Post on 30-Dec-2015
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READINESS FOR EFFECTIVE COOPERATION – THE NEW
CHALLENGE FOR CENTRAL BANKS:CASE OF GEORGIA
B y N a n a A s l a m a z i s h v i l iH e a d o f M o n e t a r y S t a t i s t i c s D i v i s i o n
N a t i o n a l B a n k o f G e o r g i a
Workshop on the Implementation of the 2008 SNA in EECCA Countries and Linkages with BPM6 and GFSM
20146-8 May 2015, Istanbul, Turkey
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Outline of Presentation
• Introduction• SebStat: Step Forward Towards Innovative
Solution • How the data are structured?• SebStat as an additional data source for SNA
and BOP compilers• SebStat: How does it work?• Lessons Learned and Way Forward
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Introduction
• Against the background of rapidly increasing statistical standards and requirements National Bank of Georgia (NBG) carries out a consistent strategy for the sustainable development of statistics under its mandate.
• Moreover, expanding and improving our data sources and statistical production, in general, we strongly believe that we should think about other compilers of macroeconomic statistics also.
• This task is quite solvable with the recently launched completely new statistical business process model for National Bank of Georgia, so called SebStat.
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SebStat: Step Forward Towards Innovative solution
• SebStat is an innovative statistical business process model for NBG providing full range of possibilities to satisfy requirements of monetary and financial statistics, as well as the needs of various macroeconomic statistics, directly or indirectly.
• This can be achieved by structuring of statistical and financial data using standardized approach for all statistical domain under the NBG’s mandate.
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How the data are structured?
Is it difficult to define data structure properly?The answer is “Yes” and “No”.
In order to build the data structure several phases shall be done:– Identification of peer data groups to create proper
data families for Central Bank’s needs;– Elaboration of the Code Lists for each data families;– Development of appropriate methodology how the
financial instruments should be classified and structured properly by financial institutions;
– The room for further development should be left.
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How the data are structured? (example for monthly financial statement data structuring)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Data
Entry
Data
Cha
ract
eristi
cs
Clie
nts’
Char
acte
ristic
s
Addi
tiona
l Info
Attrib
utes
Financial Statement Data Structure
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How the data are structured? (example for monthly financial statement data structuring)
The structure of financial statement data (FIM_Data Family) consists of:• Data entries
– Data family– Source– Frequency
• Data characteristics– Financial/nonfinancial instruments– Assets/liabilities– Stock/flow– Maturity– Currency
• Client’s characteristics– Residency– Institutional sector– Type of economic activities– Region (if resident)
• Additional info– Additional info on loans– Loans collateral– Range (for loans&Deposits)
• Attributes– Interest rate– Measure type
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How the data are structured: Other data families
FEX_data Family - Foreign Currency
Transactions
• Data entries (data family, source, frequency)
• Type of transactions• Buying prices• Selling prices• Counterpart• Measure type
MTR_Data Family - Money Transfer
Operations
• Data entries (data family, source, frequency)
• Type of transactions• Type of wire transfer• Country (sender/receiver)• Currency• Measure type
BPC_Data Family - Payment Cards’
Statistics
• Data entries (data family, source, frequency)
• Card type and category• Type of transaction• Type of service post• Measure type
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SebStat as an additional data source for SNA and BOP compilers
1. Data sources to calculate the output of financial corporations
FIM_Data Family – Monthly Financial
Statements
FEX_Data Family – Foreign Currency
Transactions
• Output of Financial Institutions
• Financial Intermediation• Central Bank
• Monetary Policy Services• FISIM
• Deposit-taking Corporations• Explicit fees charged in lieu of providing
services• FISIM• Transactions in foreign currencies
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SebStat as an additional data source for SNA and BOP compilers2. Data sources to calculate the part of international transactions of Goods and Services Account of BOP
BPC_Family- Payment
Cards’ Statistics
• Goods and Services Account• Goods
• E-Commerce (to be added)
• Services• Travel• Additional information to financial
intermediation services, related with acquiring of payment cards
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SebStat as an additional data source for SNA and BOP compilers
3. Data sources for calculation of PIB & SIB items of BOP
MTR_Data Family- Money
Transfer Operations
• PIB - Primary Income Balance• Compensation of Employees
• SIB - Secondary Income Balance• Personal Transfers
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Financial/Nonfinancial InstrumentsAssets/Liabilities
Stock/Flow
Maturity
Currency
Residency
Institutional Sector
Type of economic activity
Region
Additional info on loans
Loan’s collateral
Range
Interest rate
Measure type
Monetary Gold&SDRs
Currency
Deposits
Securities other than shares
Loans
Shares and other equity
Insurance technical reserves
Financial derivatives
Other accounts receivable/payable
Nonfinancial assets
Report description (example)
Indicator: Loans
Assets
Stock
Maturity: 10 year and more
Currency: GEL
Counterpart description:
Residency: Resident
Sector: Nonfinancial corporation
Economic activity: Trade
Region: Kakheti
Additional info: SME loan
Collateral: Real estate
Range: 5000-25000
Interest rate:
Measure type: BV (book value)
SebStat: How does it work?
Next Skip Back
Generate
February
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Table 1. Loans granted by commercial banks, Jan 2010-Feb 2015 (Mln GEL)1.01.2010 1.02.2010 … 1.02.2015
Loans, total … … … …
SebStat: How does it work? (example)
01/01/1
0
01/04/1
0
01/07/1
0
01/10/1
0
01/01/1
1
01/04/1
1
01/07/1
1
01/10/1
1
01/01/1
2
01/04/1
2
01/07/1
2
01/10/1
2
01/01/1
3
01/04/1
3
01/07/1
3
01/10/1
3
01/01/1
4
01/04/1
4
01/07/1
4
01/10/1
4
01/01/1
50.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
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Lessons Learned
• Based on Georgian experience, it is obvious, that comprehensive multifunctional statistical data model for Central Bank is best solution in order to meet not only own statistical requirements, but also needs of other macroeconomic statistical systems compilers;
• The right cooperation strategy with data providers is essential, to ensure project success in terms of data relevancy and quality, and readiness for boosting joint effort aimed at strengthening of statistical capacity;
• Close cooperation with SNA, BOP and GFS compilers on the earlier stage of project designing is important to ensure data model comprehensiveness and methodological consistency.
• In addition to high level management support, it is very important to have the backing of international partners to raising awareness of the similar achievements on the national and international level, in order to get more benefit from each other’s experience and knowledge.
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Thank you------
Contact information: Nana Aslamazishvili
tel: (995 32) 2406 251, e-mail: naslamazishvili@nbg.gov.ge