literature on thisparticular market is scant.

The paper is divided as follows. The next section presents the methodologyused to estimate the probabilities of default and to study the topology of thenetworks of correlations of probabilities of default. Section 3 presents the dataand the main empirical results of this paper. Finally, section 4 concludes thepaper summarizing the main findings of this paper.

2 Methodology

2.1 Estimation of Default Probabilities

In our approach, we estimate probabilities of default (PD’s) based on a con-ditional version of the Capital Asset Pricing Model (CAPM), following thework of Bystrom (2004).

The share price of a firm is given by:

Sit =

∑Nl=1 PltXlt

N, (1)

where N is the number of issued ordinary shares, Pl is the price of asset/liabilityl and Xl represents asset/liability l.

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The excess return on stock i is given by:

Rit = βtE(Rmt) + εit, (2)

where εit is assumed to be a white noise error term.

The conditional form of the CAPM shows that the return of a stock i dependson the time-varying market price of the risk λt, scaled by the time-varyingconditional covariance between the excess return on stock i and the stockreturn on the market portfolio:

Rit = λtE(umt, εit) + εit. (3)

The conditional variance (that is, the variability in the market value of thebank’s capital around it’s expected value) of firm capital at time t as measuredat time t− 1 is:

Et−1(StN − Et−1(StN))2 = (St−1N)σ2εit

, (4)

where σ2εit

is the variance of εi. This expression can be interpreted as thedifference between the actual and expected value of a firms capital at time t,where εit is the rational expectation forecast error.

We can thus develop a measure of the probability of default as the number ofstandard deviations the value of capital represents at time t−1 which is givenby the following expression:

Sit−1N

(Sit−1N)σεit

=1

σεit

. (5)

Assuming normality on the error term, we use the normal distribution toconstruct the default probability.

In order to estimate the equations (2) and (3) we first calculate the conditionalvariance σεt using a bivariate EGARCH, as described below:

E(σ2εt) = ω2

1 + β21σ

2t−1 + α2

1ε2t−1 + γ1Iε

E(σ2υt

) = ω23 + ω2

2 + β22σ

2t−1 + α2

2υt−1 + γ2Iυ

E(σεt,υt) = ω1ω2 + β2β1E(σεt−1,υt−1) + α2α1εt−1υt−1,

(6)

where E(σ2εt) and E(σ2

υt) are the conditional variances of εt and υt, E(σεt,υt) is

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the covariance between εt and υt, Iε (Iυ) are dummy variables that are equalto 1 when εt−1 < 0 (υt−1 < 0) and 0 otherwise.

Our choosing of a bivariate EGARCH is based on an asymmetric conditionalvolatility, i.e., falling prices will lead to a higher increase in the volatility of astock’s rate of return.

We add up the calculated daily σ2εit

estimates on each month and create a

monthly default measure 1/√

σ2ε1 + σ2

ε2 + ... + σ2ε21. Afterwards, we use an an-

nualized measure defined as 1/√

12σ2εt.

2.2 Construction of Minimum Spanning Tree (MST) and Hierarchical Treefrom Default Probabilities

From the probabilities of default we build a Minimum Spanning Tree (MST)to study the topology of the network. However, the MST requires the use ofa variable that can be interpreted as distance, satisfying the three axioms ofEuclidian distance. Therefore, we transform this matrix in order to build adistance matrix. To build the probability of default network we employ the

metric distance di,j =√

2(1− ρi,j) proposed by Mantegna and Stanley (1999),

where ρi,j is the correlation between changes in default probabilities i and j 4 .

The MST is a graph that connects all the n nodes of the graph with n − 1edges, such that the sum of all edge weights

∑i,j∈D di,j is a minimum, where

D is the distance matrix. The MST extracts significant information from thedistance matrix and it reduces the information space from n×(n−1)

2correlations

to n − 1 tree edges. It is the spanning tree of the shortest length using theKruskal algorithm of the di,j and is a graph without cycles connecting all nodeswith links 5 .

Define the maximal distance d∗i,j between two successive commodities whenmoving from PDi to PDj over the shortest path of the MST connecting thesetwo commodities 6 . The distance d∗i,j satisfies the above axioms of Euclidian

4 This metric satisfies the three axioms of Euclidian distance: (i) di,j = 0 if andonly if i = j, (ii) di,j = dj,i, and (iii) di,j ≤ di,k + dk,j .5 The Kruskal algorithm has the following steps: 1). Choose a pair of commoditieswith the nearest distance and connect with a line proportional to this distance, 2).Connect a pair with second nearest distance, 3). Connect the nearest pair that isnot connected by the same tree, and 4). Repeat step three until all commodities areconnected in one tree.6 This distance is called subdominant ultrametric distance and a space connectedby these distances provides a topological space that has associated a unique indexedhierarchy.

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distance and also the following ultrametric inequality:

di,j ≤ max[di,k, dk,j]. (7)

Networks have many properties that help researchers to understand the in-teractions between agents in a complex system. They have the property ofhierarchy which is useful to observe that the networks often have structurein which vertices cluster together into groups that then join to form groupsof groups, from the lowest levels of organization up to the level of the entirenetwork.

The ultrametric hierarchical tree uses the single-linkage clustering method,which builds up clusters by starting with distinct objects and linking thembased on similarity. The major issue with this method is that while it is robustfor strongly clustered networks, it has a tendency to link poorly clusteredgroups into chains by successively joining them to their nearest neighbors.This ultrametric hierarchical tree provides useful information to investigatethe number and nature of the common factors that affect the different sectors.

3 Empirical Results and Data

3.1 Individual Regressions

We estimate default probabilities for 30 markets over the period from March 1,2000 to June 30, 2008. In this period, default probabilities remained very lowin most market sectors. “Media” and “Broadcast and Entertainment” havehad the highest average default probability (around 6%).

The first step in our modeling procedure was to test whether default probabil-ities for the different sectors had unit roots. We find evidence suggesting thatthis is indeed the case and therefore the dependent variables were changes indefault probabilities. Second, we also test whether macro-financial variablescontained unit roots and employ the changes of these variables as well. Anadditional step was to estimate whether the macro-financial variables werestatistically correlated and to employ auxiliary regressions to build a set oforthogonal macro-financial variables. In order to do so we employ the residualsof the regressions relating two or more of these macro-financial variables as aproxy for the original variable.

We model these default probabilities by regressing them for each sector on anumber of macro-financial variables. We included 8 variables to explain theprobabilities of default: inflation, interest rate, oil price (Brent), exchange rate

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(Real/Dollar), interest rates spread, stock market index (Ibovespa - Sao PauloStock Exchange Index) as well as the Brazilian industrial production and theconsumer confidence index.

Results presented in Table 2-3 indicate that some of these probabilities can beexplained in terms of domestic macroeconomic factors, although a significantpart remains unknown as the mean adjusted R-squared is low (around 0.126).An interesting feature is that different sectors have different sensitivities tothese macro-financial variables, which is an important finding in order to buildcoherent credit risk models.

Place Table 1 About Here

Place Figure 1 About Here

The regressions of default probabilities in each sector are shown in Tables 2and 3. The coefficients for the variable which measures inflation, IPCA, varybetween -2.40 to 2.55, where 17 of them are significant. Also, 22 coefficientshave negative signal, being 16 of them significant. The results for the interestrate are limited between 0.82 to 15.53, 13 of those are significant and all ofthem have positive signal. Regarding the variable for Oil price we can find co-efficients varying from -8.87 to 3.35, where only one is significant and negative.Other 15 negative coefficients can be found within the list. Therefore, this isthe variable with the most heterogenous impact. There are 17 significant ex-change rate coefficients, all of them positive. In total, 26 of the 30 coefficientsfor this variable have positive signal, which implies that exchange rate shocksare an important source of systemic risk within the Brazilian economy.

The variable with the most homogenous impact is the interest rates spread,which is significant for 26 sectors. Also, all of these 26 coefficients are positiveand the range of impact varies from 1.32 to 20.27. Again, it seems that differentsector have very different sensitivities to these macro-financial variables. TheIbovespa index coefficients are in the interval between -14.78 to 2.21, 10 ofthose are significant, all of them part of the 25 with positive signal from the30. The index for the Brazilian Industrial production varies from -8.55 to34.41, 21 of them are positive, but only two significant. Finally, the monthlychange in the consumer confidence index presents coefficients in the range from-9.00 to 0.39. Seven of them are significant and negative. Other 21 sectors alsopresents the same signal direction. The adjusted degree of explanation of theregressions ranges between 0.05 and 0.33.

Place Table 2 and 3 About Here

Place Figures 2, 3, 4 and 5 About Here

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3.2 Panel Data Regressions

We test for the significance of the macro-financial variables within a paneldata framework. We estimate three different models to check for consistencyand robustness of results. We run a random effects model with a first-orderautoregressive (AR(1)) disturbance, a fixed effects model and also a FeasibleGeneralized Least Squares (FGLS) method, allowing for serial correlation andheteroscedasticity in the residuals (White, 1980; Arellano, 2003). We have 100time periods and only 30 sectors and therefore we do not employ the usualArellano-Bond method that corrects for the bias in dynamic panel data models(Arellano and Bond, 1991).

Table 4 presents the results of the panel regressions applied to the 30 sectorsproviding us the sensitivities of default probabilities to the macro-financialvariables. The coefficient of the lag default probability ranges between 0.643and 0.918 according to the method, indicating historically strong persistenceof these probabilities. We expect that increases in interest rates spread shouldimply in greater default probabilities and the first two methods give us posi-tive coefficients although none of those being significant at a 10% level. Also,we hypothesize that the greater the inflation the greater is the default prob-abilities, which is significantly captured by the coefficients of the variable inthe first two methods applied, the coefficients vary between -1.30 and 0.72.

Moreover, we expect that rises in inflation (measured by IPCA index) andin the consumer confidence should rise and diminish, respectively, the defaultprobabilities. All coefficients in the three models show this desired relation,varying, for the first, between -0.000630, which is not significant to 0.37 and,for the second, between -1.57 to -0.49. Also, the rise in price of Crude Oilshould diminish the default probabilities in Brazilian economic sectors, as thecountry has achieved self-sufficiency at this commodity. All three coefficientsare negative, although, in the first model one is almost significant at the 10%level, varying between -0.68 to 0.71. Another hypothesis is that the interestrate should have positive impact in default probabilities. The three methodsused provide significant coefficients with a positive relationship, in accordanceto theoretical predictions. Also the results show that for the monthly changein the production index we obtain the coefficients 11.22, 10.26 and 6.432,respectively, from the first to the third method.

As Brazilian firms are net exporters, our expectation is that a rise of theexchange rate should imply in greater default probability, this effect is signif-icantly captured in all coefficients of this variable in the models, varying from5.634 to 10.09. Furthermore, the stock market index is a leading indicatorfor economic activity, therefore if the stock market index increases we shouldexpect positive growth of the economy and lower default probabilities. Our

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results show that in the three methods applied the signal of the coefficient isthe one desired, negative and the values are -5.100, -4.986 and -3.398, for thefirst to the third model, respectively.

Place Table 4 About Here

3.3 Minimum Spanning Tree

We use the probabilities of default to construct the Minimum Spanning Tree(MST) and the ultrametric hierarchical tree, to identify clusters and con-nections between market sectors. As well as it can be seen with large com-plex financial institutions, including stock and equity markets, and interestrates (Hawkesby et al., 2007; Coelho et al., 2007; Huang et al., 2009; Tabaket al., 2009), shocks in the economy that affect a specific sector tend to affectthe entire cluster spreading to near neighbors.

Our search of these topological arrangements, which are present between thestocks of a given portfolio, is intended to provide empirical evidence about theexistence of economic factors which drive the time evolution of stock prices(Mantegna, 1999). This graphical tool based in the matrix of correlation be-tween probabilities of default can be used to minimize risks for a given portfolioreturn by optimizing the asset weights. Stocks of the minimum risk portfolioare found on the outskirts of a graph, thus it is expected that larger graphslead to a greater diversification potential, as the scope of the stock markettends to eliminate specific risks (Onnela et al., 2003; Jung et al., 2006).


A careful look at the MST and of the Taxonomy Hierarchical Tree show mainlytwo groups of stocks, centralized by International Oil & Gas and Utilities. Bothof these sectors, as we can observe in the individual regressions, suffer greatimpact due to variations of the exchange rate and of the industrial productionindex which are highly significant variables of all the three panel regressions ofTable 4. The connection between those two groups is made by the Banks andthe Speciality Financials sectors playing its role as financial intermediaries.

Also, we can observe in Figure 6 that there is a small cluster centralized by Spe-ciality Financials connecting Forestry & Paper and the Tobacco sectors. More-over, many intuitive direct connections are identifiable such as that of Brewerand Beverages sectors, Broadcast & Entertainment and Media, Forestry &Paper and Paper or Iron & Steel and Industrial Metal & Mines sectors. All ofthe latter when compared tend to have similar individual regressions indicat-ing great coherence in the graph that is able to illustrate such relation. Thisis expected as in some cases, specific sectors may possess similar stocks and

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our procedure is able to identify these sectors as they have a large correlationbetween themselves.

The observed groups are homogeneous with respect to industry (althoughwith a few exceptions) and can be divided into subgroups. It is argued thatstocks belonging to the same clusters carry detectable economic informationin the sense that their prices respond, in a statistical point of view, to similareconomic factors. In order to gain some benefit from diversification one shouldbuild portfolios using stocks that are dissimilar and are not fully connectedwith other sectors. Furthermore, these results suggests which sectors may beused to hedge against adverse price movements in specific sectors.


From a credit risk point of view diversification is crucial. Therefore, lend-ing to sectors that belong to specific clusters may imply higher credit risk,which can be avoided by focusing on distant sectors belonging to differentclusters(Bauerle, 2002).

We also present the MST for two distinct periods - 2000 to 2004 and 2004to 2008. Our results suggests that the links between clusters may weaken inspecific cases and in others they may reinforce. Therefore, these results suggestthat the method has to be updated within a certain frequency in order to gainthe full benefits of it. It is a visual method that allows to establish whichsectors should be targeted in order to mitigate risks or enhance investmentperformance.

Place Figures 8 and 9 About Here

4 Conclusions

In this paper we have estimated default probabilities for 30 market sectorsusing an approach based on stock market behavior. The measure is based ona conditional version of the CAPM and provides failure probabilities for eachof the market sectors over the last 8 years. From 2000 to 2008, we observea declining trend for the average of the probabilities. After, we try to im-prove our understanding on the sources of systematic risk in Brazil. Domesticmacroeconomic factors such as the exchange rate and spread were found tobe the most significant variables to explain these probabilities.

We have also estimated panel regressions for the default probabilities usingthree different methods. We could observe great significance in variables suchas the exchange and the interest rate, the national stock market index and

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also in the industrial production index. Furthermore, the fixed effects modelis able to explain 56% of the change in the default probabilities. The MSTand the Taxonomy Hierarchical Tree analysis designed with these probabilitiesalso tell us that the Brazilian sectors cluster together in groups centralized bythe International Oil & Gas and the Utilities sectors.

We believe that the results obtained in this paper are of vital importance torisk management. With the measure of default probabilities it is possible toassess the risk associated to the economic sectors that were analyzed. More-over, with the design of the network involving each sector, the researcher canhave a broader view of the system, being able to observe how sectors are inter-connected and whether they form clusters. This analysis opens possibilities ofminimizing risks associated to loans and investments through diversification.

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Tabak, B. M., Staub, R., 2007. Assessing financial instability: The case ofbrazil. Reasearch in International Business and Finance 21, 188–202.

White, H., 1980. A heteroskedacity-consistent covariance matrix estimator anda direct test for heteroskedacity. Econometrica 48, 817–838.

16

Table 1. Descriptive Statistics

Market Mean Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera

Media 6.4121% 0.2825 9.84E-04 0.05909 1.3166 4.70 40.87578*

Broadcast and Entertainment 6.3935% 0.2823 9.86E-04 0.05895 1.3230 4.73 41.63556*

Retail 1.3392% 0.0972 4.90E-08 0.02163 2.0179 6.54 119.945*

Broadline Retail 0.6059% 0.0478 2.20E-05 0.00954 1.9689 6.80 124.6459*

Water 0.5987% 0.0255 1.15E-03 0.00418 1.9420 7.75 156.9765*

Gas/Water/Multiutilities 0.5702% 0.0343 4.15E-04 0.00541 2.4144 10.91 358.0002*

Tobacco 0.4962% 0.0339 1.97E-04 0.00573 2.3957 9.86 291.9157*

Chemicals 0.4899% 0.0959 3.42E-09 0.01477 4.8665 27.70 2936.999*

Alternative Electricity 0.3970% 0.0262 6.99E-07 0.00493 1.9285 7.15 133.746*

Industrial Good and Services 0.3604% 0.1011 1.27E-09 0.01331 5.7956 38.85 5913.484*

Iron and Steel 0.3388% 0.0899 7.67E-07 0.00982 7.1571 61.99 15351.15*

International Oil and Gas 0.3276% 0.0375 3.50E-05 0.00601 3.4984 16.40 951.9748*

Industrial Metal and Mines 0.3100% 0.0926 1.09E-07 0.01012 7.2482 62.88 15813.79*

Oil and Gas Production 0.3075% 0.0348 2.98E-05 0.00579 3.4647 15.76 878.8194*

Electricity 0.1755% 0.0194 9.19E-09 0.00292 3.2169 16.35 915.2544*

Brewers 0.1522% 0.0315 9.37E-09 0.00444 4.4613 25.43 2428.358*

Beverages 0.1477% 0.0310 8.01E-09 0.00440 4.4610 25.19 2382.577*

Utilities 0.1376% 0.0158 3.48E-09 0.00237 3.3208 17.14 1017.049*

Specification Chemicals 0.1206% 0.0176 1.29E-07 0.00284 3.9555 19.35 1375.074*

Consumer Electricity 0.0841% 0.0196 2.26E-06 0.00247 5.6604 39.00 5933.256*

Basic Resource 0.0673% 0.0103 8.79E-09 0.00180 3.6761 16.60 995.8931*

Paper 0.0545% 0.0168 9.08E-09 0.00230 5.8910 37.79 5620.706*

Financials 0.0377% 0.0126 2.09E-08 0.00163 6.4280 44.82 7977.1*

Personal and Household Goods 0.0321% 0.0033 2.06E-06 0.00051 2.9255 14.63 706.5487*

Food and Drug Retail 0.0277% 0.0072 2.74E-13 0.00102 4.9371 28.84 3188.671*

Telecom 0.0206% 0.0031 2.80E-08 0.00056 4.0631 19.34 1388.173*

Fixed Line Telecommunications 0.0192% 0.0026 1.22E-07 0.00047 3.8555 18.22 1212.877*

Banks 0.0129% 0.0062 2.12E-08 0.00064 8.6251 80.54 26288.89*

Speciality Financials 0.0085% 0.0008 1.07E-07 0.00017 2.7221 9.90 322.0301*

Forestry and Paper 0.0075% 0.0016 1.82E-09 0.00024 4.6063 24.81 2336.458*

Probabilities of default, ranked in decreasing order. The symbol * stands for statistical significance at the 1% level

17

Tab

le2.

Reg

ress

ions

ofD

efau

ltP

roba

bilit

ies

indi

ffere

ntm

arke

tse

ctor

s.M

ark

et

Sta

tist

ics

CIP

CA

∆(R

t)

∆(O

ilt)

∆(E

Rt)

∆(S

prea

dt)

∆(I

BO

Vt)

∆(P

rodu

cti

on)

∆(C

on

fid

en

ce)

R2

Indust

rialM

eta

land

Min

es

coef.

-7.5

62828*

-1.3

04888

14.7

1741**

-2.7

19689

-8.2

54438

8.5

67546**

-7.0

14548

19.6

7821

-8.0

70765***

0.2

03601

p-v

alu

e0.0

000

0.1

783

0.0

391

0.3

966

0.3

648

0.0

390

0.1

651

0.1

856

0.0

897

Iron

and

Ste

el

coef.

-7.3

70158

-0.5

78399

13.9

0868

-2.5

01015

1.7

68415

9.2

36813*

-5.4

66020

16.9

6743

-6.6

75053

0.2

20039

p-v

alu

e0.0

000

0.4

490

0.0

123

0.3

544

0.8

268

0.0

069

0.2

336

0.1

747

0.1

021

Indust

rialG

ood

and

Serv

ices

coef.

-10.8

8001*

0.6

61191

10.5

0364

-8.8

68970**

33.3

1009

20.2

7092*

-2.3

98362

13.7

8652

0.0

44178

0.1

92211

p-v

alu

e0.0

000

0.4

296

0.1

447

0.0

358

0.1

060

0.0

027

0.7

692

0.5

456

0.9

948

Bevera

ges

coef.

-11.1

5409*

1.5

08119*

11.7

1608**

-0.3

31912

21.5

6031

12.1

9394**

-13.6

1594***

34.2

5593

-9.0

02506

0.1

76335

p-v

alu

e0.0

000

0.0

038

0.0

274

0.9

346

0.1

307

0.0

125

0.0

652

0.1

193

0.1

835

Bre

wers

coef.

-11.1

0309*

1.5

80907*

11.2

2537**

-0.3

04574

20.9

2299

11.8

4304**

-13.5

7833*

34.4

1303

-9.0

09758

0.1

72613

p-v

alu

e0.0

000

0.0

017

0.0

311

0.9

390

0.1

388

0.0

141

0.0

634

0.1

187

0.1

784

Oil

and

Gas

Pro

ducti

on

coef.

-7.3

51073*

0.8

95007*

6.1

50756*

-0.5

21231

11.7

5572**

5.2

20727*

-4.6

51838**

6.1

22886

-1.3

81934

0.2

33445

p-v

alu

e0.0

000

0.0

001

0.0

020

0.7

082

0.0

272

0.0

029

0.0

345

0.4

200

0.5

651

Pers

onaland

House

hold

Goods

coef.

-9.1

58406*

-0.1

46870

5.8

77077**

3.0

05556

5.7

59820

4.3

42761**

1.0

29457

3.1

02345

-2.2

41658

0.0

64985

p-v

alu

e0.0

000

0.6

892

0.0

222

0.0

855

0.3

003

0.0

438

0.7

568

0.7

826

0.5

294

Tobacco

coef.

-5.8

34734*

-0.0

27463

3.4

06483***

0.5

74752

8.3

62448**

3.9

62020*

-0.5

69164

1.3

44648

-1.8

64926

0.1

22386

p-v

alu

e0.0

000

0.9

086

0.0

944

0.5

958

0.0

363

0.0

018

0.7

828

0.8

399

0.4

019

Reta

ilcoef.

-7.9

44058*

2.3

88779*

6.3

72544

-0.9

96323

27.1

6017**

14.2

0563*

-7.5

82603

-2.4

66235

-1.5

18712

0.2

24313

p-v

alu

e0.0

000

0.0

046

0.2

165

0.8

052

0.0

169

0.0

064

0.1

799

0.8

581

0.7

132

Food

and

Dru

gR

eta

ilcoef.

-15.8

4976*

1.2

98060

2.6

53975

-1.1

76713

30.1

2627

16.9

0471**

-16.9

7882

-0.3

42065

-9.7

81423

0.0

45478

p-v

alu

e0.0

000

0.3

638

0.8

390

0.8

494

0.2

209

0.0

718

0.2

084

0.9

910

0.2

873

Bro

adline

Reta

ilcoef.

-7.1

18793*

0.6

32811

4.9

47176

-0.7

64007

15.1

3527

12.7

0140*

-4.1

92759

-0.6

12040

0.3

92901

0.2

63384

p-v

alu

e0.0

000

0.2

213

0.1

826

0.7

437

0.1

988

0.0

011

0.2

963

0.9

522

0.9

052

Media

coef.

-3.9

16665*

1.1

46558*

3.5

16254***

0.2

57559

12.5

6217*

4.2

83809*

0.2

12118

4.7

91558

-3.1

36591***

0.2

63298

p-v

alu

e0.0

000

0.0

000

0.0

751

0.8

298

0.0

007

0.0

161

0.9

346

0.4

554

0.0

775

Inte

rnati

onalO

iland

Gas

coef.

-7.3

01793*

0.9

45812*

5.9

55206*

-0.5

65238

12.0

1459**

5.4

61347*

-4.9

5113**

6.3

85547

-1.5

41000

0.2

56625

p-v

alu

e0.0

000

0.0

000

0.0

028

0.6

731

0.0

234

0.0

017

0.0

195

0.3

908

0.5

236

Bro

adcast

and

Ente

rtain

ment

coef.

-3.9

21331*

1.1

50493*

3.5

15430***

0.2

37541

12.5

3776*

4.2

93837**

0.2

23423

4.6

96265

-3.1

19995***

0.2

64847

p-v

alu

e0.0

000

0.0

000

0.0

746

0.8

426

0.0

007

0.0

156

0.9

310

0.4

623

0.0

783

Tele

com

coef.

-11.1

3277*

-0.1

38400

7.0

36593

-0.4

73647

24.0

4516**

11.8

2580***

-6.9

54333

1.8

31297

-8.4

00662***

0.2

10666

p-v

alu

e0.0

000

0.8

102

0.1

140

0.8

735

0.0

308

0.0

046

0.2

110

0.8

913

0.0

653

Fix

ed

Lin

eTele

com

munic

ati

ons

coef.

-10.6

0529*

-0.1

23934

4.8

61569

-0.2

52541

21.4

0344**

9.2

40069*

-4.9

15916

-0.6

74042

-8.0

85648**

0.2

00829

p-v

alu

e0.0

000

0.8

022

0.1

769

0.9

149

0.0

183

0.0

059

0.3

122

0.9

554

0.0

379

Uti

liti

es

coef.

-9.3

56747*

1.6

12017*

5.0

59278

0.8

87485

19.4

2822**

11.0

4391**

-6.5

96252

-4.2

97158

-3.5

91418

0.1

41034

p-v

alu

e0.0

000

0.0

020

0.3

110

0.7

683

0.0

279

0.0

169

0.2

138

0.7

643

0.3

423

Ele

ctr

icity

coef.

-8.9

25697*

1.5

12000*

5.1

64055

1.0

81171

17.4

5961**

10.5

5152**

-5.3

32927

-4.7

69552

-3.2

33894

0.1

35576

p-v

alu

e0.0

000

0.0

026

0.2

837

0.6

944

0.0

331

0.0

161

0.2

936

0.7

220

0.3

692

The

sym

bols

*,*

*,*

**

stand

for

stati

stic

alsi

gnifi

cance

at

1%

,5%

and

10%

levels

,re

specti

vely

.Sta

ndard

err

ors

are

corr

ecte

dfo

rhete

rosc

edast

icity

and

auto

corr

ela

tion

(New

ey

and

West

,1987).

T-s

tati

stic

sare

pro

vid

ed

inpare

nth

ese

s.W

euse

IPC

Aas

am

easu

refo

rin

flati

on,∆

(Rt)

as

the

month

lychange

ofth

e12-m

onth

sin

tere

stra

te,∆

(Oil

t)

as

the

month

lychange

ofth

epri

ce

ofC

rude

Oil

(Bre

nt)

,∆

(ER

t)

as

the

month

lychange

ofth

eexchange

rate

,∆

(Sprea

dt)

as

month

lychange

ofth

esp

read,∆

(IB

OV

t)

the

month

lychange

ofth

ein

dex

for

Sao

Paulo

Sto

ck

Mark

et,

∆(P

rodu

cti

on

t)

as

the

month

lychange

for

the

Bra

zilia

nin

dust

rialpro

ducti

on,∆

(Con

fid

en

ce

t)

as

the

month

lychange

ofth

econsu

mer

confidence

index

and

R2

isth

eadju

sted

degre

eofexpla

nati

on.T

he

exchange

rate

,in

tere

stra

tes

spre

ads

and

the

Bovesp

ain

dex

are

corr

ela

ted.T

here

fore

,befo

rew

euse

inte

rmedia

ryre

gre

ssio

ns

toderi

ve

ort

hogonalexpla

nato

ryvari

able

sfo

rth

ere

gre

ssio

nto

expla

indefa

ult

pro

babilit

ies.

The

∆(I

BO

Vt),

∆(E

Rt)

and

∆(S

prea

dt)

are

the

ort

hogonalre

siduals

ofin

term

edia

ryre

gre

ssio

ns,

avoid

ing

mult

icolineari

ty.

18

Tab

le3.

Reg

ress

ions

ofD

efau

ltP

roba

bilit

ies

indi

ffere

ntm

arke

tse

ctor

s.M

ark

et

Sta

tist

ics

CIP

CA

∆(R

t)

∆(O

ilt)

∆(E

Rt)

∆(S

prea

dt)

∆(I

BO

Vt)

∆(P

rodu

cti

on

t)

∆(C

on

fid

en

ce

t)

R2

Consu

mer

Ele

ctr

icity

coef.

-9.1

62521*

0.7

89416*

0.8

17395

0.5

71575

-0.1

55139

1.3

19393

1.8

84568

-6.9

76217

-0.2

15461

-0.0

27502

p-v

alu

e0.0

000

0.0

078

0.8

189

0.7

367

0.9

831

0.6

673

0.6

439

0.5

501

0.9

344

Alt

ern

ati

ve

Ele

ctr

icity

coef.

-7.5

02533*

1.3

83370*

3.0

22822

0.9

43870

13.9

9614**

7.2

56406**

-4.7

38684

-3.0

11976

-3.5

99439

0.1

41389

p-v

alu

e0.0

000

0.0

005

0.4

289

0.6

500

0.0

323

0.0

258

0.2

196

0.7

784

0.1

735

Gas/

Wate

r/M

ult

iuti

liti

es

coef.

-5.7

97405*

0.4

84764*

1.8

14557

-0.6

05172

10.9

6514*

2.4

70702**

-4.6

37747**

3.9

79922

-2.9

62761**

0.2

92547

p-v

alu

e0.0

000

0.0

009

0.2

134

0.6

093

0.0

000

0.0

254

0.0

102

0.5

279

0.0

270

Wate

rcoef.

-5.4

68333*

0.2

85445*

1.4

98355

-0.2

59834

8.2

07801*

1.1

88850***

-3.3

90596*

3.8

33202

-2.2

26941**

0.2

82767

p-v

alu

e0.0

000

0.0

064

0.1

229

0.7

603

0.0

000

0.0

863

0.0

099

0.4

190

0.0

339

Fin

ancia

lscoef.

-12.0

8784*

1.1

70075***

2.9

69787

1.8

38052

9.2

88916

2.9

45843

-4.0

10657

16.8

571

-8.6

48136

-0.0

11525

p-v

alu

e0.0

000

0.0

624

0.5

392

0.5

104

0.4

991

0.5

405

0.5

223

0.4

187

0.1

327

Banks

coef.

-12.2

4178*

0.5

19403

8.9

51088*

3.3

55064

6.3

75743

7.2

19296***

-2.6

98823

13.4

8979

-5.9

89863

0.0

66957

p-v

alu

e0.0

000

0.3

580

0.0

090

0.1

097

0.5

694

0.0

998

0.6

200

0.3

055

0.1

970

Specia

lity

Fin

ancia

lscoef.

-10.8

6604*

-0.8

64553

6.6

63401

2.2

14343

-8.0

28583

4.7

73156

2.2

1446

19.9

1171

-0.9

35939

0.0

48837

p-v

alu

e0.0

000

0.1

373

0.2

868

0.3

400

0.3

246

0.1

461

0.6

744

0.1

322

0.7

803

Chem

icals

coef.

-9.7

83136*

2.5

48403*

1.3

42104

2.1

02501

29.7

1079**

5.5

72278

-13.2

8910**

3.0

4594

-7.1

88978

0.1

48086

p-v

alu

e0.0

000

0.0

008

0.8

280

0.5

031

0.0

156

0.2

242

0.0

244

0.8

549

0.1

225

Specifi

cati

on

Chem

icals

coef.

-10.3

9271*

1.8

39875*

6.6

88077

1.9

04138

38.5

4339*

14.3

5309*

-5.6

20960

25.2

8971***

-2.7

82904

0.3

35084

p-v

alu

e0.0

000

0.0

002

0.0

815

0.5

711

0.0

022

0.0

018

0.2

592

0.0

645

0.5

298

Basi

cR

eso

urc

ecoef.

-9.3

01607*

-2.1

00143**

15.5

3166**

-0.2

97732

-17.9

3778

7.4

72978***

-9.9

38328*

17.5

4630

-6.2

33412

0.2

04365

p-v

alu

e0.0

000

0.0

236

0.0

278

0.9

271

0.1

108

0.0

872

0.0

806

0.3

046

0.2

343

Fore

stry

and

Paper

coef.

-12.6

7129*

0.0

49076

8.9

99254*

-0.3

49742

28.8

5039**

18.0

3276*

-11.6

049*

5.7

63899**

-2.9

48842

0.3

27440

p-v

alu

e0.0

000

0.9

102

0.0

089

0.8

969

0.0

134

0.0

000

0.0

286

0.6

770

0.5

956

Paper

coef.

-11.3

5351*

0.6

20603

6.9

75848***

-1.6

83909

25.3

2640**

13.4

2433*

-14.7

8334*

-8.5

47073

-7.2

00355

0.2

21552

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e0.0

000

0.2

024

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877

0.6

127

0.0

333

0.0

008

0.0

091

0.5

794

0.2

167

The

sym

bols

*,*

*,*

**

stand

for

stati

stic

alsi

gnifi

cance

at

1%

,5%

and

10%

levels

,re

specti

vely

.Sta

ndard

err

ors

are

corr

ecte

dfo

rhete

rosc

edast

icity

and

auto

corr

ela

tion

(New

ey

and

West

,1987).

T-s

tati

stic

sare

pre

sente

din

pare

nth

ese

s.W

euse

IPC

Aas

am

easu

refo

rin

flati

on,∆

(Rt)

as

the

month

lychange

ofth

e12-m

onth

sin

tere

stra

te,∆

(Oil

t)

as

the

month

lychange

ofth

epri

ce

ofC

rude

Oil

(Bre

nt)

,∆

(ER

t)

as

the

month

lychange

ofth

eexchange

rate

,∆

(Sprea

dt)

as

month

lychange

ofth

esp

read,∆

(IB

OV

t)

the

month

lychange

ofth

ein

dex

for

Sao

Paulo

Sto

ck

Mark

et,

∆(P

rodu

cti

on

t)

as

the

month

lychange

for

the

Bra

zilia

nin

dust

rialpro

ducti

on,∆

(Con

fid

en

ce

t)

as

the

month

lychange

ofth

econsu

mer

confidence

index

and

R2

isth

eadju

sted

degre

eofexpla

nati

on.T

he

exchange

rate

,in

tere

stra

tes

spre

ads

and

the

Bovesp

ain

dex

are

corr

ela

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here

fore

,befo

rew

euse

inte

rmedia

ryre

gre

ssio

ns

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ve

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nato

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able

sfo

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ere

gre

ssio

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expla

indefa

ult

pro

babilit

ies.

The

∆(I

BO

Vt),

∆(E

Rt)

and

∆(S

prea

dt)

are

the

ort

hogonalre

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term

edia

ryre

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ssio

ns,

avoid

ing

mult

icolineari

ty.

19

Table 4. Panel Regressions for Default Probabilities.(1) (2) (3)

VARIABLES PD PD PD

PDt−1 0.643* 0.701* 0.918*

(0.0142) (0.0132) (0.00751)

IPCA 0.369* 0.213* -0.00630

(0.0868) (0.0750) (0.0545)

∆(Rt) 5.156* 5.066* 3.185*

(0.609) (0.568) (0.428)

∆(Oilt) -0.693 -0.707*** -0.679**

(0.434) (0.420) (0.321)

∆(ERt) 10.09* 9.013* 5.634*

(1.222) (1.131) (0.847)

∆(IBOVt) -5.100* -4.986* -3.398*

(0.759) (0.728) (0.555)

∆(Spreadt) 0.724 0.420 -1.292*

(0.518) (0.455) (0.319)

∆(Productiont) 11.22* 10.26* 6.432*

(2.280) (2.305) (1.810)

∆(Confidencet) -1.567** -1.512** -0.491

(0.655) (0.648) (0.502)

C -3.315* -2.743* -0.589*

(0.173) (0.133) (0.0704)

Observations 2940 2940 2940

R2

0.561

Number of sectors 30 30 30

Wald Test 16002*

Modified Wald Test 6478*

Hausman Specification Test 97,33*

This table presents the results regressing macro variables on default probabilities using the (1) Random Effects Modelwith AR(1) disturbance, (2) Fixed Effects Model, (3) FGLS method, allowing for serial correlation and heteroscedasticity.The symbols *,**,*** stand for statistical significance at 1%, 5% and 10% levels, respectively. The values in parenthesisare the standard errors of each variable. We use IPCA as a measure for inflation, ∆(Rt) as the monthly change of the12-months interest rate, ∆(Oilt) as the monthly change of the price of Crude Oil (Brent), ∆(ERt) as the monthly changeof the exchange rate, ∆(Spreadt) as monthly change of the spread, ∆(IBOVt) the monthly change of the index for SaoPaulo Stock Market, ∆(Productiont) as the monthly change for the Brazilian industrial production, ∆(Confidencet) as

the monthly change of the consumer confidence index and R2

is the adjusted degree of explanation.

20

0

0.005

0.01

0.015

0.02

0.025

0.03

Mar-00 Sep-00 Mar-01 Sep-01 Mar-02 Sep-02 Mar-03 Sep-03 Mar-04 Sep-04 Mar-05 Sep-05 Mar-06 Sep-06 Mar-07 Sep-07 Mar-08

Fig. 1. Average of the probabilities of default.

21

03/00 03/02 04/04 05/06 06/08

1E−4

1Industrial Metal & Mines

03/00 03/02 04/04 05/06 06/08

1E−4

1Iron & Steel

03/00 03/02 04/04 05/06 06/08

1E−4

1Industrial Goods & Services

03/00 03/02 04/04 05/06 06/08

1E−4

1Beverages

03/00 03/02 04/04 05/06 06/08

1E−4

1Brewers

03/00 03/02 04/04 05/06 06/08

1E−4

Oil & Gas Production

03/00 03/02 04/04 05/06 06/08

1E−4

1Personal & Household Goods

03/00 03/02 04/04 05/06 06/08

1E−2

1Tobacco

03/00 03/02 04/04 05/06 06/08

1E−4

1Retail

Fig. 2. Probabilities of default in different market sectors, shown in logarithmicscale.

03/00 03/02 04/04 05/06 06/08

1E−6

1Food & Drug Retail

03/00 03/02 04/04 05/06 06/08

1E−2

1Broadline Retail

03/00 03/02 04/04 05/06 06/08

1E−1

1Media

03/00 03/02 04/04 05/06 06/08

1E−2

1International Oil & Gas

03/00 03/02 04/04 05/06 06/08

1E−2

1Broadcast & Entertainment

03/00 03/02 04/04 05/06 06/08

1E−4

1Telecommunication

03/00 03/02 04/04 05/06 06/08

1E−4

1Fixed Line Telecommunication

03/00 03/02 04/04 05/06 06/08

1E−4

1Utilities

03/00 03/02 04/04 05/06 06/08

1E−4

1Electricity


22

03/00 03/02 04/04 05/06 06/08

1E−3

1Consumer Electricity

03/00 03/02 04/04 05/06 06/08

1E−4

1Alternative Electricity

03/00 03/02 04/04 05/06 06/08

1E−2

1Gas/Water/Multiutilities

03/00 03/02 04/04 05/06 06/08

1E−2

1Water

03/00 03/02 04/04 05/06 06/08

1E−4

1Financials

03/00 03/02 04/04 05/06 06/08

1E−5

1Banks

03/00 03/02 04/04 05/06 06/08

1E−4

1Speciality Financials

03/00 03/02 04/04 05/06 06/08

1E−4

1Chemicals

03/00 03/02 04/04 05/06 06/08

1E−4

1Specification Chemicals


03/00 03/02 04/04 05/06 06/08

1E−4

1Basic Resource

03/00 03/02 04/04 05/06 06/08

1E−4

1Forestry & Paper

03/00 03/02 04/04 05/06 06/08

1E−4

1Paper


23

Industrial Metal & Mines

Iron & Steel

Industrial Good & Services

Beverages

Brewers


Personal & Household Goods

Tobacco

Retail

Food & Drug Retail

Broadline Retail

Media

International Oil & Gas

Broadcast & Entertainment

Telecom

Fixed Line Telecommunications

Utilities

Electricity

Consumer Electricity

Alternative Electricity

Gas/Water/Mul Utilities

Water

Financials

Banks

Speciality Financials

Chemicals

Specification Chemicals

Basic Resource

Forestry & Paper

Paper

Fig. 6. Plot of the MST of a network connecting the full sample of probabilities ofdefault for the period from January 3, 2000 to June 30, 2008.

24

Financials

Banks



Tobacco



Industrial Metal and Mines

Iron & Steel

Basic Resources

Forestry & Paper

Paper

Utilities

Electricity


Retail

Broadline

Telecom.

Fixed Line Tel.


Gas/Water/Utilities

Water


Chemicals

Food & Drugs

Media

Broadcast & Ent.

Beverages

Brewers

Industrial Goods and Services

Fig. 7. Plot of the Taxonomy Hierarchical tree of the subdominant ultrametricassociated to the MST of the full sample of probabilities of default.

25

Industrial Metal & Mines

Iron & Steel


Beverages

Brewers



TobaccoRetail

Food & Drug Retail

Broadline Retail

MediaInternational Oil & Gas


Telecom

Fixed Line TelecommunicationsUtilities

Electricity




Water

Financials

Banks


Chemicals


Basic Resource

Forestry & Paper

Paper

Fig. 8. Plot of the MST of a network connecting the full sample of probabilities ofdefault for the period from 2000 to 2004.

26

Industrial Metal & MinesIron & Steel


Beverages

Brewers


Personal & Household GoodsTobacco Retail

Food & Drug Retail

Broadline RetailMedia



Telecom Fixed Line Telecommunications

Utilities

Electricity




Water

Financials

Banks


Chemicals


Basic Resource

Forestry & Paper

Paper

Fig. 9. Plot of the MST of a network connecting the full sample of probabilities ofdefault for the period from 2004 to 2008.

27

28

Banco Central do Brasil

Trabalhos para Discussão Os Trabalhos para Discussão podem ser acessados na internet, no formato PDF,

no endereço: http://www.bc.gov.br

Working Paper Series

Working Papers in PDF format can be downloaded from: http://www.bc.gov.br

1 Implementing Inflation Targeting in Brazil

Joel Bogdanski, Alexandre Antonio Tombini and Sérgio Ribeiro da Costa Werlang

Jul/2000

2 Política Monetária e Supervisão do Sistema Financeiro Nacional no Banco Central do Brasil Eduardo Lundberg Monetary Policy and Banking Supervision Functions on the Central Bank Eduardo Lundberg

Jul/2000

Jul/2000

3 Private Sector Participation: a Theoretical Justification of the Brazilian Position Sérgio Ribeiro da Costa Werlang

Jul/2000

4 An Information Theory Approach to the Aggregation of Log-Linear Models Pedro H. Albuquerque

Jul/2000

5 The Pass-Through from Depreciation to Inflation: a Panel Study Ilan Goldfajn and Sérgio Ribeiro da Costa Werlang

Jul/2000

6 Optimal Interest Rate Rules in Inflation Targeting Frameworks José Alvaro Rodrigues Neto, Fabio Araújo and Marta Baltar J. Moreira

Jul/2000

7 Leading Indicators of Inflation for Brazil Marcelle Chauvet

Sep/2000

8 The Correlation Matrix of the Brazilian Central Bank’s Standard Model for Interest Rate Market Risk José Alvaro Rodrigues Neto

Sep/2000

9 Estimating Exchange Market Pressure and Intervention Activity Emanuel-Werner Kohlscheen

Nov/2000

10 Análise do Financiamento Externo a uma Pequena Economia Aplicação da Teoria do Prêmio Monetário ao Caso Brasileiro: 1991–1998 Carlos Hamilton Vasconcelos Araújo e Renato Galvão Flôres Júnior

Mar/2001

11 A Note on the Efficient Estimation of Inflation in Brazil Michael F. Bryan and Stephen G. Cecchetti

Mar/2001

12 A Test of Competition in Brazilian Banking Márcio I. Nakane

Mar/2001

29

13 Modelos de Previsão de Insolvência Bancária no Brasil Marcio Magalhães Janot

Mar/2001

14 Evaluating Core Inflation Measures for Brazil Francisco Marcos Rodrigues Figueiredo

Mar/2001

15 Is It Worth Tracking Dollar/Real Implied Volatility? Sandro Canesso de Andrade and Benjamin Miranda Tabak

Mar/2001

16 Avaliação das Projeções do Modelo Estrutural do Banco Central do Brasil para a Taxa de Variação do IPCA Sergio Afonso Lago Alves Evaluation of the Central Bank of Brazil Structural Model’s Inflation Forecasts in an Inflation Targeting Framework Sergio Afonso Lago Alves

Mar/2001

Jul/2001

17 Estimando o Produto Potencial Brasileiro: uma Abordagem de Função de Produção Tito Nícias Teixeira da Silva Filho Estimating Brazilian Potential Output: a Production Function Approach Tito Nícias Teixeira da Silva Filho

Abr/2001

Aug/2002

18 A Simple Model for Inflation Targeting in Brazil Paulo Springer de Freitas and Marcelo Kfoury Muinhos

Apr/2001

19 Uncovered Interest Parity with Fundamentals: a Brazilian Exchange Rate Forecast Model Marcelo Kfoury Muinhos, Paulo Springer de Freitas and Fabio Araújo

May/2001

20 Credit Channel without the LM Curve Victorio Y. T. Chu and Márcio I. Nakane

May/2001

21 Os Impactos Econômicos da CPMF: Teoria e Evidência Pedro H. Albuquerque

Jun/2001

22 Decentralized Portfolio Management Paulo Coutinho and Benjamin Miranda Tabak

Jun/2001

23 Os Efeitos da CPMF sobre a Intermediação Financeira Sérgio Mikio Koyama e Márcio I. Nakane

Jul/2001

24 Inflation Targeting in Brazil: Shocks, Backward-Looking Prices, and IMF Conditionality Joel Bogdanski, Paulo Springer de Freitas, Ilan Goldfajn and Alexandre Antonio Tombini

Aug/2001

25 Inflation Targeting in Brazil: Reviewing Two Years of Monetary Policy 1999/00 Pedro Fachada

Aug/2001

26 Inflation Targeting in an Open Financially Integrated Emerging Economy: the Case of Brazil Marcelo Kfoury Muinhos

Aug/2001

27

Complementaridade e Fungibilidade dos Fluxos de Capitais Internacionais Carlos Hamilton Vasconcelos Araújo e Renato Galvão Flôres Júnior

Set/2001

30

28

Regras Monetárias e Dinâmica Macroeconômica no Brasil: uma Abordagem de Expectativas Racionais Marco Antonio Bonomo e Ricardo D. Brito

Nov/2001

29 Using a Money Demand Model to Evaluate Monetary Policies in Brazil Pedro H. Albuquerque and Solange Gouvêa

Nov/2001

30 Testing the Expectations Hypothesis in the Brazilian Term Structure of Interest Rates Benjamin Miranda Tabak and Sandro Canesso de Andrade

Nov/2001

31 Algumas Considerações sobre a Sazonalidade no IPCA Francisco Marcos R. Figueiredo e Roberta Blass Staub

Nov/2001

32 Crises Cambiais e Ataques Especulativos no Brasil Mauro Costa Miranda

Nov/2001

33 Monetary Policy and Inflation in Brazil (1975-2000): a VAR Estimation André Minella

Nov/2001

34 Constrained Discretion and Collective Action Problems: Reflections on the Resolution of International Financial Crises Arminio Fraga and Daniel Luiz Gleizer

Nov/2001

35 Uma Definição Operacional de Estabilidade de Preços Tito Nícias Teixeira da Silva Filho

Dez/2001

36 Can Emerging Markets Float? Should They Inflation Target? Barry Eichengreen

Feb/2002

37 Monetary Policy in Brazil: Remarks on the Inflation Targeting Regime, Public Debt Management and Open Market Operations Luiz Fernando Figueiredo, Pedro Fachada and Sérgio Goldenstein

Mar/2002

38 Volatilidade Implícita e Antecipação de Eventos de Stress: um Teste para o Mercado Brasileiro Frederico Pechir Gomes

Mar/2002

39 Opções sobre Dólar Comercial e Expectativas a Respeito do Comportamento da Taxa de Câmbio Paulo Castor de Castro

Mar/2002

40 Speculative Attacks on Debts, Dollarization and Optimum Currency Areas Aloisio Araujo and Márcia Leon

Apr/2002

41 Mudanças de Regime no Câmbio Brasileiro Carlos Hamilton V. Araújo e Getúlio B. da Silveira Filho

Jun/2002

42 Modelo Estrutural com Setor Externo: Endogenização do Prêmio de Risco e do Câmbio Marcelo Kfoury Muinhos, Sérgio Afonso Lago Alves e Gil Riella

Jun/2002

43 The Effects of the Brazilian ADRs Program on Domestic Market Efficiency Benjamin Miranda Tabak and Eduardo José Araújo Lima

Jun/2002

31

44 Estrutura Competitiva, Produtividade Industrial e Liberação Comercial no Brasil Pedro Cavalcanti Ferreira e Osmani Teixeira de Carvalho Guillén

Jun/2002

45 Optimal Monetary Policy, Gains from Commitment, and Inflation Persistence André Minella

Aug/2002

46 The Determinants of Bank Interest Spread in Brazil Tarsila Segalla Afanasieff, Priscilla Maria Villa Lhacer and Márcio I. Nakane

Aug/2002

47 Indicadores Derivados de Agregados Monetários Fernando de Aquino Fonseca Neto e José Albuquerque Júnior

Set/2002

48 Should Government Smooth Exchange Rate Risk? Ilan Goldfajn and Marcos Antonio Silveira

Sep/2002

49 Desenvolvimento do Sistema Financeiro e Crescimento Econômico no Brasil: Evidências de Causalidade Orlando Carneiro de Matos

Set/2002

50 Macroeconomic Coordination and Inflation Targeting in a Two-Country Model Eui Jung Chang, Marcelo Kfoury Muinhos and Joanílio Rodolpho Teixeira

Sep/2002

51 Credit Channel with Sovereign Credit Risk: an Empirical Test Victorio Yi Tson Chu

Sep/2002

52 Generalized Hyperbolic Distributions and Brazilian Data José Fajardo and Aquiles Farias

Sep/2002

53 Inflation Targeting in Brazil: Lessons and Challenges André Minella, Paulo Springer de Freitas, Ilan Goldfajn and Marcelo Kfoury Muinhos

Nov/2002

54 Stock Returns and Volatility Benjamin Miranda Tabak and Solange Maria Guerra

Nov/2002

55 Componentes de Curto e Longo Prazo das Taxas de Juros no Brasil Carlos Hamilton Vasconcelos Araújo e Osmani Teixeira de Carvalho de Guillén

Nov/2002

56 Causality and Cointegration in Stock Markets: the Case of Latin America Benjamin Miranda Tabak and Eduardo José Araújo Lima

Dec/2002

57 As Leis de Falência: uma Abordagem Econômica Aloisio Araujo

Dez/2002

58 The Random Walk Hypothesis and the Behavior of Foreign Capital Portfolio Flows: the Brazilian Stock Market Case Benjamin Miranda Tabak

Dec/2002

59 Os Preços Administrados e a Inflação no Brasil Francisco Marcos R. Figueiredo e Thaís Porto Ferreira

Dez/2002

60 Delegated Portfolio Management Paulo Coutinho and Benjamin Miranda Tabak

Dec/2002

32

61 O Uso de Dados de Alta Freqüência na Estimação da Volatilidade e do Valor em Risco para o Ibovespa João Maurício de Souza Moreira e Eduardo Facó Lemgruber

Dez/2002

62 Taxa de Juros e Concentração Bancária no Brasil Eduardo Kiyoshi Tonooka e Sérgio Mikio Koyama

Fev/2003

63 Optimal Monetary Rules: the Case of Brazil Charles Lima de Almeida, Marco Aurélio Peres, Geraldo da Silva e Souza and Benjamin Miranda Tabak

Feb/2003

64 Medium-Size Macroeconomic Model for the Brazilian Economy Marcelo Kfoury Muinhos and Sergio Afonso Lago Alves

Feb/2003

65 On the Information Content of Oil Future Prices Benjamin Miranda Tabak

Feb/2003

66 A Taxa de Juros de Equilíbrio: uma Abordagem Múltipla Pedro Calhman de Miranda e Marcelo Kfoury Muinhos

Fev/2003

67 Avaliação de Métodos de Cálculo de Exigência de Capital para Risco de Mercado de Carteiras de Ações no Brasil Gustavo S. Araújo, João Maurício S. Moreira e Ricardo S. Maia Clemente

Fev/2003

68 Real Balances in the Utility Function: Evidence for Brazil Leonardo Soriano de Alencar and Márcio I. Nakane

Feb/2003

69 r-filters: a Hodrick-Prescott Filter Generalization Fabio Araújo, Marta Baltar Moreira Areosa and José Alvaro Rodrigues Neto

Feb/2003

70 Monetary Policy Surprises and the Brazilian Term Structure of Interest Rates Benjamin Miranda Tabak

Feb/2003

71 On Shadow-Prices of Banks in Real-Time Gross Settlement Systems Rodrigo Penaloza

Apr/2003

72 O Prêmio pela Maturidade na Estrutura a Termo das Taxas de Juros Brasileiras Ricardo Dias de Oliveira Brito, Angelo J. Mont'Alverne Duarte e Osmani Teixeira de C. Guillen

Maio/2003

73 Análise de Componentes Principais de Dados Funcionais – uma Aplicação às Estruturas a Termo de Taxas de Juros Getúlio Borges da Silveira e Octavio Bessada

Maio/2003

74 Aplicação do Modelo de Black, Derman & Toy à Precificação de Opções Sobre Títulos de Renda Fixa

Octavio Manuel Bessada Lion, Carlos Alberto Nunes Cosenza e César das Neves

Maio/2003

75 Brazil’s Financial System: Resilience to Shocks, no Currency Substitution, but Struggling to Promote Growth Ilan Goldfajn, Katherine Hennings and Helio Mori

Jun/2003

33

76 Inflation Targeting in Emerging Market Economies Arminio Fraga, Ilan Goldfajn and André Minella

Jun/2003

77 Inflation Targeting in Brazil: Constructing Credibility under Exchange Rate Volatility André Minella, Paulo Springer de Freitas, Ilan Goldfajn and Marcelo Kfoury Muinhos

Jul/2003

78 Contornando os Pressupostos de Black & Scholes: Aplicação do Modelo de Precificação de Opções de Duan no Mercado Brasileiro Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo, Antonio Carlos Figueiredo, Eduardo Facó Lemgruber

Out/2003

79 Inclusão do Decaimento Temporal na Metodologia Delta-Gama para o Cálculo do VaR de Carteiras Compradas em Opções no Brasil Claudio Henrique da Silveira Barbedo, Gustavo Silva Araújo, Eduardo Facó Lemgruber

Out/2003

80 Diferenças e Semelhanças entre Países da América Latina: uma Análise de Markov Switching para os Ciclos Econômicos de Brasil e Argentina Arnildo da Silva Correa

Out/2003

81 Bank Competition, Agency Costs and the Performance of the Monetary Policy Leonardo Soriano de Alencar and Márcio I. Nakane

Jan/2004

82 Carteiras de Opções: Avaliação de Metodologias de Exigência de Capital no Mercado Brasileiro Cláudio Henrique da Silveira Barbedo e Gustavo Silva Araújo

Mar/2004

83 Does Inflation Targeting Reduce Inflation? An Analysis for the OECD Industrial Countries Thomas Y. Wu

May/2004

84 Speculative Attacks on Debts and Optimum Currency Area: a Welfare Analysis Aloisio Araujo and Marcia Leon

May/2004

85 Risk Premia for Emerging Markets Bonds: Evidence from Brazilian Government Debt, 1996-2002 André Soares Loureiro and Fernando de Holanda Barbosa

May/2004

86 Identificação do Fator Estocástico de Descontos e Algumas Implicações sobre Testes de Modelos de Consumo Fabio Araujo e João Victor Issler

Maio/2004

87 Mercado de Crédito: uma Análise Econométrica dos Volumes de Crédito Total e Habitacional no Brasil Ana Carla Abrão Costa

Dez/2004

88 Ciclos Internacionais de Negócios: uma Análise de Mudança de Regime Markoviano para Brasil, Argentina e Estados Unidos Arnildo da Silva Correa e Ronald Otto Hillbrecht

Dez/2004

89 O Mercado de Hedge Cambial no Brasil: Reação das Instituições Financeiras a Intervenções do Banco Central Fernando N. de Oliveira

Dez/2004

34

90 Bank Privatization and Productivity: Evidence for Brazil Márcio I. Nakane and Daniela B. Weintraub

Dec/2004

91 Credit Risk Measurement and the Regulation of Bank Capital and Provision Requirements in Brazil – a Corporate Analysis Ricardo Schechtman, Valéria Salomão Garcia, Sergio Mikio Koyama and Guilherme Cronemberger Parente

Dec/2004

92

Steady-State Analysis of an Open Economy General Equilibrium Model for Brazil Mirta Noemi Sataka Bugarin, Roberto de Goes Ellery Jr., Victor Gomes Silva, Marcelo Kfoury Muinhos

Apr/2005

93 Avaliação de Modelos de Cálculo de Exigência de Capital para Risco Cambial Claudio H. da S. Barbedo, Gustavo S. Araújo, João Maurício S. Moreira e Ricardo S. Maia Clemente

Abr/2005

94 Simulação Histórica Filtrada: Incorporação da Volatilidade ao Modelo Histórico de Cálculo de Risco para Ativos Não-Lineares Claudio Henrique da Silveira Barbedo, Gustavo Silva Araújo e Eduardo Facó Lemgruber

Abr/2005

95 Comment on Market Discipline and Monetary Policy by Carl Walsh Maurício S. Bugarin and Fábia A. de Carvalho

Apr/2005

96 O que É Estratégia: uma Abordagem Multiparadigmática para a Disciplina Anthero de Moraes Meirelles

Ago/2005

97 Finance and the Business Cycle: a Kalman Filter Approach with Markov Switching Ryan A. Compton and Jose Ricardo da Costa e Silva

Aug/2005

98 Capital Flows Cycle: Stylized Facts and Empirical Evidences for Emerging Market Economies Helio Mori e Marcelo Kfoury Muinhos

Aug/2005

99 Adequação das Medidas de Valor em Risco na Formulação da Exigência de Capital para Estratégias de Opções no Mercado Brasileiro Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo,e Eduardo Facó Lemgruber

Set/2005

100 Targets and Inflation Dynamics Sergio A. L. Alves and Waldyr D. Areosa

Oct/2005

101 Comparing Equilibrium Real Interest Rates: Different Approaches to Measure Brazilian Rates Marcelo Kfoury Muinhos and Márcio I. Nakane

Mar/2006

102 Judicial Risk and Credit Market Performance: Micro Evidence from Brazilian Payroll Loans Ana Carla A. Costa and João M. P. de Mello

Apr/2006

103 The Effect of Adverse Supply Shocks on Monetary Policy and Output Maria da Glória D. S. Araújo, Mirta Bugarin, Marcelo Kfoury Muinhos and Jose Ricardo C. Silva

Apr/2006

35

104 Extração de Informação de Opções Cambiais no Brasil Eui Jung Chang e Benjamin Miranda Tabak

Abr/2006

105 Representing Roommate’s Preferences with Symmetric Utilities José Alvaro Rodrigues Neto

Apr/2006

106 Testing Nonlinearities Between Brazilian Exchange Rates and Inflation Volatilities Cristiane R. Albuquerque and Marcelo Portugal

May/2006

107 Demand for Bank Services and Market Power in Brazilian Banking Márcio I. Nakane, Leonardo S. Alencar and Fabio Kanczuk

Jun/2006

108 O Efeito da Consignação em Folha nas Taxas de Juros dos Empréstimos Pessoais Eduardo A. S. Rodrigues, Victorio Chu, Leonardo S. Alencar e Tony Takeda

Jun/2006

109 The Recent Brazilian Disinflation Process and Costs Alexandre A. Tombini and Sergio A. Lago Alves

Jun/2006

110 Fatores de Risco e o Spread Bancário no Brasil Fernando G. Bignotto e Eduardo Augusto de Souza Rodrigues

Jul/2006

111 Avaliação de Modelos de Exigência de Capital para Risco de Mercado do Cupom Cambial Alan Cosme Rodrigues da Silva, João Maurício de Souza Moreira e Myrian Beatriz Eiras das Neves

Jul/2006

112 Interdependence and Contagion: an Analysis of Information Transmission in Latin America's Stock Markets Angelo Marsiglia Fasolo

Jul/2006

113 Investigação da Memória de Longo Prazo da Taxa de Câmbio no Brasil Sergio Rubens Stancato de Souza, Benjamin Miranda Tabak e Daniel O. Cajueiro

Ago/2006

114 The Inequality Channel of Monetary Transmission Marta Areosa and Waldyr Areosa

Aug/2006

115 Myopic Loss Aversion and House-Money Effect Overseas: an Experimental Approach José L. B. Fernandes, Juan Ignacio Peña and Benjamin M. Tabak

Sep/2006

116 Out-Of-The-Money Monte Carlo Simulation Option Pricing: the Join Use of Importance Sampling and Descriptive Sampling Jaqueline Terra Moura Marins, Eduardo Saliby and Joséte Florencio dos Santos

Sep/2006

117 An Analysis of Off-Site Supervision of Banks’ Profitability, Risk and Capital Adequacy: a Portfolio Simulation Approach Applied to Brazilian Banks Theodore M. Barnhill, Marcos R. Souto and Benjamin M. Tabak

Sep/2006

118 Contagion, Bankruptcy and Social Welfare Analysis in a Financial Economy with Risk Regulation Constraint Aloísio P. Araújo and José Valentim M. Vicente

Oct/2006

36

119 A Central de Risco de Crédito no Brasil: uma Análise de Utilidade de Informação Ricardo Schechtman

Out/2006

120 Forecasting Interest Rates: an Application for Brazil Eduardo J. A. Lima, Felipe Luduvice and Benjamin M. Tabak

Oct/2006

121 The Role of Consumer’s Risk Aversion on Price Rigidity Sergio A. Lago Alves and Mirta N. S. Bugarin

Nov/2006

122 Nonlinear Mechanisms of the Exchange Rate Pass-Through: a Phillips Curve Model With Threshold for Brazil Arnildo da Silva Correa and André Minella

Nov/2006

123 A Neoclassical Analysis of the Brazilian “Lost-Decades” Flávia Mourão Graminho

Nov/2006

124 The Dynamic Relations between Stock Prices and Exchange Rates: Evidence for Brazil Benjamin M. Tabak

Nov/2006

125 Herding Behavior by Equity Foreign Investors on Emerging Markets Barbara Alemanni and José Renato Haas Ornelas

Dec/2006

126 Risk Premium: Insights over the Threshold José L. B. Fernandes, Augusto Hasman and Juan Ignacio Peña

Dec/2006

127 Uma Investigação Baseada em Reamostragem sobre Requerimentos de Capital para Risco de Crédito no Brasil Ricardo Schechtman

Dec/2006

128 Term Structure Movements Implicit in Option Prices Caio Ibsen R. Almeida and José Valentim M. Vicente

Dec/2006

129 Brazil: Taming Inflation Expectations Afonso S. Bevilaqua, Mário Mesquita and André Minella

Jan/2007

130 The Role of Banks in the Brazilian Interbank Market: Does Bank Type Matter? Daniel O. Cajueiro and Benjamin M. Tabak

Jan/2007

131 Long-Range Dependence in Exchange Rates: the Case of the European Monetary System Sergio Rubens Stancato de Souza, Benjamin M. Tabak and Daniel O. Cajueiro

Mar/2007

132 Credit Risk Monte Carlo Simulation Using Simplified Creditmetrics’ Model: the Joint Use of Importance Sampling and Descriptive Sampling Jaqueline Terra Moura Marins and Eduardo Saliby

Mar/2007

133 A New Proposal for Collection and Generation of Information on Financial Institutions’ Risk: the Case of Derivatives Gilneu F. A. Vivan and Benjamin M. Tabak

Mar/2007

134 Amostragem Descritiva no Apreçamento de Opções Européias através de Simulação Monte Carlo: o Efeito da Dimensionalidade e da Probabilidade de Exercício no Ganho de Precisão Eduardo Saliby, Sergio Luiz Medeiros Proença de Gouvêa e Jaqueline Terra Moura Marins

Abr/2007

37

135 Evaluation of Default Risk for the Brazilian Banking Sector Marcelo Y. Takami and Benjamin M. Tabak

May/2007

136 Identifying Volatility Risk Premium from Fixed Income Asian Options Caio Ibsen R. Almeida and José Valentim M. Vicente

May/2007

137 Monetary Policy Design under Competing Models of Inflation Persistence Solange Gouvea e Abhijit Sen Gupta

May/2007

138 Forecasting Exchange Rate Density Using Parametric Models: the Case of Brazil Marcos M. Abe, Eui J. Chang and Benjamin M. Tabak

May/2007

139 Selection of Optimal Lag Length inCointegrated VAR Models with Weak Form of Common Cyclical Features Carlos Enrique Carrasco Gutiérrez, Reinaldo Castro Souza and Osmani Teixeira de Carvalho Guillén

Jun/2007

140 Inflation Targeting, Credibility and Confidence Crises Rafael Santos and Aloísio Araújo

Aug/2007

141 Forecasting Bonds Yields in the Brazilian Fixed income Market Jose Vicente and Benjamin M. Tabak

Aug/2007

142 Crises Análise da Coerência de Medidas de Risco no Mercado Brasileiro de Ações e Desenvolvimento de uma Metodologia Híbrida para o Expected Shortfall Alan Cosme Rodrigues da Silva, Eduardo Facó Lemgruber, José Alberto Rebello Baranowski e Renato da Silva Carvalho

Ago/2007

143 Price Rigidity in Brazil: Evidence from CPI Micro Data Solange Gouvea

Sep/2007

144 The Effect of Bid-Ask Prices on Brazilian Options Implied Volatility: a Case Study of Telemar Call Options Claudio Henrique da Silveira Barbedo and Eduardo Facó Lemgruber

Oct/2007

145 The Stability-Concentration Relationship in the Brazilian Banking System Benjamin Miranda Tabak, Solange Maria Guerra, Eduardo José Araújo Lima and Eui Jung Chang

Oct/2007

146 Movimentos da Estrutura a Termo e Critérios de Minimização do Erro de Previsão em um Modelo Paramétrico Exponencial Caio Almeida, Romeu Gomes, André Leite e José Vicente

Out/2007

147 Explaining Bank Failures in Brazil: Micro, Macro and Contagion Effects (1994-1998) Adriana Soares Sales and Maria Eduarda Tannuri-Pianto

Oct/2007

148 Um Modelo de Fatores Latentes com Variáveis Macroeconômicas para a Curva de Cupom Cambial Felipe Pinheiro, Caio Almeida e José Vicente

Out/2007

149 Joint Validation of Credit Rating PDs under Default Correlation Ricardo Schechtman

Oct/2007

38

150 A Probabilistic Approach for Assessing the Significance of Contextual Variables in Nonparametric Frontier Models: an Application for Brazilian Banks Roberta Blass Staub and Geraldo da Silva e Souza

Oct/2007

151 Building Confidence Intervals with Block Bootstraps for the Variance Ratio Test of Predictability

Nov/2007

Eduardo José Araújo Lima and Benjamin Miranda Tabak

152 Demand for Foreign Exchange Derivatives in Brazil: Hedge or Speculation? Fernando N. de Oliveira and Walter Novaes

Dec/2007

153 Aplicação da Amostragem por Importância à Simulação de Opções Asiáticas Fora do Dinheiro Jaqueline Terra Moura Marins

Dez/2007

154 Identification of Monetary Policy Shocks in the Brazilian Market for Bank Reserves Adriana Soares Sales and Maria Tannuri-Pianto

Dec/2007

155 Does Curvature Enhance Forecasting? Caio Almeida, Romeu Gomes, André Leite and José Vicente

Dec/2007

156 Escolha do Banco e Demanda por Empréstimos: um Modelo de Decisão em Duas Etapas Aplicado para o Brasil Sérgio Mikio Koyama e Márcio I. Nakane

Dez/2007

157 Is the Investment-Uncertainty Link Really Elusive? The Harmful Effects of Inflation Uncertainty in Brazil Tito Nícias Teixeira da Silva Filho

Jan/2008

158 Characterizing the Brazilian Term Structure of Interest Rates Osmani T. Guillen and Benjamin M. Tabak

Feb/2008

159 Behavior and Effects of Equity Foreign Investors on Emerging Markets Barbara Alemanni and José Renato Haas Ornelas

Feb/2008

160 The Incidence of Reserve Requirements in Brazil: Do Bank Stockholders Share the Burden? Fábia A. de Carvalho and Cyntia F. Azevedo

Feb/2008

161 Evaluating Value-at-Risk Models via Quantile Regressions Wagner P. Gaglianone, Luiz Renato Lima and Oliver Linton

Feb/2008

162 Balance Sheet Effects in Currency Crises: Evidence from Brazil Marcio M. Janot, Márcio G. P. Garcia and Walter Novaes

Apr/2008

163 Searching for the Natural Rate of Unemployment in a Large Relative Price Shocks’ Economy: the Brazilian Case Tito Nícias Teixeira da Silva Filho

May/2008

164 Foreign Banks’ Entry and Departure: the recent Brazilian experience (1996-2006) Pedro Fachada

Jun/2008

165 Avaliação de Opções de Troca e Opções de Spread Européias e Americanas Giuliano Carrozza Uzêda Iorio de Souza, Carlos Patrício Samanez e Gustavo Santos Raposo

Jul/2008

39

166 Testing Hyperinflation Theories Using the Inflation Tax Curve: a case study Fernando de Holanda Barbosa and Tito Nícias Teixeira da Silva Filho

Jul/2008

167 O Poder Discriminante das Operações de Crédito das Instituições Financeiras Brasileiras Clodoaldo Aparecido Annibal

Jul/2008

168 An Integrated Model for Liquidity Management and Short-Term Asset Allocation in Commercial Banks Wenersamy Ramos de Alcântara

Jul/2008

169 Mensuração do Risco Sistêmico no Setor Bancário com Variáveis Contábeis e Econômicas Lucio Rodrigues Capelletto, Eliseu Martins e Luiz João Corrar

Jul/2008

170 Política de Fechamento de Bancos com Regulador Não-Benevolente: Resumo e Aplicação Adriana Soares Sales

Jul/2008

171 Modelos para a Utilização das Operações de Redesconto pelos Bancos com Carteira Comercial no Brasil Sérgio Mikio Koyama e Márcio Issao Nakane

Ago/2008

172 Combining Hodrick-Prescott Filtering with a Production Function Approach to Estimate Output Gap Marta Areosa

Aug/2008

173 Exchange Rate Dynamics and the Relationship between the Random Walk Hypothesis and Official Interventions Eduardo José Araújo Lima and Benjamin Miranda Tabak

Aug/2008

174 Foreign Exchange Market Volatility Information: an investigation of real-dollar exchange rate Frederico Pechir Gomes, Marcelo Yoshio Takami and Vinicius Ratton Brandi

Aug/2008

175 Evaluating Asset Pricing Models in a Fama-French Framework Carlos Enrique Carrasco Gutierrez and Wagner Piazza Gaglianone

Dec/2008

176 Fiat Money and the Value of Binding Portfolio Constraints Mário R. Páscoa, Myrian Petrassi and Juan Pablo Torres-Martínez

Dec/2008

177 Preference for Flexibility and Bayesian Updating Gil Riella

Dec/2008

178 An Econometric Contribution to the Intertemporal Approach of the Current Account Wagner Piazza Gaglianone and João Victor Issler

Dec/2008

179 Are Interest Rate Options Important for the Assessment of Interest Rate Risk? Caio Almeida and José Vicente

Dec/2008

180 A Class of Incomplete and Ambiguity Averse Preferences Leandro Nascimento and Gil Riella

Dec/2008

181 Monetary Channels in Brazil through the Lens of a Semi-Structural Model André Minella and Nelson F. Souza-Sobrinho

Apr/2009

40

182 Avaliação de Opções Americanas com Barreiras Monitoradas de Forma Discreta Giuliano Carrozza Uzêda Iorio de Souza e Carlos Patrício Samanez

Abr/2009

183 Ganhos da Globalização do Capital Acionário em Crises Cambiais Marcio Janot e Walter Novaes

Abr/2009

184 Behavior Finance and Estimation Risk in Stochastic Portfolio Optimization José Luiz Barros Fernandes, Juan Ignacio Peña and Benjamin Miranda Tabak

Apr/2009

185 Market Forecasts in Brazil: performance and determinants Fabia A. de Carvalho and André Minella

Apr/2009

186 Previsão da Curva de Juros: um modelo estatístico com variáveis macroeconômicas André Luís Leite, Romeu Braz Pereira Gomes Filho e José Valentim Machado Vicente

Maio/2009

187 The Influence of Collateral on Capital Requirements in the Brazilian Financial System: an approach through historical average and logistic regression on probability of default Alan Cosme Rodrigues da Silva, Antônio Carlos Magalhães da Silva, Jaqueline Terra Moura Marins, Myrian Beatriz Eiras da Neves and Giovani Antonio Silva Brito

Jun/2009

188 Pricing Asian Interest Rate Options with a Three-Factor HJM Model Claudio Henrique da Silveira Barbedo, José Valentim Machado Vicente and Octávio Manuel Bessada Lion

Jun/2009

189 Linking Financial and Macroeconomic Factors to Credit Risk Indicators of Brazilian Banks Marcos Souto, Benjamin M. Tabak and Francisco Vazquez

Jul/2009

190 Concentração Bancária, Lucratividade e Risco Sistêmico: uma abordagem de contágio indireto Bruno Silva Martins e Leonardo S. Alencar

Set/2009

191 Concentração e Inadimplência nas Carteiras de Empréstimos dos Bancos Brasileiros Patricia L. Tecles, Benjamin M. Tabak e Roberta B. Staub

Set/2009

192 Inadimplência do Setor Bancário Brasileiro: uma avaliação de suas medidas Clodoaldo Aparecido Annibal

Set/2009

193 Loss Given Default: um estudo sobre perdas em operações prefixadas no mercado brasileiro Antonio Carlos Magalhães da Silva, Jaqueline Terra Moura Marins e Myrian Beatriz Eiras das Neves

Set/2009

194 Testes de Contágio entre Sistemas Bancários – A crise do subprime Benjamin M. Tabak e Manuela M. de Souza

Set/2009

195 From Default Rates to Default Matrices: a complete measurement of Brazilian banks' consumer credit delinquency Ricardo Schechtman

Oct/2009

41

196 The role of macroeconomic variables in sovereign risk Marco S. Matsumura and José Valentim Vicente

Oct/2009

197 Forecasting the Yield Curve for Brazil Daniel O. Cajueiro, Jose A. Divino and Benjamin M. Tabak

Nov/2009

198 Impacto dos Swaps Cambiais na Curva de Cupom Cambial: uma análise segundo a regressão de componentes principais Alessandra Pasqualina Viola, Margarida Sarmiento Gutierrez, Octávio Bessada Lion e Cláudio Henrique Barbedo

Nov/2009

199 Delegated Portfolio Management and Risk Taking Behavior José Luiz Barros Fernandes, Juan Ignacio Peña and Benjamin Miranda Tabak

Dec/2009

200 Evolution of Bank Efficiency in Brazil: A DEA Approach Roberta B. Staub, Geraldo Souza and Benjamin M. Tabak

Dec/2009

201 Efeitos da Globalização na Inflação Brasileira Rafael Santos e Márcia S. Leon

Jan/2010

202 Considerações sobre a Atuação do Banco Central na Crise de 2008 Mário Mesquita e Mario Torós

Mar/2010

203 Hiato do Produto e PIB no Brasil: uma Análise de Dados em Tempo Real Rafael Tiecher Cusinato, André Minella e Sabino da Silva Pôrto Júnior

Abr/2010

204 Fiscal and monetary policy interaction: a simulation based analysis of a two-country New Keynesian DSGE model with heterogeneous households Marcos Valli and Fabia A. de Carvalho

Apr/2010

205 Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions George Athanasopoulos, Osmani Teixeira de Carvalho Guillén, João Victor Issler and Farshid Vahid

Apr/2010

206 Fluctuation Dynamics in US interest rates and the role of monetary policy Daniel Oliveira Cajueiro and Benjamin M. Tabak

Apr/2010

207 Brazilian Strategy for Managing the Risk of Foreign Exchange Rate Exposure During a Crisis Antonio Francisco A. Silva Jr.

Apr/2010

208 Correlação de default: uma investigação empírica de créditos de varejo no Brasil Antonio Carlos Magalhães da Silva, Arnildo da Silva Correa, Jaqueline Terra Moura Marins e Myrian Beatriz Eiras das Neves

Maio/2010

209 Produção Industrial no Brasil: uma análise de dados em tempo real Rafael Tiecher Cusinato, André Minella e Sabino da Silva Pôrto Júnior

Maio/2010

210 Determinants of Bank Efficiency: the case of Brazil Patricia Tecles and Benjamin M. Tabak

May/2010

42

211 Pessimistic Foreign Investors and Turmoil in Emerging Markets: the case of Brazil in 2002 Sandro C. Andrade and Emanuel Kohlscheen

Aug/2010

212 The Natural Rate of Unemployment in Brazil, Chile, Colombia and Venezuela: some results and challenges Tito Nícias Teixeira da Silva

Sep/2010

213 Estimation of Economic Capital Concerning Operational Risk in a Brazilian banking industry case Helder Ferreira de Mendonça, Délio José Cordeiro Galvão and Renato Falci Villela Loures

Oct/2010

214 Do Inflation-linked Bonds Contain Information about Future Inflation? José Valentim Machado Vicente and Osmani Teixeira de Carvalho Guillen

Oct/2010

215 The Effects of Loan Portfolio Concentration on Brazilian Banks’ Return and Risk Benjamin M. Tabak, Dimas M. Fazio and Daniel O. Cajueiro

Oct/2010

216 Cyclical Effects of Bank Capital Buffers with Imperfect Credit Markets:

international evidence A.R. Fonseca, F. González and L. Pereira da Silva

Oct/2010

217 Financial Stability and Monetary Policy – The case of Brazil

Benjamin M. Tabak, Marcela T. Laiz and Daniel O. Cajueiro Oct/2010

218 The Role of Interest Rates in the Brazilian Business Cycles

Nelson F. Souza-Sobrinho

Oct/2010

219 The Brazilian Interbank Network Structure and Systemic Risk Edson Bastos e Santos and Rama Cont

Oct/2010

220 Eficiência Bancária e Inadimplência: testes de Causalidade Benjamin M. Tabak, Giovana L. Craveiro e Daniel O. Cajueiro

Out/2010

221 Financial Instability and Credit Constraint: evidence from the cost of bank financing Bruno S. Martins

Nov/2010

222 O Comportamento Cíclico do Capital dos Bancos Brasileiros R. A. Ferreira, A. C. Noronha, B. M. Tabak e D. O. Cajueiro

Nov/2010

223 Forecasting the Yield Curve with Linear Factor Models Marco Shinobu Matsumura, Ajax Reynaldo Bello Moreira and José Valentim Machado Vicente

Nov/2010

224 Emerging Floaters: pass-throughs and (some) new commodity currencies Emanuel Kohlscheen

Nov/2010

225 Expectativas Inflacionárias e Inflação Implícita no Mercado Brasileiro Flávio de Freitas Val, Claudio Henrique da Silveira Barbedo e Marcelo Verdini Maia

Nov/2010

226 A Macro Stress Test Model of Credit Risk for the Brazilian Banking Sector Francisco Vazquez, Benjamin M.Tabak and Marcos Souto

Nov/2010

43

227 Uma Nota sobre Erros de Previsão da Inflação de Curto Prazo Emanuel Kohlscheen

Nov/2010

228 Forecasting Brazilian Inflation Using a Large Data Set Francisco Marcos Rodrigues Figueiredo

Dec/2010

229 Financial Fragility in a General Equilibrium Model: the Brazilian case Benjamin M. Tabak, Daniel O. Cajueiro and Dimas M. Fazio

Dec/2010

230 Is Inflation Persistence Over? Fernando N. de Oliveira and Myrian Petrassi

Dec/2010

231 Capital Requirements and Business Cycles with Credit Market Imperfections P. R. Agénor, K. Alper and L. Pereira da Silva

Jan/2011

Documents

Modeling Default Probabilities: the case of · PDF fileModeling Default Probabilities: The Case of Brazil Benjamin M. Tabak1, ... value of the probability, but to movements in the