Dangers and Opportunities for the Russian Banking Sector 2007 - 2008

Dangers and Opportunities for the Russian Banking Sector:

2007 - 2008

Dean Fantazzini Alexander Kudrov Andrew Zlotnik

Moscow - December, the 11th 2009

(Due to copyright restrictions, I can not upload the full paper on SSRN, but only the

slides of the presentation at the first Russian Economic Congress, December the 11th

2009, Moscow - Russia.)

Overview of the Presentation

• Introduction

2007-2009: Dangers and Opportunities for the Russian Banking Sector 2


• Introduction

• Current Situation in Russian Banking Sector

2007-2009: Dangers and Opportunities for the Russian Banking Sector 2-a


• Introduction


• Econometric Analyses of the Banking Stocks: the

Zero-Price-Probability (ZPP)

2007-2009: Dangers and Opportunities for the Russian Banking Sector 2-b


• Introduction




• Econometric Analyses of the Banking Stocks: An Extreme Value

Theory Approach to Value at Risk Estimation

2007-2009: Dangers and Opportunities for the Russian Banking Sector 2-c


• Introduction






• Econometric Analyses of the Banking Sectors

2007-2009: Dangers and Opportunities for the Russian Banking Sector 2-d


• Introduction






• Econometric Analyses of the Banking Sectors

• What is Next?

2007-2009: Dangers and Opportunities for the Russian Banking Sector 2-e

Introduction

The Russian banking system is in its worst crisis since 1998: on the one

hand, this is a consequence of global financial and economic crisis; on the

other hand there are specific country factors.

• First of all, Russian economy depends on a relative small number of

industries.

• Secondly, Russian firms have a large amount of foreign debt.

• Furthermore, when oil prices decrease, this then leads to a decline in

the ruble against the dollar and the euro.

• Another problem is represented by the large amount of risky

investments.


Introduction

In the last 10 years, risk managers in Russia (and worldwide, too) were not

really independent and unprejudiced:

⇒ since risk managers know what top management wants (usually, they

want money), this has determined a large increase in risky investments

which has generated high profits, but also high risk.

⇒ The main institutional problem is that risk management measures risk

but does not manage it.

In this regard, this clearly implies that Basel II conception should be

revised and (real!) stress testing should lead to real protection.


Current Situation in Russian Banking Sector

The current total number of banks in Russia hasn’t changed significantly

from the past years before the crisis.

⇒ According to a report by the Central Bank of Russia (CBR), during the

year 2008 the amount of banks decreased by 3.1%, from 1092 banks to

1058 banks in Russia.

These banks were acquired by State banks or by top private banks with

government protection, and their market share was less than 5%.

⇒ The main reasons of these deals were the impossibility of refinancing

(due to the seizure in global credit markets since July), non-optimal

structure of assets and liabilities, as well as low quality risk management.



The Russian authorities have tried to deal with this threat to protect

population, banks and large firms:

⇒ The Deposit Insurance Agency now guarantees the deposits up to

20 000 dollars in each bank, while the local Parliament passed a law

extending the time for the VAT (value added tax) paymentsa.

⇒ Besides, Vnesheconombank (VEB) received $50 bln in refinancing

facilities (5 years at LIBOR + 5).

⇒ The CBR has also taken some measures for increasing liquidity: it has

increased the limit of free budget funds on deposits of Commercial Banks,

enlarged the number of banks for which budget funds are available up to

28, and decreased the rate of legal reserve requirements by 0.5%.aIn this paper, we assume 35 ruble for 1 dollar for current and future events, while

we use 25 to 1 Rub/$ for the period of time before December, 2008.



Furthermore, the CBR and the Ministry of Finance have provided $38 bln

in subordinated long debt financing (maturity at 2019 at 8%): $20 bln for

Sberbank, $8 bln for VTB, $1 bln for Rosselkhozbank and $ 9 bln for other

top banks.

⇒ It is important to highlight that the first 200 Russian banks have

accumulated the 95% of assets, while the first 50 banks have concentrated

the 80% of assets.

On January 1 2009, the total capital of the registered operating credit

institutions amounted to 25.2 billion dollars, more than 20.4% higher than

the level of that capital on January 1, 2008 (see Figures 1 a-b-c-d below).



Figure 1a: Currencies (Ruble or foreign) in which the credits are denominated and distribution of

banking credits by sectors. Sources: Central Bank of Russia, Troika Dialog, Renaissance Capital



Figure 1b: Distribution of banking credits by sectors. Sources: Central Bank of Russia, Troika

Dialog, Renaissance Capital



Figure 1c: Currencies (Ruble or foreign) in which the credits are denominated and distribution of

banking credits by sectors. Sources: Central Bank of Russia, Troika Dialog, Renaissance Capital



Figure 1d: Distribution of banking credits by sectors and banks. Sources: Central Bank of Russia,

Troika Dialog, Renaissance Capital



General trends in Russian Banking sector:

1) The first important trend is the outflow of retail and wholesale deposits

in 2008-4th quarter.

Retail deposits accounted for almost 25% of banks’ finance but, in general,

the situation on the outflow of funds can not be described as extremely

problematic.

The outflow of funds from retail deposits in state-owned banks was about

5-6% in October 2008. With regard to private banks, the outflow was

much more serious, having lost on average 10-12% of retail deposits in

October 2008.



Interestingly, there is no substantial outflow of funds from corporate

deposits in Sberbank, but rather there is a flow of funds in settlement

funds.

⇒ This is consistent with the expectation that corporations are now

sending their funds to those financial institutions which are more stable.

⇒ Consequently, the probability of a fall of the financial sustainability for

many smaller banks has increased, together with the probability of a fall of

the Ruble as a result of large-scale conversion of deposits denominated in

Rubles into dollars and euros.



⇒ However, since January 2009, the Ruble has stabilized thanks to these

contemporaneous events:

1. the ending of large currency conversions by small investors: → most

Russian savers has already converted by then a significant part of their

deposits in $ and Euros, while keeping the remaining part in Ruble for

everyday life and business.

2. Interventions by the CBR to block speculations on the Ruble by

Russian banks: by the end of October 2008, the outflow of money by

foreign investors was largely finished, and the pressure on the Ruble

were mainly due to Russian banks betting on further devaluations.

⇒ It was sufficient an “order call” to tell these banks that the

speculation game had to finish.

We remark that the Ruble has started stabilizing two months before the

world markets started rebounding in March 2009.



2) The second trend is that the wholesale funding (debt market) works only

partially.

The only source of liquidity for the Russian banks is now represented by

the resources of the CBR and the Ministry of Finance.

Banks will not start recovering large-scale lending until the debt market

has stabilized.

In November 2008, the CBR has amended the regulations to support not

only banks with rating assigned by agencies such as S&P, Fitch and

Moody’s, but also banks with ratings assigned by national agencies

recognized by CBR.



→ The consequences of this situation on banks will depend mainly on their

abilities and dimensions.

→ As a result, for example, top banks are forecasted to remain quite

stable, because they are supported by the state and the CBR.

→ So far, the risk of large-scale bankruptcies in the banking sector remains

low and is fully manageable.



3) The third new trend is the new lending structure. Most private banks

have reduced lending activity and their lending portfolio have declined by

5-7% on average in October:

Out of $33 bln. received in November 2008 from the CBR, the largest banks have used

only a third of this amount for lending activities, whereas they used the remaining

part to finance their own debts, mainly debts towards to the Ministry of Finance.

Nevertheless, in October 2008, the two largest state-controlled banks (VTB

and Sberbank) significantly increased lending: both by $5 bln.

→ In general, the aggregate effect on the credit system can be described as

a neutral or slightly negative.

→ However, the majority of new loans were taken by large borrowers,

while small businesses are experiencing significant problems in refinancing

their debts.



4) Another result of the difficult situation for lending is the deteriorating

quality of assets.

According to the aggregate balance sheet for 30 Russian banks issued by the CBR, the

amount of outstanding debt in the major banks loaned to other credit organizations

has increased seven times and reached $0.3 bln., whereas the amount loaned to

non-financial organizations has increased by 25% and amounted to $7 bln.

While such figures are the first signs of a general deterioration in the

quality of credit portfolios, nevertheless its importance for the banking

system is not crucial. For example, the percentage of credit-related crimes

increased only by 15-20%.



5) The last new trend (now partially over) is represented by the fact that

Russian banks increased their assets in the interbank foreign exchange

market, for example by 15-20 bln dollars in October 2008 alone.

The CBR initially did not take effective action to stop or mitigate the

effects of these speculations:

As we said before, in September 2008 the most aggressive players against

the Ruble were foreign banks, but in October 2008 they were Russian

banks.

→ The CBR has since then imposed sanctions on those banks that use

funds from the state support to buy foreign currencies.

→ This measure has proved extremely effective and, together with the

contemporaneous ending of large currency conversions by Russian savers, it

helped stabilizing the Ruble.


Econometric Analyses of the Banking Stocks:the Zero-Price-Probability (ZPP)

The recent default of the food giant Parmalat in the 2003 and Enron in

2002 clearly showed how the debts reported in the certified balanced sheet

can represent only a part of the true debt figures.

In general, the debt values reported in the certified balance sheets are

underestimated for two reasons:

1) to “window dress” the financial health of the company, in the best case;

2) to hide financial fraud, in the worst case (see, Parmalat, Enron, etc.).

Ketz (2003) discusses a wide variety of techniques to hide debts and

financial risk:

⇛ This explains why the default probabilities computed with

KMV-Merton type models (or other balance sheet based models) are

usually underestimated.



Besides, the standard log-normal assumption is not an appropriate

distribution for price dynamics.

Furthermore, heteroskedasticity is not considered at all in the

KMV-Merton type models: increasing volatility and leptokurtosis can be

interpreted as a signal of informed trading (see Biais et al. (2005) and

Hasbrouck (2007), for recent surveys about market microstructure studies).

⇒ Therefore, using accounting data to infer the firm’s default probability

can be misleading and result in a very poor estimate.

⇒ In order to avoid such problems, we use a recent approach proposed in

Fantazzini, DeGiuli and Maggi (2008), Fantazzini (2009) and Fantazzini,

Kudrov and Slotnik (2010), that uses the null price as a default barrier to

separate an operative firm from a defaulted one, and to estimate its default

probability without resorting to accounting data.



If we consider the following two quantities:

ET = AT − BT

E′

T = AT = (AT − BT ) + BT = ET + BT

we can easily see that the meanings and signs of ET and E′

T can be

completely different according to the situation faced by the firm:

Table 3: Financial Meaning and Signs of ET and E′

T

ET = AT − BT E′

T = AT

OPERATIVE Equity belonging Asset value

to shareholders (+) (+)

DEFAULTED Loss given default Equity belonging to Debtholders

for Debtholders (−) (+)

7→ Therefore, we can estimate the Distant to Default (D.D.) simply by

using ET , instead of Merton’s formula [ln(AT ) − ln(BT ) − T · (µE − σE/2)]/σA,

and the default probability by P(ET < 0).



If we are at time t and we want to compute the (implicit) probability at a

given time t + T that the stock price will cross the truncation level of zero,

i.e. p(Pt+T < 0), then

1. Consider a generic conditional model for the differences of prices levels

Xt = Pt − Pt−1, without the log-transformation :

Xt = E [Xt|Ft−1] + εt (1)

εt = H1/2t ηt, ηt ∼ i.i.d(0, 1) (2)

where H1/2t is the conditional standard deviation, while Ft is the

information set available at time t.

2. Simulate a high number N of price trajectories up to time t + T , using

the estimated time series model:

3. The default probability is simply the number of times n out of N when

the price touched or crossed PT = 0 along the simulated trajectory.



Example: 5000 simulated price trajectories, 1-year ahead, for a

risky stock (ZPP∼40%)



This method entails a number of important benefits:

1. We only need the stock prices for estimating the default probability;

2. We do not need any latent default barrier D, or firm’s volatility σA,

like in Merton style models;

3. We can estimate the default probability for any given time horizon

t + T ;

4. We can consider more realistic distributions than the log-normal;

5. We can screen the default risk daily or even intra-daily. The ZPP can

therefore be used as a tool for risk management.

6. Given the face value of the debt BT , we can compute the average loss

given default for debtholders and therefore the average recovery rate.


Zero-Price-Probability (ZPP): Some Recent Examples

Fantazzini, DeGiuli, Maggi (2008) estimate the 1-year ahead ZPP

considering the last 200 / 1000 trading days for four famous defaulted

stocksa:

1. Cirio: 24/09/1999 - 24/07/2003 (Last 1000 days). Second largest

default in the Italian food sector (the first is Parmalat, see below);

2. Enron: 13/02/2001 - 03/12/2001 (Last 200 days). Largest default in

American history.

3. Parmalat : 22/02/2000 - 22/12/2003 (Last 1000 days). Largest default

in Italian history.

4. Swissair : 12/12/2000 - 03/10/2001 (Last 200 days). Largest default in

European Airline Industry.

aAR(p)-GARCH(1,1)/TGARCH(1,1) models with Student’s T distribution

were used. Data from Datastream.



Figure 2: KMV-Merton default probability and ZPP:

CIRIO and PARMALAT



Figure 3: KMV-Merton default probability and ZPP:

ENRON and SWISSAIR



• The KMV-Merton model shows numerical instability due to abrupt

changes in the debt values at the end of the year;

• The log-normal is not an appropriate distribution for price dynamics

⇒ tail underestimation

• Debt values reported in the certified balance sheets are

underestimated, and so are default probabilities



Fantazzini, Kudrov and Zlotnik (2010) examined the development of credit

risk in the last two years (2007-2008), with particular reference to the

Russian banking sector.

They analyze four single banks (one for Russia, one for USA, one for Italy

and one for UK), that represent important cases due to their dimension

and/or financial history:

• Sberbank (Russia)

• Citigroup (USA)

• Unicredit (Italy)

• Royal Bank of Scotland (UK)



Figure 1: Estimated Default Probability by using the ZPP: Citigroup,

RBS, Sberbank and Unicredit



⇒ Second bailout for RBS in January-February 2009

⇒ Citigroup split in two in January 2009 and a new capital infusion may be

required after the recent “Stress tests”

⇒ As for Unicredit and Sberbank, even though their risks of default have

increased after the financial turmoil in October 2008 , nevertheless these risks

have stabilized since then, differently from the previous American and English

banks


Econometric Analyses of the Banking Stocks: An ExtremeValue Theory Approach to Value at Risk Estimation

As a confirmation of these insights, Fantazzini, Kudrov and Zlotnik (2010)

used a completely different methodology based on Extreme Value Theory.

⇒ Robust estimation algorithm for Value at Risk :

1. For every consecutive 250 days (during the considered period of time)

we compute the set of Hill’s estimators (γ(k)) for the extremal index

of the distribution function of negative returns:

Suppose to have, within the considered period, m negative returns

X1, ..., Xm and let X(1) ≤ X(2) ≤ ... ≤ X(m) be their ordered statistics.

Then the set of Hill’s estimators (γ(k)) is defined as follows:

γ(k) =1

k

k∑

i=1

(ln X(m−i+1) − ln X(m−k)), 1 ≤ k ≤ m − 1



Consider the following model for the sequence of Hill’s estimators γ(k):

γ(k) = γ + β1k + εk, k = 1, ..., κ, (3)

where E[γ(k)] = γ + β1k, V ar(εk) = σ2/k and γ is the true value of the

extremal index for the distribution of negative returns.

We can then estimate γ by using the method of weighted least squares with

a weighting κ × κ matrix W , that has (√

1, ...,√

κ) on the main diagonal and

zeroes elsewhere.

2. To estimate the excess level or Value a Risk xp at the probability level p

(0 < p < 1) for the next day, we use the following estimator:

xp =

(

rpn

)γ− 1

1 − 2−γ(X(n−r) − X(n−2r)) + X(n−r) (4)

where n is the number of negative returns, r = [κ/2] ([.]-integer part), γ is

the estimator for the extremal index γ, X(n−r), X(n−2r) are (n − r)- and

(n − 2r)-ordered statistics of the absolute valued positive returns sequence

X1, ..., Xn, respectively.



⇛ The estimator of extremal index γ, used in the first step of the

estimation algorithm for VaR was proposed by Huisman et al. (2001),

where it is recommended to take κ = m/2.

⇒ Instead of selecting an optimal threshold for the Hill’s estimator of the

extremal index, this approach allows to compute an optimal unbiased

estimate of γ on the basis of the Hill’s estimators set (with the thresholds

k = 1, ..., κ).

⇛ On the second step we use the consistent estimator of the excess level xp

proposed by Dekkers and De Haan (1989).



Figure 2: Estimated Value at Risk at the 1% probability level: Citi-

group, RBS, Sberbank and Unicredit.


Econometric Analyses of the Banking Sectors

The structure in Table 3 can be easily generalized to a general sector :

instead of having the equity for a single firm, we can have the equity

belonging to all shareholders of a specific sector composed of n firms, for

example, the financial sector:

Table 4: The ZPP framework extended to a general business SECTOR∑ n

i=1 ET,i =∑ n

i=1(AT,i − BT,i)∑ n

i=1 E′

T,i =∑ n

i=1 AT,i

OPERATIVE Equity belonging SECTOR Asset value

to the SECTOR shareholders (+) (+)

DEFAULTED Loss given default for Equity belonging to the

the SECTOR Debtholders (−) SECTOR Debtholders (+)

⇒ Therefore, by using a sector index instead of a single stock, the ZPP can

be used as an early warning system for systemic default of a general sector.

Fantazzini, Kudrov and Zlotnik (2010) consider the Russian RTS Financial

Index, the American Dow Jones Financial Index, the English FTSE

Banking Index and the Italian MIBTEL Financial Index.



Figure 3: Estimated Default Probability: American English, Russian

and Italian financial sectors indexes



The Russian financial index clearly shows a higher degree of riskiness than

the other markets, that was quite high already at the beginning of 2007

and peaked in October 2008.

⇒ However, this higher risk is mostly due to a higher country risk (Russia

has a rating of BBB+), than the competing countries (US and UK have

AAA, while Italy A+).

Besides, the increases in the default probabilities for the American and

English banking sectors in 2008 are very large and reflect the difficulties

that they have experienced so far.

⇒ Interestingly, the Italian financial sector currently shows the smallest

default probability (although it was still higher than the American and

English ones till July 2008), thus confirming the smaller impact the

subprime crisis has had on Italian banks



Figure 4: Estimated Value at Risk at the 1% probability level: Amer-

ican, English, Russian and Italian financial sectors indexes


What is Next?

1.) Consolidation of Banking sector

The Government has helped stated-controlled banks to buy banks with

troubles. However, the current situation is profitable also for some large

Commercial Banks, which can acquire troubled banks, too.

In general, in terms of stability of the banking system, the risks remain

manageable.

⇒ The top 20-30 Russian banks as well as the strongest regional banks are

most likely to overcome the crisis: for them, the crisis is a good opportunity

to raise their market share and to acquire small banks by negligible price.

⇒ Moreover, the liquidity provided by the state has become the

determining factor in the banking system.


What is Next?

2.) Dangers

• A continued decrease in oil prices may determine a drop in economic

growth, which may last much longer in the absence of cash and credits;

• While we have seen some stabilization in the FOREX market, it is

difficult to expect the recovering of the lending activity immediately.

Most likely, there will be further restructured loans and the declining

quality of the collaterals, with an increase of bad loans in the banks’

portfolios

• A rising unemployment and the deteriorating economic conditions can

lead to a loss of public confidence in the government


What is Next?

3.) Opportunities

⇒ The Russian banks have used the current situation to try to optimize

their expenses.

For example, Sberbank announced the next target values for the main

parameters of efficiency in a long term perspective:

expenses/revenue: 40-45 % (in a previous Sberbank report in 2008q1, this

parameter was set to 51%), assets/employee - $3,3 bln. (it was $0,8 bln.),

total number of employees 200 - 220 thousands (they were 258 thousands).

Besides, VTB announced a cut in its working stuff and re-organization, too.


References

Dekkers A. L. M. and de Haan L. (1989) On the estimation of the extreme-value index

and large quantile estimation, Annals of Statistics, 17(4), 1795-1832.

Elliott, G., Rothenberg, T. and J. H. Stock (1996). Efficient Tests for an

Autoregressive Unit Root, Econometrica, 64, 813-836.

Fantazzini, D., DeGiuli, M.E., and M. Maggi (2008). A New Approach for Firm Value

and Default Probability Estimation beyond Merton Models, Computational

Economics, 31(2), 161-180.

Fantazzini, D. (2009). Forecasting Default Probability without Accounting Data:

Evidence from Russia. In: Stock Market Volatility, Chapman & Hall / CRC, 527-548.

Fantazzini, D., Kudrov, A., and Zlotnik, A. (2010). 2007-2008: Dangers and

Opportunities for the Russian Banking Sector. In: Handbook of Banking Crises,

Chapman-Hall/Taylor and Francis Group, forthcoming.

Granger, C. Terasvirta, T. and Patton, A. (2006). Common factors in conditional

distributions for bivariate time series, Journal of Econometrics, 132(1), 43-57.

Huisman, R., Koedijk, K. G., Kool, C., Palm F. (2001) Tail-Index Estimates in Small

Samples. Journal of Business and Economic Statistics, 19(2), 208-216

Kwiatkowski, D., Phillips, P., Schmidt, P., and Y. Shin (1992). Testing the Null

Hypothesis of Stationary against the Alternative of a Unit Root, Journal of

Econometrics, 54, 159-178.

Sklar, A. (1959). Fonctions de repartition a n dimensions et leurs marges.

Publications de l’Institut de Statistique de l’Universit e de Paris, 8, 229-231.


Appendix. The ZPP applied to Sovereign Default Probabilities:The case of Greece

Given the current hot debate on the Greek sovereign default risk, we

wanted to use the ZPP with the Athens composite stock index (ASE).

⇛ The rationale is to extend the idea of tables 3 and 4 to an entire

nation:

Table 4: The ZPP framework extended to a NATION∑ n

i=1 ET,i =∑ n

i=1(AT,i − BT,i)∑ n

i=1 E′

T,i =∑ n

i=1 AT,i

OPERATIVE Equity belonging NATION Asset value

to the NATION shareholders (+) (+)

DEFAULTED Loss given default for Equity belonging to the

the NATION Debtholders (−) NATION Debtholders (+)

⇛ While a composite stock index is only a proxy for a nation “net

assets”, it surely can serve well to signal the level of sovereign default

risk, given the high liquidity and including the most important and

largest country businesses.



Figure 5: 1-year-ahead ZPP: Greece (05/12/2007 - 09/12/2009)



Figure 6: 1-year-ahead ZPP: Russia (Last 500 trading days before the

default in August 1998)



Figure 7: 1-year-ahead ZPP: Argentina (Last 500 trading days before

the default in December 2001)



DISCLAIMER: The content in this presentation is provided as

general information only and should not be taken as investment

advice. All presentation content shall not be construed as a

recommendation to buy or sell any security or financial instrument,

or to participate in any particular trading or investment strategy.

The ideas expressed in this presentation are solely the opinions of the

author and do not necessarily represent the opinions of sponsors or

firms or public/private institutions affiliated with the author. Any

action that you take as a result of information, or analysis, contained

in this presentation is ultimately your responsibility.


Dangers and Opportunities for the Russian Banking Sector 2007 - 2008

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