Reporting and derivation of data on financial transactions related to banks’ securities holdings.
Date: 2023-07-11
Institutions: European Central Bank
Subjects: European System of Central Banks, data collection, financial derivative, financial transaction, information security, statistical method

Statistics Paper Series 

Antonio Colangelo, Asier Cornejo Pérez, 
Danilo Liberati, Giorgio Nuzzo, 
Antonio Rodríguez Caloca 

Reporting and derivation of data on 
financial transactions related to 
banks’ securities holdings 

No 44 / June 2023 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). 
The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
Contents 

Abstract 

Non-technical summary 

1 

2 

Introduction 

Methodologies for estimating data on financial transactions 

2.1  Method 1: ESA 2010 approach 

2.2  Method 2: Balance sheet method used in BSI statistics 

2.3  Method 3: Simplified balance sheet method used in BSI 

statistics 

2.4  Method 4: SHSS approach 

2.5  Method 5: Modified SHSS approach 

2.6  Comparing the methods 

3 

The dataset 

3.1  Data from the Banca d’Italia 

3.2  Data from SHSS 

3.3  Pricing information from the CSDB 

3.4  Merging the source data 

4 

Main results 

4.1  Comparing the methods for specific securities 

4.2  Aggregated comparisons by instrument type 

Lessons learnt from the comparison exercise 

Conclusions 

5 

6 

Annex 1 – Formalisation of methods to derive transactions on holdings 

of securities 

Annex 2 – Differences between the indirect methods and the benchmark 

References 

Acknowledgements 

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Abstract 

This paper contributes to the ongoing efforts by the European authorities to reduce 
the reporting burden for banks by assessing the statistical methods currently used to 
compile data on financial transactions related to securities holdings. Based on 
statistical information collected from the Banca d’Italia, we compare data on 
purchases of securities net of sales and redemptions reported by banks with 
transaction estimates based on indirect (balance sheet) methods that are permitted 
within the methodological framework of datasets compiled by the European System 
of Central Banks (ESCB). Although the direct method of collecting data on 
transactions is more costly for reporting agents, it produces results which are fully 
aligned with current statistical methodological standards (European System of 
Accounts 2010, ESA 2010). By contrast, the indirect method is a simplified and less 
costly approach. The recent development of high-quality data sources such as the 
ESCB integrated system for the market prices of securities – the Centralised 
Securities Database – has boosted the attractiveness of indirect methods since they 
have the potential to deliver accurate and reliable estimates. The significance of the 
differences between direct collection and indirect compilation of these data is 
analysed in detail for listed ISIN securities that are actively traded on exchanges, by 
also considering the impact of price volatility and trading activity. From an 
aggregated perspective, all indirect methods produce results which are comparable 
and consistent with the ESA 2010 methodology for all instrument types. There are 
some minor differences for equity instruments, due to the higher price volatility and 
trading activity associated with these instruments, but the overall aggregated 
dynamics are also well captured by indirect methods in these cases. The results thus 
support implementing simplified reporting solutions that would reduce the burden of 
statistical data collection without jeopardising statistical quality. It should also be 
noted that the differences would be expected to be even smaller if the methods are 
applied at a monthly frequency (as may be the case in future in the context of the 
ESCB Integrated Reporting Framework, for instance) instead of at a quarterly 
frequency, as in our exercise. 

Keywords: micro data, security-by-security data, securities, transaction data. 

JEL codes: C18, C81, G15. 

ECB Statistics Paper Series No 44 

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Non-technical summary 

The European System of Central Banks (ESCB) is currently fully engaged in 
lowering the burden of its collection of statistics from banks1  by developing a 
standardised and integrated reporting framework based on state-of-the-art 
compilation methodologies. One of the areas in which efforts are ongoing is the 
reporting and derivation of data on financial transactions related to banks’ securities 
holdings. 

Data on financial transactions represent a fundamental input for assessing 
macroeconomic development. While the approaches used to collect and compile 
data on transactions differ widely across countries and datasets, they can generally 
be grouped into two categories: the direct collection of data on financial transactions 
from reporting agents, and indirect methods used by statistical compilers to estimate 
information based on available data. While direct collection obviously measures 
transactions in a way which is more accurate and in line with statistical methodology 
(i.e. the requirements of international statistical standards such as the European 
System of Accounts 2010 (ESA 2010)), such an approach places a burden on 
reporting agents. Recent improvements to the sources underlying indirect methods 
now provide additional reasons for reassessing compilation strategies. 

This paper contributes to the ongoing discussions by presenting a novel comparison 
of different available approaches to collecting and compiling transaction data for the 
securities holdings of deposit-taking corporations, and how this might impact the 
compilation of macro and micro statistics. Data on financial transactions collected by 
the Banca d’Italia as part of the national implementation of MFI balance sheet 
statistics are compared with estimates obtained from indirect methods that are 
permitted in the methodological framework for datasets compiled by the ESCB. The 
comparison is performed for listed ISIN2  securities that are actively traded on 
exchanges. 

The main results are twofold. First, it is shown that indirect methods deliver robust 
estimates when compared with true transaction data, for the purpose of compiling 
macroeconomic statistics. This outcome holds even when drilling down at the 
instrument level and across time. The results of this paper thus support the 
implementation of simplified reporting solutions that would reduce the burden of 
statistical data collection on reporting agents, without jeopardising statistical quality. 
From a micro perspective, however, significant differences may arise when trading 
activity and/or price volatility is high. Therefore, for the purposes of performing micro 
analyses, the direct collection of data on financial transactions would seem to 
provide more precise information. At the same time, such data collection would need 
to be justified by concrete analytical needs. 

1    The terms “deposit-taking corporations” and “banks” are used interchangeably throughout this paper. 
2   

International Securities Identification Number. 

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1 

Introduction 

Traditionally, as new user needs have arisen the ESCB has set out statistical 
regulations for banks’ reporting in silos, allowing national central banks (NCBs) to 
collect the information required as part of their national statistical (and/or 
supervisory) reporting frameworks. In practice, this has resulted in extremely costly 
and heterogenous reporting systems for banks, featuring (among other things) 
duplications and overlaps in the reporting, complex reporting schedules and 
processes, and different methodological approaches. 

Data on financial transactions are an essential ingredient for assessing 
macroeconomic developments for monetary, economic and financial stability 
analysis purposes, but at the moment reporting and compiling them is particularly 
challenging. The methodological approaches in place vary significantly across euro 
area countries, statistical domains and instrument types. In some cases, data on 
transactions are collected directly from reporting agents while in other cases national 
central banks (NCBs) derive such data indirectly. This indirect approach usually 
refers to changes in stocks3  which are adjusted for effects relating to revaluations 
(due to variations in prices and exchange rates) and changes in volumes (i.e. write-
offs in the case of loans, and so-called reclassifications). In turn, data on these 
effects are either collected from reporting agents or estimated by the NCBs based on 
information available internally (e.g. exchange rates or market prices for securities) 
or transmitted by reporting agents on an ad hoc basis. 

In the context of the development of the Integrated Reporting Framework (IReF), the 
ESCB is looking at standardising existing national practices in the euro area for the 
compilation of transactions and the corresponding reporting requirements. The goal 
of the IReF programme is, as much as possible, to integrate the ESCB’s existing 
statistical data requirements for banks into a unique and standardised reporting 
framework that would be directly applicable across the euro area 4. A qualitative 
stock-taking survey of the state of play of statistical reporting across domains and 
countries was conducted in 20185  to help design scenarios relating to the various 
collection aspects (e.g. reporting dates, data frequency, derogations, etc.) of a 
possible integrated framework. With regard to the compilation of financial 
transactions related to banks’ securities holdings (i.e. purchases of securities net of 
sales and redemptions), the banking industry and other affected stakeholders 
supported an indirect approach rather than the direct collection of data on 
transactions. However, it became clear that any choice of standardised methodology 
could only be based on a quantitative study that would compare the results of the 
methods available and, specifically, assess the relevant estimates against the 
approach favoured under the ESA 20106, which lays down the statistical accounting 

3    The terms “stocks” and “outstanding amounts” are used interchangeably throughout this document. 
4    For further information see the dedicated page on the ECB’s website and references therein. 
5    See European Central Bank (2019a). 
6    Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013 on the 

European system of national and regional accounts in the European Union, (OJ L 174, 26.6.2013, p. 1). 

ECB Statistics Paper Series No 44 

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standards to be applied in Europe. In this regard, it is worth noting that the ESCB 
Centralised Securities Database (CSDB), a reference integrated system of the 
market prices of securities, has improved its coverage and quality in recent years, 
thus enhancing the attractiveness of indirect methods and calling for a reassessment 
of previous national choices made in respect of compilation strategies for financial 
transactions. 

This paper presents a quantitative study based on a granular dataset collected by 
the Banca d’Italia from resident deposit-taking corporations (i.e. banks) in the context 
of the national implementation of monetary financial institution (MFI) balance sheet 
items (BSI) statistics. The dataset covers security-by-security (s-b-s) monthly data on 
outstanding amounts and transactions according to the ESA 2010 methodology. This 
granular dataset, complemented by information from the CSDB and from the dataset 
on Securities Holdings Statistics by Sector (SHSS), is then also used to estimate 
transactions based on indirect methods which are allowed in the methodological 
framework of SHSS and BSI statistics7  and which are currently in use in several 
euro area countries. As the exercise requires the availability of market data the 
comparison is limited to listed ISIN securities that are actively traded on exchanges. 
This paper offers a detailed analysis of the results of the exercise, the main objective 
being to provide quantitative-based evidence for the choice of methodology that is 
likely to be adopted by the ESCB under the forthcoming IReF. 

The paper is structured as follows. Section 2 introduces the various methods 
considered for the compilation of financial transactions related to securities holdings. 
Section 3 details the main features of the s-b-s dataset used in the exercise. Section 
4 presents the main results, first at the level of individual securities to investigate the 
main drivers of the observed differences, and then on an aggregated basis. Section 
5 discusses the main lessons learnt, and provides some remarks regarding the 
estimation of financial transactions related to securities which are either unlisted or 
not actively traded on exchanges. Section 6 concludes. 

7    See European Central Bank (2019b). 

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2 

Methodologies for estimating data on 
financial transactions 

According to ESA 2010, a “transaction is an economic flow that is an interaction 
between institutional units by mutual agreement” (paragraph 1.66). In particular, a 
“financial transaction between institutional units is a simultaneous creation or 
liquidation of a financial asset and the counterpart liability, or a change in ownership 
of a financial asset, or an assumption of a liability” (paragraph 5.02). 8  Hence, in 
respect of securities holdings any purchase of securities, whether at issuance or on 
the secondary market, as well as any sale or redemption of securities, would be 
treated as a transaction in statistical terms.9 

However, current practices regarding the reporting and compilation of financial 
transactions vary significantly across euro area countries as well as across statistical 
domains and instrument type.10  In some cases, data on transactions are collected 
directly from reporting agents, while in others, NCBs derive transactions indirectly. 
For securities holdings, the indirect approach involves deriving transactions from 
changes in stocks which are adjusted for effects which are not related to 
transactions, namely: 

• 

• 

revaluations, reflecting effects arising from changes in prices and exchange 
rates; 

other changes in volume, reflecting effects where an aggregated time series 
shows a break due to, for example, corporate restructuring, the reclassification 
of counterparties and of assets and liabilities, the correction of reporting errors, 
or the introduction of new statistical concepts or definitions11. 

Data on these effects are either estimated by the NCBs or the ECB based on 
information available internally (e.g. currency breakdowns, exchange rates and 
market prices of securities), or are transmitted by reporting agents as part of the 
official national collection frameworks or on an ad hoc basis. 

This paper considers five different methods that could potentially be adopted by the 
IReF for the compilation of financial transactions. Interpretations are then provided 
for each method, together with some explanations as to how they might translate into 
concrete requirements for reporting agents. The comparison only considers 

8    Similar references can be also found in the Handbook on Securities Statistics in paragraphs 5.10 to 

5.20. 

9    ESA 2010 also stipulates, in paragraph 5.150, how to deal with transactions related to the (de-)listing of 

equity shares. 

10    See Section V of the final report from the ESCB task force on portfolio investment collection systems 

for a detailed discussion of the data collection systems available to collect such information. 

11    Corporate restructuring may refer, for example, to mergers or acquisitions (i.e. take-overs) in which 

either one of the two corporations involved ceases to exist or the two corporations form an entirely new 
corporation. Reclassifications of counterparties involve changes in the country of residency or sector 
classification of an entity. Similarly, reclassifications of assets and liabilities cover modifications 
regarding the instrument type, maturity or currency of denomination. For additional information on the 
concept of reclassifications, please see the Manual on MFI balance sheet statistics. 

ECB Statistics Paper Series No 44 

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securities denominated in euro. Revaluations due to changes in exchange rates can 
therefore be ignored, resulting in much easier calculations. However, the results 
remain robust given the very limited impact of non-euro denominated securities on 
total securities holdings.12  In addition, the exercise does not consider the impact of 
interest accruals on debt securities – in all likelihood, this is extremely limited during 
the periods under consideration given the low interest rates applicable.13 

The methods and their interpretations are formalised in Annex 1. 

2.1 

Method 1: ESA 2010 approach 

This method corresponds to the approach favoured in the ESA 2010 and is the 
benchmark for our comparison exercise. Sales and purchases during the reference 
period are valued at their transaction values. 

The information can be collected from reporting agents either on an aggregated 
basis or s-b-s, distinguishing between gross purchases and gross sales. However, in 
all cases, reporting agents should rely on transaction-by-transaction information to 
compile the data to be reported according to the requirements. Intra-period 
transactions (i.e. sales of securities purchased during the same reference period) are 
also considered in the calculations. This approach is equivalent to collecting all gains 
and losses (realised and unrealised) for holdings during the period and deriving 
transactions indirectly by adjusting stock changes for such revaluation effects. 

Example 

Let us suppose a bank holds 1,000 units of equity security A at the end of a year, 
and that during the first quarter of the next year the following transactions take place 
for that security: a purchase of ten additional units which are held until the end of the 
period, a purchase of two units which are sold during the same period, and a sale of 
five units which were held at the beginning of the period. At the end of the first 
quarter, the bank will hold 1,005 units of security A. Figure 1 shows the market 
prices at the beginning and at the end of the period, and at the points in time when 
the transactions take place. No exchange rate revaluations or other changes in 
volume apply. 

12    As at December 2019 holdings of debt securities and holdings of equity denominated in currencies 
other than the euro by Italian deposit-taking corporations amounted to €32 billion and €15 billion 
respectively, i.e. 5% and 11% respectively of the total holdings of debt securities and equity. 

13    Outstanding amounts of debt securities are affected by interest accruals in the sense that they increase 
when interest accrues and decrease when interest is paid. The equations in Annex 1 should therefore, 
in principle, be amended to reflect the impact of accruals by distinguishing between “dirty” and “clean” 
prices. These effects have not been included in the scope of this paper for the sake of simplicity. See 
also Section 3.3. 

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Figure 1 
Units of equity security A on the balance sheet, transactions during the period and 
corresponding market prices 

Intra-period transaction

Purchase 
1

Purchase 
2

Sale 1

Sale 2

31 
December
1.00

1000

Market prices:

Number of 
securities held:

Number of 
securities 
transacted:

1.01

1.02

1. 03

1.04

10

2

2

5

31 March

1.04

1005

Table 1 
Derivation of transactions under Method 1 

Number of securities held 

Beginning of the period 

Time of Purchase 1 

Time of Purchase 2 

Time of Sale 1 

Time of Sale 2 

End of the period 

Prices (EUR) 

Outstanding amounts 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

Purchase of securities held until end of the 
period 

Purchase 1 

Number of securities 
transacted 

Intra-period transaction 

Sale of securities held at beginning of the 
period 

Value of transactions 

Transactions under Method 1 

Revaluations under Method 1 

Purchase 2 

Sale 1 

Sale 2 

Purchase 1 

Purchase 2 

Sale 1 

Sale 2 

1,000 

1,005 

1.00 

1.01 

1.02 

1.03 

1.04 

1.04 

1,000.00 

1,045.20 

10 

2 

2 

5 

10.10 

2.04 

-2.06 

-5.20 

4.88 

40.32 

As Table 1 shows, the outstanding amount of equity security A increases from 1,000 
at the beginning of the quarter to 1,045.2 at the end of the quarter. The difference in 
stocks is equal to the sum of the total value of the transactions taking place during 
the period (4.88) and the price revaluations (40.32). It is also easy to verify that, in 
line with the description of equation 6 in Annex 1, the price revaluations are equal to 

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the realised gains on the securities sold during the period (5*0.04+2*0.01=0.22) plus 
the unrealised gains on the securities held and those bought during the period 
(995*0.04+10*0.03=40.1). 

2.2 

Method 2: Balance sheet method used in BSI statistics 

This approach considers sales and purchases made during the reference period, 
excluding intra-period transactions, and values them on a s-b-s basis at the relevant 
market prices on the opening and the closing balance sheets respectively. 

This is equivalent to calculating revaluations relating to price changes by only 
accounting for unrealised holding gains on securities that were held throughout the 
period. Once again, the information can be collected from reporting agents on either 
an aggregated basis or s-b-s, but the advantage of the method is that reporting 
agents do not need to trace individual transactions. For each security, the only 
information required is the number of securities sold which were held at the 
beginning of the period, or the number of securities purchased and still held at the 
end of the period (i.e. excluding intra-period transactions), or the number of 
securities held throughout the reference period. Aggregates are derived based on 
market prices at the beginning and at the end of the reference period, rather than on 
market prices at the time sales and purchases take place. 

Example [continued] 

Considering that the bank sells five units of equity security A, the number of 
securities held throughout the period is 995. Based on the information summarised in 
Table 2, price revaluations under Method 2 can be calculated as the unrealised 
gains on these securities (39.80). In turn, transactions are calculated as the 
purchase of ten units valued at the market price on 31 March (1.04) minus the sale 
of five units valued at the market price on 31 December (1.00), equalling 5.40. Once 
again, the sum of the estimated transactions and revaluations is equal to the change 
in outstanding amounts. 

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Table 2 
Derivation of transactions under Method 2 

Number of securities held 

Prices (EUR) 

Beginning of the period 

End of the period 

Outstanding amounts 

Number of securities 
transacted 

Purchase of securities held until end of the 
period 

Purchase 1 

Sale of securities held at beginning of the 
period 

Sale 2 

Number of securities held throughout the period 

Transactions under Method 2 

Revaluations under Method 2 

1,000 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

1,005 

1.00 

1.04 

1,000.00 

1,045.20 

10 

5 

995 

5.40 

39.80 

2.3 

Method 3: Simplified balance sheet method used in BSI 
statistics 

This approach estimates revaluations for price changes as the (unrealised) holding 
gains on securities that are held throughout the period, estimating the number of 
securities held throughout the period as the lower of the number of securities held on 
the opening and the closing balance sheets. 

This represents a simplification of Method 2, in that information on the securities held 
throughout the period (or, equivalently, sold or purchased excluding intra-period 
transactions) is estimated rather than obtained from reporting agents. Method 3 can 
thus be applied by compilers based on s-b-s information on stocks, without any 
additional information required from reporting agents – i.e. to the extent that the 
market prices at the beginning and at the end of the period are known to the 
compilers. Transactions are estimated indirectly by adjusting stock changes for the 
estimated price revaluation effects. 

Example [continued] 

Based on the information summarised in Table 3, under Method 3 price revaluations 
are calculated as the lower of the number of units of equity security A at the 
beginning and at the end of the quarter (1,000) multiplied by the change in prices, 
thus equalling 40. Given that the units of equity security A held increased during the 
quarter, transactions can be calculated based on equation 10 in Annex 1 as the 
increase in units held of equity security A (5) multiplied by the market price at the 
end of the period (1.04), equalling 5.2. In turn, the sum of transactions and price 
revaluations is equal to the change in outstanding amounts. 

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Table 3 
Derivation of transactions under Method 3 

Number of securities held 

Prices (EUR) 

Beginning of the period 

End of the period 

Outstanding amounts 

Transactions under Method 3 

Revaluations under Method 3 

1,000 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

1,005 

1.00 

1.04 

1,000.00 

1,045.20 

5.20 

40.00 

2.4 

Method 4: SHSS approach 

The SHSS approach14  values all sales and purchases made during the reference 
period at the average of the market prices at the beginning and at the end of the 
reference period. This is equivalent to considering, on an s-b-s basis, the difference 
between the number of securities held on the opening and the closing balance 
sheets and multiplying this figure by the average market price. Compilers can apply 
also this approach based on s-b-s information on stocks, without needing to obtain 
any additional information from reporting agents. The price information needed is the 
same as for Method 3 (i.e. the prices at the beginning and the end of the reference 
period). 

Example [continued] 

Using the SHSS methodology, transactions can be calculated from the information 
summarised in Table 4 as the difference between the number of units of equity 
security A held at the beginning and at the end of the period (5) multiplied by the 
average of the market prices at the beginning and at the end of the quarter (1.02), 
equalling 5.1. 

14    The current SHSS Regulation and Guideline allow several alternatives for the data collection and the 
reporting by NCBs. The SHSS system calculates the transactions according to the option chosen by 
the respective NCB. For example, the information on transactions is sometimes that reported by the 
NCB while at other times it is calculated by the system, with different methods used according to the 
data available (e.g. stocks available at a monthly or a quarterly frequency). The calculations may 
therefore differ across countries. Where transactions are calculated by the system and only stocks are 
reported, revaluations due to changes in prices and exchange rates are calculated based on Method 4. 
The methodology followed to compile transactions in the SHSS system is detailed in Deutsche 
Bundesbank (2015), “Securities holding statistics for analysing holdings of securities in Germany and 
Europe: methodology and results”, March, pp. 95-107. 

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Table 4 
Derivation of transactions under Method 4 

Number of securities held 

Prices (EUR) 

Beginning of the period 

End of the period 

1,000 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

Average of prices at beginning and end of the period 

Outstanding amounts 

Transactions under Method 4 

Revaluations under Method 4 

31-Dec 

31-Mar 

1,005 

1.00 

1.04 

1.02 

1,000.00 

1,045.20 

5.10 

40.10 

Based on equation 13 in Annex 1, price revaluations are calculated as the average 
number of equity securities held during the period (1,002.5) multiplied by the change 
in market prices during the period (0.04), equalling 40.1. The sum of transactions 
and price revaluations is equal to the change in outstanding amounts. 

2.5 

Method 5: Modified SHSS approach 

This method is a modified version of Method 4. It values all sales and purchases 
during the reference period at the average of the daily market prices during the 
reference period. 

This is equivalent to considering, on an s-b-s basis, the difference between the 
number of securities held on the opening and the closing balance sheets and 
multiplying this figure by the average of the daily market prices for the reference 
period instead of the simple average of the prices at the beginning and at the end of 
the period (as in Method 4). Also this method is easy for compilers to apply, although 
it relies on the availability of reliable information on daily prices. 

Example [continued] 

Based on the information summarised in Table 5, under Method 5 transactions can 
be calculated as the difference in the number of units of equity security A that are 
held at the beginning and at the end of the period (5) multiplied by the average of the 
daily market prices during the quarter (1.03), equalling 5.15. In this case, 
revaluations can be calculated based on equation 16 in Annex 1 as 40.05. Once 
again, the sum of transactions and price revaluations is equal to the change in 
outstanding amounts. 

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Table 5 
Derivation of transactions under Method 5 

31-Dec 

1,000 

Number of securities held 

Beginning of the period 

Prices (EUR) 

End of the period 

Average of daily prices 

Outstanding amounts 

Transactions under Method 5 

Revaluations under Method 5 

31-Mar 

31-Dec 

31-Mar 

31-Dec 

31-Mar 

1,005 

1.00 

1.04 

1.03 

1,000.00 

1,045.20 

5.15 

40.05 

2.6 

Comparing the methods 

Table 6 provides a quick review of the main features of the five methods and their 
implications in terms of reporting requirements. Obviously, the methods produce 
results which vary according to price volatility, the characteristics of the securities 
and the structure of the transactions. Although market prices may represent a good 
approximation of transaction values at a certain point in time, indirect methods use 
price indicators (e.g. market prices at the beginning and the end of periods, mid-
prices or daily averages) that may introduce some noise into the measurement, in 
particular when there are large market movements. For example, for securities with 
high price volatility, the values at which purchases and sales take place are likely to 
differ greatly from the market price indicators used by Methods 2-5 (see also the 
example provided below). These price indicators may also differ between 
themselves: the market prices on the opening and the closing balance sheets are 
different from the average price during the period, so Methods 4 and 5 would be 
expected to differ from Method 3. Method 5 also tends to differ from Method 4 when 
the distribution of prices during the period is skewed and, therefore, the average of 
the daily prices differs from the simple average of the price at the beginning and the 
price at the end of the period. Similarly, when there is a high turnover (i.e. a high 
number of transactions during the period) of securities, Method 2 does not include 
many transactions that would instead be captured under the ESA 2010 approach, 
and, if the number of securities held at the beginning and at the end of period does 
vary too much, Method 3 creates significant bias by assuming that the lower of the 
number of securities held at the beginning and at the end of the reference period 
represents securities which are held throughout the period. 

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Table 6 
Summary of the methods and implications in terms of reporting requirements 

Transactions 

Revaluations 

Reporting requirements 

Method 1 

All sales and purchase valued at 
transaction value 

Realised and unrealised gains 

Stocks and transactions 

Sales and purchases (excluding 
intra-period transactions) valued at 
opening and closing market prices 
for the period     

Unrealised gains on securities held 
throughout the period 

Stocks and number of securities held 
throughout the period 

Method 2 

Method 3 

Difference in the number of 
securities multiplied by the market 
price at the beginning (end) of the 
period if the number of securities 
decreases (increases) 

Unrealised gains on securities held 
throughout the period, approximated 
as the lower of the number of 
securities held on the opening and 
the closing balance sheets 

Stocks 

Stocks 

Method 4 

Difference in the number of 
securities multiplied by the average 
market price 

Unrealised gains on securities held 
throughout the period, approximated 
as the lower of the number of 
securities held on the opening and 
the closing balance sheets 

Method 5 

Difference in the number of 
securities multiplied by the average 
of daily prices 

Realised and unrealised gains, 
calculated by approximating the 
purchase and sales prices by using 
the average of daily prices 

Stocks 

Example [continued] 

Let us suppose a bank holds 100 units of equity security B at the end of a year, and 
that during the first quarter of the next year the following transactions take place for 
that security: a purchase of three additional units which are held until the end of the 
period, a purchase of two units which are sold during the same period, and a sale of 
one unit which had been held at the beginning of the period. At the end of the first 
quarter, the bank will hold 102 units of equity security B. Figure 2 shows the market 
prices at the beginning and at the end of the period, and at the points in time when 
the transactions take place, plus the averages of prices at the beginning and at the 
end of the quarter, and the average of the daily prices. No exchange rate 
revaluations or other changes in volume apply. 

Equity security B is characterised by a much higher price volatility than equity 
security A. Using the formulas provided in Annex 1, it is easy to calculate the 
transactions that apply under the various methods. Table 7 compares the values for 
the transactions that were calculated for equity securities A and B. The differences 
are much higher for equity security B, ranging from 8.7% for Method 4 to 52.2% for 
Method 2. 

ECB Statistics Paper Series No 44 

14 

 
 
  
 
Figure 2 
Units of equity security B on the balance sheet, transactions during the period and 
corresponding market prices 

Intra-period transaction

Purchase 
1

Purchase 
2

Sale 1

Sale 2

Market prices:

Number of 
securities held:

Number of 
securities 
transacted:

31 
December
10

100

13

3

11

2

14

2

10

1

31 March

15

102

Average of prices at beginning and end of the period: 12.5

Average of daily prices: 12.5

Table 7 
Comparing the derivation of transactions for equity securities A and B 

Transactions 

Differences from Method 1 (%) 

Method 1 

Method 2 

Method 3 

Method 4 

Method 5 

Method 2 

Method 3 

Method 4 

Method 5 

Equity security A 

Equity security B 

4.88 

5.40 

5.20 

5.10 

5.15 

10.7% 

6.6% 

4.5% 

5.5% 

23.00 

35.00 

30.00 

25.00 

27.00 

52.2% 

30.4% 

8.7% 

17.4% 

The differences between the methods will be considered further in Section 4, both at 
the level of individual securities and in the aggregate. Finally, it should be noted that 
Method 2 will not be considered further in this paper as the data collected from the 
Banca d’Italia do not allow for the separate identification of intra-period transactions. 
The method would not, in any case, be very appealing as it would involve tracing 
some information relating to the transactions executed during the period, resulting in 
only limited (or no) benefits compared with Method 1. 

ECB Statistics Paper Series No 44 

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3 

The dataset 

The dataset used in the exercise consists of quarterly s-b-s data of (“alive”15) euro-
denominated listed ISIN securities that are held by Italian deposit-taking corporations 
and are actively traded on exchanges. For each ISIN, the dataset includes 
information on outstanding amounts, the numbers of securities held, and gross 
purchases and sales in each quarter for the period Q4 2017 to Q4 2019. 

The dataset was obtained by merging three data sources: 

• 

• 

• 

data collected by the Banca d’Italia from domestic deposit-taking corporations 
regarding holdings of ISIN and non-ISIN securities, in accordance with the 
requirements laid down in the “Matrice dei conti” national collection framework 
(Banca d’Italia, 2008); 

data on outstanding amounts of ISIN securities held by Italian deposit-taking 
corporations, as available in SHSS; 

daily prices of ISIN securities, as available in the CSDB. 

This section first provides some background on the three data sources, before 
explaining how they are merged into the dataset used in the analyses. The section 
closes by analysing the resulting dataset and examining how it relates to the original 
data sources. 

3.1 

Data from the Banca d’Italia 

The Italian collection framework for data from banks includes detailed requirements 
on securities holdings, the main objective being to provide integrated input data for 
the compilation of BSI and SHSS statistics. In particular, while BSI statistics are 
compiled directly using this s-b-s dataset, SHSS statistics are derived using a more 
complex compilation framework which may result in small differences between the 
two datasets (see also the SHSS subsection below). 

With regard to ISIN securities, data are collected on an s-b-s basis for both listed 
and unlisted securities. Data refer to monthly balance sheet outstanding amounts 
plus aggregated information on gross purchases and sales during the reference 
month (i.e. the data are collected at security level and not at transaction level). 
Outstanding amounts refer to the market (or fair) value of the securities. However, in 
some cases (e.g. unlisted securities or securities for which no market value is 
available) reporting agents provide accounting values. The dataset is complemented 
by the number of equity instruments held underlying the monthly data on outstanding 
amounts.16  Gross purchases and sales are valued at transaction value – which will 

15    Securities are considered to be “not alive” when, for example, they have matured, they have been 

redeemed early, or they have defaulted. 

16    This information is not available for debt securities. 

ECB Statistics Paper Series No 44 

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be equal (or close) to the market price when a market price exists (i.e. consistent 
with Method 1 as described in Section 2.1). The dataset makes it possible to identify 
holdings of own securities (only outstanding amounts are collected for these 
securities), which have been excluded from the compilation of BSI statistics since 
2017, although they are included in SHSS statistics. 

Holdings of non-ISIN securities are normally fairly small, especially in the case of 
debt securities.17  Data are collected on an aggregated basis, broken down 
according to the relevant variables including instrument type, sector and area of 
residency of the debtor. Positions are valued at accounting value while data on 
transactions refer to transaction values. 

The dataset also covers holdings of debt securities arising from the securitisation of 
own loans that have not been derecognised from the balance sheet. The data are 
collected on an s-b-s basis, although they only refer to outstanding amounts. 
Transactions are estimated as the differences in outstanding amounts corrected for 
possible reclassification effects. The holdings fall within the scope of both BSI and 
SHSS statistics. 

3.2 

Data from SHSS 

The SHSS dataset provides s-b-s information on outstanding amounts and 
transactions involving securities held by institutional sectors in the euro area, broken 
down by instrument type, sector and area of residency of the debtor, along with 
further additional classifications.18  The legal basis for collecting SHSS data is laid 
down in Regulation ECB/2012/2419  (and subsequent amendments). This Regulation 
is complemented by Guideline ECB/2013/720  (and subsequent amendments), which 
also sets out the procedures to be followed by NCBs when reporting to the ECB.21 
Data only cover listed ISIN securities and have been collected on a quarterly basis 
since Q4 2013.22 

The integrated input dataset described in the previous subsection represents the 
basis for the data input into SHSS with regard to Italian deposit-taking corporations. 
However, the SHSS compilation process uses CSDB reference data in the 
classification and valuation of the instruments. This could lead to differences 
between the two datasets. In particular, while SHSS data on outstanding amounts 

17    As at December 2019, non-ISIN debt securities accounted for about 0.5% of the total holdings of debt 

securities. The figure for equity securities was around 3% for the same reference period. 

18    See ECB (2015) for comprehensive information on SHSS and the mechanism for collecting s-b-s 

information and various examples of the use of SHSS data from both a monetary and a 
macroprudential perspective. 

19    Regulation (EU) No 1011/2012 of the European Central Bank of 17 October 2012 concerning statistics 

on holdings of securities (ECB/2012/24), (OJ L 305, 1.11.2012, p. 6.). 

20    Guideline (EU) 2018/323 of the European Central Bank of 22 February 2018 amending Guideline 

ECB/2013/7 concerning statistics on holdings of securities (ECB/2018/8) (OJ L 62, 5.3.2018, p. 38). 
21    Securities holdings statistics also include a module relating to holdings of banking groups (i.e. SHSG). 

However, this paper only focuses on the sectoral data module (i.e. SHSS), which is directly comparable 
with the dataset from the Banca d’Italia. 

22    Holdings of non-ISIN securities are also considered in the framework but are reported on a voluntary 

basis. For this reason, they are not considered in the analysis. 

ECB Statistics Paper Series No 44 

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always refer to the market values of securities, as mentioned above the data on 
outstanding amounts of the dataset from the Banca d’Italia are not based on market 
values in some cases.23 

It is also worth mentioning that there are important conceptual and methodological 
differences between SHSS data and BSI statistics. In addition to the more limited 
scope for listed ISIN securities, it should be noted that: 

• 

• 

in terms of counterparty sector, while BSI compiles data according to the debtor, 
SHSS focuses on the issuer of the securities. While in most cases the debtor 
and issuer of a security coincide, debtors sometimes use the services of a third-
party issuer, resulting in differences between the two datasets (e.g. securities 
issued on a fiduciary basis); 

as mentioned above, while BSI excludes holdings of own securities (i.e. 
holdings are recorded on a net basis), such holdings are included in SHSS (i.e. 
holdings are recorded on a gross basis). 

While the first point is less relevant for Italian banks, the second point should be 
borne in mind throughout the remainder of the paper. 

3.3 

Pricing information from the CSDB 

The CSDB is the ESCB’s reference database on ISIN securities. It contains s-b-s 
information on all securities issued by EU residents, including securities likely to be 
held and transacted by EU residents, and securities denominated in euro, whoever 
the issuer may be and wherever such securities are held. It currently contains 
around six million “alive” securities. 

The database was primarily set up to support the compilation of statistics based on 
reference information, although it now also supports other non-statistical needs of 
the ESCB. The dataset covers attributes such as the type of instrument, its market 
price and the issuer. For instance, these attributes are needed for the classification 
of the instrument, its valuation according to ESA 2010 principles and the 
identification of the counterparty for SHSS purposes.24  The ISIN code represents the 
common identifier used to link CSDB information with s-b-s datasets such as SHSS. 
It should also be clarified that the CSDB includes different “price” types that are 
identified on the basis of their quotation: while the market price normally refers to the 
so-called “dirty” price, prices may also refer to the “clean” price (i.e. the price of a 
security net of interest accrued). Dirty prices are used in this paper to reflect the fact 

23    Differences in the classification of the instruments are normally due to errors either in the reporting or in 

the CSDB and disappear over time during the data revision process. 

24    See European Central Bank (2010) for more information. 

ECB Statistics Paper Series No 44 

18 

 
 
 
that outstanding amounts include interest accruals and the impact of the latter on 
changes in outstanding amounts is not considered separately in the analyses.25 

The CSDB is a multisource system containing information received from the different 
national sources (including reporting agents) available to NCBs, internal ECB 
sources (e.g. for information on eligible assets26), commercial data providers and 
rating agencies. Data are processed on a daily basis and consist of around two 
million prices and 400,000 individual record updates. 

It should also be noted that commercial data providers are the primary source of the 
daily market prices included in the CSDB – only limited price information is provided 
by NCBs. The information is therefore typically only available for ISIN securities 
listed on organised markets and actively exchanged, although in some cases 
commercial data providers also provide estimated prices when real market data are 
not available. The CSDB system has also implemented algorithms which estimate 
market prices when these are not available from the usual sources, either through 
cash-flow discounting (“estimated prices”) or by setting the market price to 100 
(“default prices”), as in the case of zero coupon bonds. In addition, while in principle 
the CSDB contains daily prices, in practice data quality is typically ensured only for 
prices at the end of the month, which are currently used in the compilation of 
statistics (e.g. for valuing outstanding amounts of securities at the end of the month). 
Hence, the quality of the daily prices, especially when these prices are partly 
estimated during the month, does have important implications for the analyses 
performed in this paper, as Method 5 relies on the availability of average daily prices, 
which may be influenced by inconsistencies between estimated and actual prices. 

3.4 

Merging the source data 

The three data sources were merged by first restricting their scope to listed ISIN 
securities and adopting a quarterly frequency to reflect the content of SHSS. Only 
listed ISIN securities common to the Banca d’Italia and SHSS datasets were 
considered, thereby excluding holdings of own securities27. For each quarter and for 
each ISIN, information from the Banca d’Italia was considered for outstanding 
amounts, gross purchases and sales. For debt securities, outstanding amounts and 
CSDB pricing information were used to estimate transactions under Methods 3, 4 
and 5, based on the formulas provided in Annex 1. For equity instruments 
transactions under Methods 3, 4 and 5 were calculated using CSDB pricing 
information and the number of securities held as reported to the Banca d’Italia. The 
dataset was further augmented with indicators calculated to take into account the 
price volatility and turnover (or tradability) of the securities during each quarter. 

25    In detail, in the comparison exercise dirty prices are used to estimate the number of debt securities 
held on the opening and the closing balance sheets. These numbers are, in turn, used to estimate 
transactions according to Methods 3, 4 and 5, based on dirty prices. Considering that dirty prices do 
not diverge significantly from clean prices in the period considered in the analyses, the results are 
deemed to be robust for the practical approach taken. 

26    Further information can be found on the dedicated ECB webpage. 
27    As explained above, the dataset from the Banca d’Italia is in fact used as a source for SHSS. Hence, 

only small differences apply given the specific features of the SHSS compilation framework. 

ECB Statistics Paper Series No 44 

19 

 
 
 
These are defined, respectively, as the coefficient of variation (i.e. the standard 
deviation divided by the average) of the daily prices, and the sum of purchases and 
sales divided by the outstanding amounts (which are defined in the calculations as 
the lower of the outstanding amount at the beginning and that at the end of the 
quarter). 

The following filters are applied to obtain a clean dataset. 

1. 

Investment fund shares/units are excluded owing to the relatively small values 
observed (i.e. outstanding amounts of €10-12 billion on average during the 
period) and the low availability of market prices in the CSDB (i.e. investment 
fund shares/units with available prices in the CSDB account for only about a 
quarter of the outstanding amounts for the set of ISINs that are part of the 
merged dataset28). 

2.  Holdings of debt securities arising from the securitisation of own loans that have 
not been derecognised from the balance sheet are not considered either, as the 
dataset from the Banca d’Italia does not contain information on transactions 
based on Method 1. 

3.  For each quarter, a filter was applied for securities for which no actual prices 

were available in the CSDB.29  Given the high coverage of the CSDB, this was 
equivalent to excluding from the analyses securities that were not actively 
traded in specific quarters. The filtering was needed to guarantee the availability 
of pricing information that could be directly used to derive transaction estimates, 
in accordance with the various methods presented in this paper. Such securities 
were only excluded for a specific quarter and not for the entire time period so 
that as many securities as possible can be captured in the comparison. 

4. 

It should be noted that this procedure implies that the pools of securities 
considered are non-comparable across different quarters. In statistical terms, 
this has “reclassification effects” on outstanding amounts in the series. While 
this is not problematic for the purposes of this paper, such effects need to be 
taken into consideration when analysing how the series evolves over time (see 
also Section 4.2). 

5.  A few listed shares and long-term debt securities have also been excluded from 
the merged dataset for the quarters during which they show data quality issues 

28    As at end-December 2019, CSDB total market capitalisation, in euro, for euro area investment funds 
covered 100% of market capitalisation as published in the euro area quarterly financial accounts. The 
CSDB contains market prices for around 140,000 instruments issued by euro area investment funds, 
meaning that coverage is good (and essentially complete) for such funds. However, the pricing 
information for non-EU investment funds is suboptimal due to the way investment funds do business: 
they are often not traded daily, and in most cases they target specific investors, so the main data 
vendors are not interested in buying the prices for their clients. 

29    CSDB default or estimated prices were not considered, thereby ensuring the high quality of the pricing 
information to be used in the exercise, especially in terms of the plausibility of using quarterly average 
prices compared with prices at the beginning and at the end of the period. 

ECB Statistics Paper Series No 44 

20 

 
 
 
in terms of consistency between the data reported on outstanding amounts and 
transactions or in terms of price dynamics in the CSDB.30 

Figure 3 
Merging the data sources 

Banca d’Italia
Input for BSI statistics

SHSS

Not actively traded 
on exchanges

Non-ISIN securities
Unlisted ISIN 
securities

Merged dataset 

Own securities

Debt securities arising 
from securitisation of own 
loans

This section closes by comparing the reference dataset derived from the merging 
and filtering outlined above, with the SHSS and the BSI official series on securities 
held by Italian deposit-taking corporations31. The differences are explained in detail, 
with specific reference to the conceptual and methodological aspects. 

Chart 1 shows the comparison for the outstanding amounts of short-term debt 
securities. The merged dataset is quite close to the official BSI series, the difference 
mostly arising from the filtering on prices (up to €8 billion over the observed period). 
This component is very volatile over time, reflecting the different underlying pools of 
securities that remain after the filtering. Holdings of securities that are not 
denominated in euro, or do not have an ISIN, or are unlisted are not significant, while 
holdings of own securities and debt securities arising from the securitisation of own 
loans that are not derecognised from the balance sheet do not affect this category. 

Differences from the SHSS official series are mostly driven by securities holdings not 
denominated in euro and methodological differences related to the classification and 
valuation of securities, especially in the most recent quarters. 

30    The outstanding amounts of the listed shares and long-term debt securities excluded from the 

comparison by this filter amount to as much as €3.8 billion and €2.2 billion respectively over the time 
period. These data quality issues are currently being investigated. 

31    As explained above, the BSI series on securities holdings are derived directly from the Banca d’Italia 

dataset used in this exercise. 

ECB Statistics Paper Series No 44 

21 

 
 
 
 
 
Chart 1 
The merged dataset for outstanding amounts of short-term debt securities 

a) BSI 

(EUR billions) 

b) SHSS 

(EUR billions) 

BSI official
Merged dataset
Filtering on prices
Non-euro, or non-ISIN or unlisted securities

SHSS official
Merged dataset
Filtering on prices
Non-euro securities and methodological differences

30

25

20

15

10

5

0

30

25

20

15

10

5

0

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2017

2018

2019

2017

2018

2019

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Holdings of own securities and debt securities arising from the securitisation of own loans that are not derecognised from the 
balance sheet do not affect short-term debt securities. The category “Filtering on prices” refers to the filtering described in the earlier 
point 3 and also reflects small differences in valuation between the Banca d’Italia dataset (which relies on data submitted by reporting 
agents) and the merged dataset (which uses market prices from the SHSS dataset). This results in very minor negative amounts for 
this category in Q4 2019 (below €0.2 billion). The negative amounts are not reflected in the chart for ease of reference. 

Chart 2 
The merged dataset for outstanding amounts of long-term debt securities 

a) BSI 

(EUR billions) 

b) SHSS 

(EUR billions) 

BSI official
Merged dataset
Filtering on prices
Own securitisation
Non-euro, or non-ISIN or unlisted securities

SHS official
Merged dataset
Filtering on prices
Non-euro securities and methodological differences
Own securitisation
Own holdings

900

800

700

600

500

400

300

200

100

0

900

800

700

600

500

400

300

200

100

0

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2017

2018

2019

2017

2018

2019

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: “Own holdings” refers to holdings of own securities, while “Own securitisation” refers to holdings of debt securities arising from 
the securitisation of own loans that are not derecognised from the balance sheet. The category “Filtering on prices” refers to the 
filtering described in the earlier point 3, and also reflects the exclusion of ISINs for which data quality issues have been identified, as 
explained in the earlier point 4. 

With regard to long-term debt securities, Chart 2 shows the significance of holdings 
of own securities and debt securities arising from the securitisation of own loans that 

ECB Statistics Paper Series No 44 

22 

 
 
 
 
 
 
are not derecognised from the balance sheet which, in terms of outstanding 
amounts, account for up to €150 billion and €120 billion over the period respectively. 
The impact of the price filtering on long-term debt securities is also significant, 
accounting for amounts ranging between €49 billion and €62 billion over the 
observed period. Holdings of securities that are not denominated in euro, or do not 
have an ISIN, or are unlisted explain the residual small differences from the official 
BSI series, while the residual differences from the official SHSS series (mostly 
related to methodological differences in terms of classification and valuation of 
instruments) are also small. 

Panel a of Chart 3 reveals that unlisted equity represents most of the equity 
portfolios of Italian deposit-taking corporations, accounting for between €130 billion 
and €140 billion of outstanding amounts over the observed period (the amount of 
non-euro denominated equity instruments included in the BSI series is in fact quite 
small and accounts for no more than €16 billion over the observed period). In turn, 
the price filtering has a relatively low impact on equity instruments and accounts for 
no more than €2 billion over the period, while holdings of own securities as well as 
residual differences from the official SHSS series (mostly related to methodological 
differences in terms of the classification and valuation of instruments) amount to 
between €1.5 billion and €7 billion over the period. 

Chart 3 
The merged dataset for outstanding amounts of equity 

a) BSI 

(EUR billions) 

b) SHSS 

(EUR billions) 

BSI official
Merged dataset
Filtering on prices
Non-euro, or non-ISIN or unlisted securities

SHS official
Merged dataset
Filtering on prices
Non-euro securities and methodological differences

180

160

140

120

100

80

60

40

20

0

180

160

140

120

100

80

60

40

20

0

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2017

2018

2019

2017

2018

2019

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Holdings of own securities do not affect equity. The category “Filtering on prices” refers the filtering described in point 3 and 
also reflects small differences in valuation between the Banca d’Italia dataset (which relies on data submitted by reporting agents) and 
the merged dataset (which uses market prices from the SHSS dataset). This results in minor negative amounts for this category 
(below €1.1 billion). The negative amounts are not reflected in the chart for ease of reference. 

ECB Statistics Paper Series No 44 

23 

 
 
 
 
4 

Main results 

Based on the dataset described in Section 3, this section provides a detailed 
analysis of the results of Methods 3, 4 and 5 for compiling transactions compared 
with the ESA 2010-compliant data collected from reporting agents, which are used 
as a benchmark. The analysis starts with a comparison for specific securities, the 
aim being to investigate the sources of possible differences. Aggregated results are 
then presented to account for the variation of differences over time. 

4.1 

Comparing the methods for specific securities 

Four debt securities and four equity instruments were selected from the dataset, to 
cover cases with low and high price volatility and low and high turnover respectively. 
For the sake of simplicity, the comparison was limited to a single quarter (Q2 2018). 
The tables below summarise all the variables used to estimate transactions, based 
on Methods 3, 4 and 5. The resulting methods are then compared with the 
benchmark (i.e. real transaction data as available in the dataset from the Banca 
d’Italia). 

Table 8 presents data for debt securities. Looking at debt security 1, estimated data 
for transactions under Method 3 are lower than real data for transactions. This is due 
to the fact that the number of securities on the balance sheet remained relatively 
stable during the period, while there was a high number of intra-period transactions 
at a time of (relatively) high price volatility. Method 3 yields a lower value than 
Methods 4 and 5 as the number of securities held increases while the price at the 
end of the period (used to estimate transactions under Method 3) is lower than the 
average prices used under Methods 4 and 5 (see also Annex 1). The average of the 
daily prices is higher than the average of the prices at the beginning and at the end 
of the period, resulting in higher estimates under Method 5 than under Method 4. It 
should also be noted that the transactions estimated under Methods 4 and 5 are also 
relatively close to the real transactions. Transaction-by-transaction data would be 
required to further investigate the reasons for this. However, it seems clear that the 
closeness of the estimates is not due to any intrinsic methodological advantage of 
the methods but rather to the specific price dynamics of the security during the 
period (e.g. intra-period transactions taking place at transaction values that, on 
average, may be close to the averages used in Methods 4 and 5). 

ECB Statistics Paper Series No 44 

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Table 8 
Comparisons of transactions for selected debt securities 

Price volatility 

Turnover 

Number of 
securities held 

T0 

T1 

T0 

T1 

Prices (EUR) 

Average of prices at T0 and 
T1 

Debt security 1 
High volatility 
High turnover 

Debt security 2 
High volatility 
Low turnover 

Debt security 3 
Low volatility 
High turnover 

Debt security 4 
Low volatility 
Low turnover 

0.0218 

0.0115 

0.0039 

0.0010 

15.35 

14,000 

14,491 

94.5 

88.6 

91.5 

0.17 

1,689.60 

41,208 

48,457 

101.1 

98.6 

99.8 

1 

1 

99.9 

100.8 

100.3 

0.17 

23,118 

23,089 

100.3 

100.2 

100.3 

Average of daily prices 

92.9 

100.2 

100.0 

100.4 

Outstanding 
amounts (EUR) 

T0 

T1 

1,322,403 

4,166,523 

1,284,062 

4,775,472 

Transactions (EUR) 

45,148 

608,677 

55 

55 

-10 

2,319,768 

2,312,892 

-40 

Method 1 

Purchases (EUR) 

10,168,934 

703,275 

46,459 

200,818 

Sales (EUR) 

10,123,786 

94,598 

46,469 

200,858 

Method 3 - Transactions (EUR) 

43,557 

714,441 

Method 4 - Transactions (EUR) 

44,994 

723,720 

Method 5 - Transactions (EUR) 

45,676 

726,518 

-0 

-0 

-0 

-2,951 

-2,948 

-2,952 

Source: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Data refer to Q2 2018. T0 and T1 refer to the beginning and the end of the quarter respectively. Outstanding amounts and 
transactions are expressed in euro. In particular, outstanding amounts are sourced from the Banca d’Italia, while the number of 
securities is estimated as outstanding amounts divided by prices. Prices are sourced from the CSDB. Real data on transactions are 
sourced from the Banca d’Italia. Price volatility is derived as the coefficient of variation of the daily prices within the quarter, while 
turnover represents the sum of purchases and sales during the quarter divided by the outstanding amounts (which are defined in the 
calculations as the lower of the outstanding amount at the beginning and at the end of the quarter). Debt securities 1 and 2 are 
allocated to a “high volatility” group as they belong to the 20% upper tail of the distribution of the debt securities in respect of this 
indicator. 

For debt security 2, even if the values for sales and purchases point to a relatively 
low level of intra-period transactions, price volatility seems to play a fairly important 
role. In particular, the value for purchases is lower than the estimates under Methods 
3, 4 and 5, hinting at the possibility that these purchases took place at a price which 
was lower than both the price at the end of the period and the average price. In 
addition, as for debt security 1, the estimate of transactions under Method 3 is lower 
than it is under Methods 4 and 5 as the number of securities held increases while the 
price at the end of the period is lower than the average price. For debt security 3, the 
same number of securities are held on the balance sheet at the beginning and at the 
end of the period, so transactions estimated under Methods 3, 4 and 5 equal zero. 
However, as the numbers of securities bought and sold during the period were 
similar and the price volatility was low, there were only small differences from the 
real transaction data, even in the presence of high turnover. For debt security 4, the 
number of securities held on the balance sheet at the beginning and at the end of the 
period remains virtually unchanged, with the result that estimates for transactions are 
similar under Methods 3, 4 and 5. In this case the security also has a low price 
volatility, although the intra-period transactions in a situation in which prices are 

ECB Statistics Paper Series No 44 

25 

 
 
   
decreasing lead, in relative terms, to some small differences from the data for real 
transactions. 

Turning to equity (see Table 9), instrument 1 is characterised by a high number of 
intra-period transactions, which explain the differences between Method 1 and 
Methods 3, 4 and 5. Method 3 yields a lower value than Methods 4 and 5 as the 
number of securities held decreases while the price at the beginning of the period 
(used to estimate transactions under Method 3) is higher than the average prices 
used under Methods 4 and 5. The average of the daily prices is higher than the 
average of the prices at the beginning and at the end of the period, resulting in lower 
estimates under Method 5 than under Method 4. 

For equity instrument 2, Methods 3 and 5 yield very similar values as the price at the 
beginning of the period is very close to the average price. Real transactions are 
lower than those estimated under Methods 3 and 5 as some of the sales are likely to 
take place at prices higher than those for purchases given the price dynamics (i.e. 
sales may have been concentrated at the end of the period). Method 4 therefore 
seems to yield values that are closer to those for real transactions because in this 
specific instance the average price is closer to the end price. For equity instrument 3 
there is an increase in the number of securities on the balance sheet and a final 
price that is much higher than the average price. This is why Method 3 yields a 
higher value than Methods 4 and 5. The high number of intra-period transactions, 
even with low price volatility, explains the much higher values for real transactions 
compared with values estimated by Methods 3, 4 and 5. With low price volatility and 
low turnover, however, Methods 3, 4 and 5 yield similar results to real transactions, 
as shown in the case of equity instrument 4. 

ECB Statistics Paper Series No 44 

26 

 
 
Table 9 
Comparisons of transactions for selected equity instruments 

Price volatility 

Turnover 

Number of 
securities held 

Prices (EUR) 

T0 

T1 

T0 

T1 

Average of prices at T0 and 
T1 

Average of daily prices 

Equity 1 
High volatility 
High turnover 

Equity 2 
High volatility 
Low turnover 

Equity 3 
Low volatility 
High turnover 

Equity 4 
Low volatility 
Low turnover 

0.1124 

0.1026 

0.0184 

0.0121 

8.88 

0.28 

7.40 

612,377 

1,450,985 

245,855 

478,422 

1,383,915 

379,143 

17.4 

13.3 

15.4 

15.7 

0.8 

1.0 

0.9 

0.8 

65.3 

68.7 

67.0 

66.3 

0.02 

20,077 

20,043 

22.7 

22.9 

22.8 

23.3 

Outstanding 
amounts (EUR) 

T0 

T1 

10,682,913 

1,175,298 

16,061,707 

455,748 

6,341,488 

1,328,558 

26,028,167 

458,985 

Transactions (EUR) 

-2,539,003 

-61,845 

10,288,415 

-872 

Method 1 

Purchases (EUR) 

46,185,002 

152,204 

101,463,465 

Sales (EUR) 

48,724,005 

214,049 

91,175,050 

Method 3 - Transactions (EUR) 

-2,336,835 

-54,327 

9,150,221 

Method 4 - Transactions (EUR) 

-2,056,201 

-59,357 

8,928,963 

Method 5 - Transactions (EUR) 

-2,105,999 

-54,868 

8,839,799 

4,218 

5,090 

-772 

-775 

-791 

Source: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Data refer to Q2 2018. T0 and T1 refer to the beginning and the end of the quarter respectively. Outstanding amounts and 
transactions are expressed in euro. In particular, the number of securities is sourced from the Banca d’Italia, while outstanding 
amounts are calculated by multiplying the number of securities by prices. Prices are sourced from the CSDB. Real data on 
transactions are sourced from the Banca d’Italia. Price volatility is derived as the coefficient of variation of the daily prices within the 
quarter, while the turnover represents the sum of purchases and sales during the quarter divided by the outstanding amounts (which 
are defined in the calculations as the lower of the outstanding amount at the beginning and at the end of the quarter). 

Overall, the results discussed so far confirm the fact that intra-period transactions 
are driving the differences between real transactions and Methods 3, 4 and 5: the 
more intra-period transactions there are, the more likely it is that real transactions 
are not captured correctly by any estimation method. In addition, the higher the price 
volatility, the higher the differences between Methods 3, 4 and 5. Methods 4 and 5 
tend to smooth the estimates compared with Method 3 as they use average prices. 
This does not, however, imply better results, as the differences are driven by price 
dynamics: the closer the transaction prices are to the average prices, the better the 
estimates are under Methods 4 and 5. In practice, though, transactions can be 
concentrated at the beginning or at the end of the period, resulting in better 
estimates under Method 3 in such cases. 

It should also be noted that if the exercise had been conducted at a monthly 
frequency the comparisons may have produced much closer results across the 
methods. In fact, fewer transactions would have been reversed in the same month 
and, similarly, price volatility over a month would be lower than during a full quarter. 

ECB Statistics Paper Series No 44 

27 

 
 
   
4.2 

Aggregated comparisons by instrument type 

This section extends the comparison to the overall security portfolios covered in the 
exercise. It compares the transaction volumes resulting from Methods 3, 4 and 5 and 
the corresponding annual growth rates (which represent the standard measure used 
to analyse developments in macroeconomic series) with the benchmark. The charts 
below present the results for debt securities (short and long-term) and equity 
instruments, and are complemented by Table 10, which also presents the quarterly 
and annual growth rates. 

As shown in Chart 4, the four methods are essentially equivalent for short-term debt 
securities. In particular, the largest difference in transaction volumes is €0.02 billion, 
while the largest difference in annual growth rates amounts to 147 basis points, 
albeit at a point in time when the growth rate is above 500%. This is due to the fact 
that short-term debt securities have, on average, very low price volatility, resulting in 
non-significant differences across the methods, even when intra-period transactions 
are significant. 

Chart 4 
Short-term debt securities 

a) Transactions volumes 

b) Annual growth rates 

(EUR billions) 

(annual growth rates, percentages) 

Method 1
Method 3

Method 4
Method 5

Method 1
Method 3

Method 4
Method 5

14

12

10

8

6

4

2

0

-2

-4

600%

500%

400%

300%

200%

100%

0%

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2018

2019

Q4

2018

Q1

Q2

Q3

Q4

2019

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Growth rates are computed in accordance with the BSI methodology (see also the manual on MFI balance sheet statistics). In 
particular, to take into account the different composition of the pool of securities over the quarters, growth rates are calculated by 
comparing the transactions of a specific period with the outstanding amounts for the corresponding pool of securities at the beginning 
of the period. 

ECB Statistics Paper Series No 44 

28 

 
 
 
 
Chart 5 
Long-term debt securities 

a) Transactions volumes 

b) Annual growth rates 

(EUR billions) 

(annual growth rates, percentages) 

Method 1
Method 3

Method 4
Method 5

Method 1
Method 3

Method 4
Method 5

70

60

50

40

30

20

10

0

25%

20%

15%

10%

5%

0%

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2018

2019

Q4

2018

Q1

Q2

Q3

Q4

2019

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Growth rates are computed in accordance with the BSI methodology (see also the manual on MFI balance sheet statistics). In 
particular, to take into account the different composition of the pool of securities over the quarters, growth rates are calculated by 
comparing the transactions of a specific period with the outstanding amounts for the corresponding pool of securities at the beginning 
of the period. 

Also for long-term debt securities, the four methods yield very similar results. As 
shown in Chart 5, Method 3 is slightly closer to the actual transactions, with 
differences that never exceed €1.51 billion (or 24 basis points in terms of annual 
growth rates). The differences between Methods 4 and 5 and the actual transactions 
are somewhat larger, peaking at €3.11 billion in Q3 2019 (or 65 basis points in Q4 
2019 in terms of annual growth rates) and at €2.91 billion in Q3 2019 (or 67 basis 
points in Q4 2019 in terms of annual growth rates) respectively. 

Chart 6 shows that the four approaches also result in similar estimates for equity 
instruments, although there are some small differences. The results seem to be in 
line with the findings of Section 4.1, as equity instruments are normally more 
frequently traded than debt securities (i.e. there are more intra-period transactions) 
and equity prices are typically much more volatile than debt security prices. For 
instance, price volatility seems to play a role in the differences for Q4 2018, which is 
characterised by a higher price volatility than the other quarters based on the 
indicator used in our analyses (see Table 10). At the same time, Method 3 seems to 
perform similarly to Methods 4 and 5. In Method 3, differences from the actual 
transactions in absolute terms are always lower than €0.05 billion (or 18 basis points 
in terms of annual growth rates). Method 4 shows differences peaking at €0.08 billion 
in absolute terms (or 39 basis points in terms of annual growth rates), while Method 
5 delivers a difference of up to €0.08 billion (or 49 basis points in terms of annual 
growth rates). These findings also seem to relate to the fact that the estimates 
obtained under Methods 4 and 5 are more sensitive to price volatility. 

ECB Statistics Paper Series No 44 

29 

 
 
 
 
Chart 6 
Equity 

a) Transactions volumes 

b) Annual growth rates 

(EUR billions) 

(annual growth rates, percentages) 

Method 1
Method 3

Method 4
Method 5

Method 1
Method 3

Method 4
Method 5

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

6%

4%

2%

0%

-2%

-4%

-6%

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2018

2019

Q4

2018

Q1

Q2

Q3

Q4

2019

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Growth rates are computed in accordance with the BSI methodology (see also the manual on MFI balance sheet statistics). In 
particular, to take into account the different composition of the pool of securities over the quarters, growth rates are calculated by 
comparing the transactions of a specific period with the outstanding amounts for the corresponding pool of securities at the beginning 
of the period. 

Table 10 provides a complete overview of the transaction volumes for the different 
methods, with the corresponding quarterly and annual growth rates. 

Overall, it may be concluded that the differences for specific securities tend to 
balance out in the aggregate, even in periods of relatively high price volatility such as 
Q4 2018. As shown in Annex 2, the deviations shown by all indirect methods from 
the ESA 2010 approach have a very low correlation with price volatility and would 
appear to be acceptable in the aggregate, given the cost of directly reporting data on 
transactions and the fact that from an aggregate perspective the “story told by the 
data” does not change.32 

32    The derivation of concrete measures to ascertain what level of deviation is acceptable is considered to 
be outside the scope of this paper. However, it should be clarified that such a quantification could only 
be performed in the context of specific econometric modelling frameworks and may therefore depend 
on the other variables considered in the analyses and their interaction, which might also vary from 
period to period. Measures of acceptable deviation may therefore lack generality. 

ECB Statistics Paper Series No 44 

30 

 
 
 
 
 
 
 
Table 10 
Comparing transactions according to the different compilation methods 

Short-term debt securities 

Transactions 
(EUR billions) 

Quarterly growth rate 
(percentages) 

Annual growth rate 
(percentages) 

Reference 
period 

Outstanding 
amounts 
(EUR 
billions) 

Price 
volatility 
(median) 

T0 

T1 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Method 
1 

8.0  11.3  0.0003 

3.27 

3.27 

3.27 

3.27  40.86%  40.83%  40.84%  40.83% 

- 

8.0  17.9  0.0005 

9.85 

9.86 

9.86 

9.86  123.09% 123.28% 123.29% 123.29% 

- 

15.0  22.6  0.0006 

7.64 

7.64 

7.65 

7.65  51.02%  51.05%  51.06%  51.07% 

- 

Method 
3 

Method 
4 

Method 
5 

- 

- 

- 

- 

- 

- 

- 

- 

- 

15.2  13.0  0.0006 

-2.20 

-2.19 

-2.20 

-2.20  -14.45% -14.40% -14.43% -14.45% 306.01% 306.60%  306.50%  306.39% 

9.9  21.6  0.0006  11.61  11.63  11.62  11.61  117.03% 117.18% 117.10% 117.06% 525.56% 527.03%  526.59%  526.35% 

15.6  18.4  0.0003 

2.81 

2.82 

2.81 

2.81  18.08%  18.10%  18.09%  18.09%  231.11% 231.66%  231.38%  231.26% 

13.3  18.5  0.0003 

5.16 

5.17 

5.17 

5.17  38.86%  38.93%  38.95%  38.92%  204.44% 205.05%  204.82%  204.64% 

12.7  14.5  0.0003 

1.77 

1.77 

1.77 

1.77  13.93%  13.90%  13.91%  13.91%  305.41% 305.88%  305.78%  305.62% 

Long-term debt securities 

Transactions 
(EUR billions) 

Quarterly growth rate 
(percentages) 

Annual growth rate 
(percentages) 

Reference 
period 

Outstanding 
amounts 
(EUR 
billions) 

Price 
volatility 
(median) 

T0 

T1 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Method 
1 

Method 
3 

Method 4  Method 

410.2  438.5  0.0032 

25.67  25.99  25.52  25.38  6.26%  6.34%  6.22%  6.19% 

425.0  470.1  0.0042 

61.06  60.77  62.58  62.45  14.37%  14.30%  14.72%  14.69% 

463.3  464.9  0.0033 

8.50 

7.08 

7.29 

7.32  1.84%  1.53%  1.57%  1.58% 

- 

- 

- 

- 

- 

- 

- 

- 

- 

5 

- 

- 

- 

454.7  461.7  0.0038 

0.36 

0.78 

0.34 

0.25  0.08%  0.17%  0.07%  0.06%  23.85%  23.61%  23.87%  23.78% 

457.1  480.8  0.0049 

19.41  20.92  20.42  20.26  4.25%  4.58%  4.47%  4.43%  21.51%  21.56%  21.83%  21.74% 

471.3  486.3  0.0037 

8.67 

8.69 

7.87 

7.85  1.84%  1.84%  1.67%  1.66%  8.20%  8.32% 

7.96% 

7.91% 

473.7  514.9  0.0041 

26.01  24.65  22.90  23.10  5.49%  5.20%  4.84%  4.88%  12.08%  12.24%  11.43%  11.41% 

497.9  503.8  0.0034 

14.37  13.98  14.83  14.76  2.89%  2.81%  2.98%  2.96%  15.23%  15.19%  14.66%  14.65% 

Q1 
2018 

Q2 
2018 

Q3 
2018 

Q4 
2018 

Q1 
2019 

Q2 
2019 

Q3 
2019 

Q4 
2019 

Q1 
2018 

Q2 
2018 

Q3 
2018 

Q4 
2018 

Q1 
2019 

Q2 
2019 

Q3 
2019 

Q4 
2019 

ECB Statistics Paper Series No 44 

31 

 
 
 
 
Reference 
period 

Outstanding 
amounts 
(EUR 
billions) 

Price 
volatility 
(median) 

T0 

T1 

Equity 

Transactions 
(EUR billions) 

Quarterly growth rate 
(percentages) 

Annual growth rate 
(percentages) 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Method 
1 

Method 
3 

Method 
4 

Method 
5 

Q1 
2018 

Q2 
2018 

Q3 
2018 

Q4 
2018 

Q1 
2019 

Q2 
2019 

Q3 
2019 

Q4 
2019 

26.5  26.9  0.0454 

-0.93    -0.95    -0.92    -0.93    -3.50%  -3.57%  -3.47%  -3.52% 

26.8  27.7  0.0443 

0.14    0.10    0.18    0.21    0.53%  0.36%  0.65%  0.77% 

27.0  27.6  0.0368 

-0.18    -0.16    -0.18    -0.18    -0.66%  -0.61%  -0.66%  -0.65% 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

29.2  25.1  0.0558 

-0.35    -0.35    -0.27    -0.26    -1.19%  -1.19%  -0.93%  -0.90%  -4.77%  -4.95%  -4.38%  -4.28% 

23.6  28.5  0.0460 

0.62    0.66    0.58    0.57    2.63%  2.81%  2.46%  2.42%  1.28%  1.34%  1.49%  1.62% 

26.8  26.4  0.0458 

0.71    0.68    0.68    0.70    2.64%  2.53%  2.54%  2.62%  3.40%  3.53%  3.40%  3.49% 

27.6  27.7  0.0438 

-0.35    -0.35    -0.37    -0.35    -1.26%  -1.27%  -1.33%  -1.28%  2.78%  2.84%  2.70%  2.83% 

26.6  26.8  0.0420 

0.07    0.10    0.10    0.07    0.25%  0.39%  0.37%  0.26%  4.27%  4.49%  4.05%  4.03% 

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 
Notes: Outstanding amounts and transactions are expressed in billions of euro. Price volatility is derived as the coefficient of variation 
of the daily prices within the quarter; the median within the quarter across securities is shown in the table. Growth rates are computed 
in accordance with the BSI methodology (see also the manual on MFI balance sheet statistics). In particular, to take into account the 
different composition of the pool of securities over the quarters, growth rates are calculated by comparing the transactions of a specific 
period with the outstanding amounts for the corresponding pool of securities at the beginning of the period. 

ECB Statistics Paper Series No 44 

32 

 
 
 
5 

Lessons learnt from the comparison 
exercise 

Section 4 shows that for listed ISIN securities that are actively traded on exchanges 
(for which there is good coverage of pricing information in the CSDB) both the 
simplified BSI approach (i.e. Method 3) and the SHSS approach deliver accurate 
estimates of transaction data. As differences are very small it would be difficult to 
justify the collection of granular transaction data on methodological grounds. 

Apart from this empirical evidence, the design and implementation of the comparison 
exercise has raised various points which are crucial to bear in mind when developing 
a successful strategy for compiling aggregated statistics on securities holdings, 
based on granular data. These considerations are therefore key for ESCB compilers 
to consider when defining an approach for collecting data from banks in the context 
of the IReF. 

A general point is that compiling balance sheet statistics in a manner which is 
consistent with the principles of ESA 2010 means that securities holdings must be 
valued at market (or fair) value.33  However, this information may not be readily 
available in banks’ internal systems for all types of securities. For instance, securities 
that are not classified in trading portfolios for accounting purposes are not typically 
marked to market. Collecting information from reporting agents on the nominal 
amounts of debt securities held or the number of equity securities held (as is 
currently the case in many euro area countries in an SHSS context) would therefore 
represent a good basis for compilers to estimate market values based on CSDB 
pricing information. 

At the same time, the CSDB only focuses on listed ISIN securities and, although it 
has very high coverage (close to 100% for securities held by euro area residents), 
thus guaranteeing the availability of reference data for compilation purposes, the 
availability of pricing information can vary from country to country and according to 
the type of security. This is because it depends strongly on the commercial sources 
available and the liquidity of securities, especially for securities issued outside the 
EU. For instance, in this paper no comparisons can be drawn for investment fund 
shares/units, and several debt securities had to be filtered out of the exercise, as 
explained in Section 3.4. Given the ESCB’s wide range of needs when it comes to 
instruments to be covered for compilation purposes, it is becoming more difficult to 
ensure that all the price data required are available in the CSDB in a timely manner. 
This is especially apposite for specific markets or securities that are not very liquid or 

33    It should be underlined that such an approach reduces the linkages with banks’ internal accounting 
data, and with data that banks may publish or report to other authorities. In addition, the basic 
accounting principle stipulating that assets should balance liabilities is no longer applicable in this 
context. The balancing of the balance sheet is normally achieved by allocating a balancing item to 
remaining assets or liabilities, something statistical compilers may find challenging to monitor over time. 
For instance, when a bank writes-down the holdings of a security, in accounting terms this determines a 
decrease in the outstanding amounts of the security, which is balanced by a loss in terms of capital and 
reserves. Valuing the holdings at market value means that the loss will normally be matched by a 
revaluation of the balancing item. 

ECB Statistics Paper Series No 44 

33 

 
 
 
which target particular investors. It implies that getting prices from commercial 
sources is more challenging in such cases as data vendors normally focus their 
efforts on the instruments that are most relevant for their clients. Hence, for listed 
securities that are not actively traded (and even more so for securities that are not 
listed on organised markets) pricing data can only be collected from reporting agents 
on a best-effort basis (e.g. based on fair valuation or by simply collecting accounting 
values)34. An alternative would be to estimate the missing prices based on other 
available data (e.g. cash-flow information or prices of similar securities)35, although 
the accuracy of the resulting estimates would again depend on data availability. 
Default methodologies can also be used.36 

These points also have important implications for the derivation of transactions. For 
the simplified BSI and SHSS methods to deliver good estimates of transactions the 
data collected from reporting agents should make it possible to accurately estimate 
the number of securities held on the balance sheet. Hence, independently of the 
approach used to value the outstanding amounts on the statistical balance sheet, the 
reporting framework must guarantee the collection or the indirect estimation of these 
data (e.g. based on the nominal amounts of debt securities held when the securities 
are covered by the CSDB). In addition, good pricing information must be available in 
the CSDB for listed ISIN securities. In this respect, it should be noted that the 
simplified BSI and SHSS methods (Methods 3 and 4) seem to be more reliable than 
the modified SHSS method (Method 5). In fact, while the former only require good 
pricing information at each period end, the latter uses average prices over the 
reference period – these can be negatively influenced by outliers37. Methods 3 and 4 
are very similar, as they only differ according to the price used to estimate 
transactions – the price at the beginning or at the end of the period under Method 3, 
or the average of the two under Method 4. From a conceptual perspective neither of 
the two methods is superior, as their performance is directly dependent on the price 
at which transactions take place. The empirical exercise showed that under Method 
3 the differences from the real transactions were slightly smaller, and were mostly 
driven by the differences observed for long-term debt securities in Q3 2019. 

For securities without pricing information in the CSDB, the derivation of estimates of 
transactions is strictly related to the approach followed for the valuation of data on 
outstanding amounts. For instance, if the outstanding amounts of unlisted securities 
are marked at the accounting values on the statistical balance sheet, an indirect 
approach for transactions might be preferred (e.g. using the values that are implicitly 
reported for the valuation of the outstanding amounts). 

34    This approach would enable compilers to close the gap between the merged dataset in Charts 1-3 and 
the official series. Non-ISIN equity instruments, own securities and debt securities arising from the 
securitisation of own loans that are not derecognised from the balance sheet would either be covered 
by the CSDB, or the relevant data would be collected from reporting agents. 

35    This approach is followed, for example, for investments funds, for which benchmarks can be used to 

estimate prices for those funds for which prices are not available. This is done based on the investment 
strategy or geographical allocations of the funds’ investments. 

36    It should be noted that the CSDB already offers both options, ensuring that there is at least one price 

for each security, and providing an automatic alternative in the compilation process. 

37    In the CSDB, outliers often materialise given that the compounding algorithm sources pricing 

information from different sources or markets. 

ECB Statistics Paper Series No 44 

34 

 
 
 
Whichever approach is established, it will also be essential to fix precise and 
appropriate reporting and compilation rules for securities that may change type. 
Examples of this would be a listed share which has become unlisted, or a listed 
security for which no price information is available in the CSDB for a specific period 
(for which its identification would not be known prior to the collection, as this can 
change over time). Even if these changes would usually have a limited quantitative 
impact on outstanding amounts in aggregate terms, the impact on transactions could 
be quite sizeable. 

ECB Statistics Paper Series No 44 

35 

 
 
6 

Conclusions 

This paper presents empirical findings with regard to the differences between indirect 
statistical approaches used to derive data on financial transactions (which are 
related to deposit-taking corporations’ holdings of listed ISIN securities that are 
actively traded on exchanges) and the direct collection of data on transactions 
(which would be compliant with statistical methodology). Using s-b-s data collected 
by the Banca d’Italia directly from reporting agents on both outstanding amounts and 
transactions, it has been possible to compare transaction data compliant with the 
ESA 2010 approach with estimates based on balance sheet methods used in 
relation to BSI and SHSS statistics. 

The empirical results show that, for listed ISIN securities for which there is good 
coverage of pricing information in the CSDB, both the BSI and the SHSS 
approaches deliver accurate estimates of data on transactions in aggregate terms.38 
Although the paper focuses on data over a two-year period, the results seem robust 
given that that the differences also remain low in periods of relatively high price 
volatility. The differences are also very small when the results are analysed by 
instrument type (i.e. debt securities and equity securities). While there seems to be 
no theoretical reason why one indirect method might be superior to another, the BSI 
method seems to perform slightly better for this sample over the period considered. 
In addition, the modified SHS method, which uses the average of the daily prices 
during the period, relies on good underlying daily data being available in the CSDB. 
This would seem to make the method more prone to possible errors than the other 
methods, which only rely on pricing information at the end of each period. 

While the ongoing cost-benefit assessment of the IReF will make it possible to 
evaluate the reporting burden of the direct collection of data on transactions, the 
empirical exercise performed in this paper shows that this type of data collection 
does not appear to be necessary for the purposes of compiling accurate macro 
statistics. This will need to be taken into account when weighing up the benefits of 
this type of data collection against the burden it would imply for reporting agents. 

At the same time, it should be noted that unlisted securities and listed securities that 
are not actively traded on exchanges (for which the availability of pricing information 
from commercial sources may be more limited) represent a significant share of 
banks’ holdings, at least in the case of Italy. For these securities a dedicated strategy 
must be developed to derive data on financial transactions. The most reliable 
approach to use for the estimation should be decided strictly on the basis of the 
valuation criteria that are applied to outstanding amounts. 

38    In fact, experience has shown that the quality of the estimates based on indirect methods has been 

satisfactory, even for a range of ad hoc analyses at the granular level. When drilling down at the level of 
individual securities, however, substantial differences may materialise for securities that feature high 
trading activity or high price volatility. Therefore, from a micro statistics perspective the direct collection 
of transaction data would provide more precise information. At the same time, however, there would 
have to be concrete analytical needs for this kind of data collection. 

ECB Statistics Paper Series No 44 

36 

 
 
 
Finally, it should also be stressed that the results presented in this document refer 
only to Italian deposit taking corporations. While a certain degree of caution should 
be exercised when generalising these conclusions to other institutional sectors, the 
results would be expected to apply for the euro area as a whole given the size, 
characteristics and features of the Italian banking market. 

ECB Statistics Paper Series No 44 

37 

 
 
 
Annex 1 – Formalisation of methods to 
derive transactions on holdings of 
securities 

This annex formalises the different scenarios presented in Section 2 for the 
collection and derivation of transactions related to securities.39  In particular, it is 
assumed that the securities are denominated in euro, meaning that no revaluations 
are required in relation to changes in exchange rates. The impact of interest accruals 
on debt securities is not considered either (such accruals are treated as being 
included in price revaluation effects). In addition, the formulas do not take other 
changes in volume into account, although this does not imply a loss of generality as 
the formulas can easily be amended to include this additional factor.40  In line with 
the ESA 2010 approach to the valuation of securities holdings, only the case in 
which securities holdings are marked at market (or fair) value is considered. 

𝑆𝑆

𝐵𝐵

, 

  and 

. Also, let 

(𝑡𝑡, 𝑡𝑡 + 1]
𝑡𝑡

  indicate the reference period and 

  denote the number of securities 
  indicate the number of transactions taking place 

Let 
at time 
during the period relating, respectively, to sales of securities that are on the balance 
sheet at time 
, 
and intra-period transactions (i.e. relating to securities which are purchased after 
time 
). The corresponding quantities are denoted as 
follows: 

, purchases of securities that are on the balance sheet at time 

  and sold before time 

𝑡𝑡 + 1

𝑁𝑁𝑡𝑡

𝑛𝑛

𝑛𝑛

𝑛𝑛

𝑡𝑡

𝐼𝐼

𝑡𝑡

𝑡𝑡 + 1

the number of securities sold at time 

  that were held at the beginning of the 

𝑆𝑆
period (i.e. in 
𝑁𝑁𝑠𝑠𝑖𝑖

= 

), with 

  for 

𝑠𝑠𝑖𝑖

; 

𝑆𝑆

the number of securities bought at time 

𝑠𝑠𝑖𝑖  ∈ (𝑡𝑡, 𝑡𝑡 + 1]

𝑁𝑁𝑡𝑡

𝑖𝑖 = 1, … , 𝑛𝑛

  that are held until the end of the 

𝐵𝐵
period (i.e. in 
𝑁𝑁𝑠𝑠𝑗𝑗

= 

), with 

  for 

𝑠𝑠𝑗𝑗
𝑗𝑗 = 1, … , 𝑛𝑛

; 

𝐵𝐵

the number of securities bought at time 

  and sold at time 

, with 

𝑁𝑁𝑡𝑡+1

𝑠𝑠𝑗𝑗 ∈ (𝑡𝑡, 𝑡𝑡 + 1]

= 

  for 

𝐼𝐼
𝑁𝑁𝑠𝑠𝑙𝑙
2
𝑠𝑠𝑙𝑙
The total number of securities purchased, sold and traded within the period can thus 
be determined, respectively, as 

𝑙𝑙 = 1, … , 𝑛𝑛

1
𝑡𝑡 < 𝑠𝑠𝑙𝑙

< 𝑡𝑡 + 1

  and 

2
𝑠𝑠𝑙𝑙

1
𝑠𝑠𝑙𝑙

≤

, 

. 

. 

𝐼𝐼

𝑛𝑛
𝐵𝐵
  to represent the number of securities that are held throughout the period 
= ∑ 𝑁𝑁𝑠𝑠𝑗𝑗
𝑗𝑗=1
  that are not sold within the period), it may be seen that 

𝑛𝑛
𝑆𝑆
= ∑ 𝑁𝑁𝑠𝑠𝑖𝑖
𝑖𝑖=1

𝑛𝑛
𝐼𝐼
= ∑ 𝑁𝑁𝑠𝑠𝑙𝑙
𝑙𝑙=1

𝑁𝑁

𝑁𝑁

𝑁𝑁

𝐵𝐵

𝑆𝑆

𝐼𝐼

𝑆𝑆

𝐵𝐵

𝐼𝐼

𝐾𝐾

Using 
(i.e. securities held at time 
the following identities hold: 
𝑡𝑡

𝑁𝑁

𝑁𝑁𝑡𝑡 = 𝑁𝑁

𝐾𝐾

𝑆𝑆

+ 𝑁𝑁

(1) 

39    The annex generalises examples J and K of Section 5 of the Manual on MFI balance sheet statistics to 

the case in which more sales and purchases take place during the reference period. 

40    It should be remembered that in this paper other changes in volume do not play a role in the 

comparisons as the analyses are performed in each quarter, based on the same pool of securities. 

ECB Statistics Paper Series No 44 

38 

 
 
 
 
and 

𝑁𝑁𝑡𝑡+1 = 𝑁𝑁
Let 

𝐾𝐾

𝐵𝐵

+ 𝑁𝑁

(2) 

indicate the market (or fair) value41  of the security at time 

. The outstanding amounts of the securities held at time 

𝑝𝑝𝑠𝑠 

as: 
1]

, where 
  can thus be identified 

𝑠𝑠

𝑠𝑠 ∈ (𝑡𝑡, 𝑡𝑡 +

𝑡𝑡

(3) 

𝑆𝑆𝑡𝑡 = 𝑝𝑝𝑡𝑡 ∙ 𝑁𝑁𝑡𝑡
The five methods considered in Section 2 may be formalised as follows. 

Method 1. Value all sales and purchases (i.e. including intra-period transactions) 
during the reference period at their transaction value (the ESA 2010 approach). 

  denote transactions during the reference period, in accordance with Method 

Let 
1. These transactions are calculated by measuring all sales and purchases at the 
price at which they take place (i.e. the market (or fair) price). Hence: 

1
𝑇𝑇𝑡𝑡+1

𝐵𝐵

𝑛𝑛

𝑆𝑆

𝑛𝑛

𝐼𝐼

𝑛𝑛

1
𝑇𝑇𝑡𝑡+1

𝐵𝐵
= � 𝑁𝑁𝑠𝑠𝑗𝑗
𝑗𝑗=1

∙ 𝑝𝑝𝑠𝑠𝑗𝑗

𝑆𝑆
− � 𝑁𝑁𝑠𝑠𝑖𝑖
𝑖𝑖=1

∙ 𝑝𝑝𝑠𝑠𝑖𝑖

𝐼𝐼
− � 𝑁𝑁𝑠𝑠𝑙𝑙
𝑙𝑙=1

2

∙ �𝑝𝑝𝑠𝑠𝑙𝑙

1

− 𝑝𝑝𝑠𝑠𝑙𝑙

�

(4) 

  denote the revaluations due to changes in prices during the reference 

Now, let 
period, in accordance with Method 1. Under the assumptions stated above, the 
following holds: 

1
𝑅𝑅𝑡𝑡+1

= 𝑆𝑆𝑡𝑡+1 − 𝑆𝑆𝑡𝑡 − 𝑇𝑇𝑡𝑡+1

1
𝑅𝑅𝑡𝑡+1
Substituting equations 4 and 3 and identities 1 and 2 into equation 5 yields: 

1

1
𝑅𝑅𝑡𝑡+1

=

𝐵𝐵

𝑛𝑛

𝑆𝑆

𝑛𝑛

𝐼𝐼

𝑛𝑛

(5) 

(6) 

𝐾𝐾

𝑁𝑁

𝑗𝑗=1

∙ �𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑠𝑠𝑗𝑗�

𝐵𝐵
∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑡𝑡) + � 𝑁𝑁𝑠𝑠𝑗𝑗

𝑆𝑆
+ � 𝑁𝑁𝑠𝑠𝑖𝑖
𝑖𝑖=1
Under Method 1, the revaluation thus equals the realised gains on the securities sold 
during the period (i.e. addends 3 and 4 in equation 6) plus the unrealised gains on 
the securities held and those bought during the period (i.e. addends 1 and 2 in 
equation 6). 

𝐼𝐼
+ � 𝑁𝑁𝑠𝑠𝑙𝑙
𝑙𝑙=1

∙ �𝑝𝑝𝑠𝑠𝑖𝑖 − 𝑝𝑝𝑡𝑡�

− 𝑝𝑝𝑠𝑠𝑙𝑙

∙ �𝑝𝑝𝑠𝑠𝑙𝑙

�

1

2

41    All prices referred in this annex are “dirty” prices, reflecting the fact that the impact of interest accruals 

is not considered separately in the analyses. 

ECB Statistics Paper Series No 44 

39 

 
 
 
 
 
 
 
 
 
Calculating transactions or revaluations due to changes in prices according to the 
ESA 2010 approach is extremely burdensome. As shown in equations 4 and 6, 
reporting agents would need to report detailed information on the quantities involved 
in any transactions taking place during the period. 

Method 2. Consider sales and purchases made during the reference period 
excluding intra-period transactions, and value them on an s-b-s basis at the market 
value on the opening and closing balance sheet dates respectively. 

  denote the transactions during the period, in accordance with Method 2. 

Let 
Under this simplified approach, transactions can be calculated as: 

2
𝑇𝑇𝑡𝑡+1

𝐵𝐵

𝑛𝑛

𝑆𝑆

𝑛𝑛

2
𝑇𝑇𝑡𝑡+1

𝐵𝐵
= 𝑝𝑝𝑡𝑡+1 ∙ � 𝑁𝑁𝑠𝑠𝑗𝑗

𝑗𝑗=1

− 𝑝𝑝𝑡𝑡

𝑆𝑆
∙ � 𝑁𝑁𝑠𝑠𝑖𝑖
𝑖𝑖=1

= 𝑝𝑝𝑡𝑡+1 ∙ 𝑁𝑁

𝐵𝐵

𝑆𝑆

− 𝑝𝑝𝑡𝑡 ∙ 𝑁𝑁

(7) 

Substituting equations 7 and 3 and identities 1 and 2 into equation 5 yields, for the 
calculation of revaluations 

under Method 2: 

2
𝑅𝑅𝑡𝑡+1 

(8) 

𝐾𝐾

= 𝑁𝑁

∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑡𝑡)

2
𝑅𝑅𝑡𝑡+1
Revaluations due to changes in prices can thus be estimated as the holding gains 
and losses on securities held throughout the period. As explained above, the number 
of such securities is equal to the number of securities held at the beginning of the 
reference period 
the reference period 
𝑁𝑁𝑡𝑡
the end of the reference period 
reference period and are held until the end of the reference period 
𝑁𝑁𝑡𝑡+1

  minus the those belonging to the initial pool that are sold during 
  (identity 1) or, alternatively, to the securities that are held at 

  minus those that are bought during the 

  (identity 2). 

𝑁𝑁

𝑆𝑆

𝐵𝐵

𝑁𝑁

This approach is equivalent to using equation 6 and ignoring the last three addends. 
Under this approach, there is no need to collect information from reporting agents on 
individual transactions. Information need only be obtained for the total amount of 
securities that are held throughout the period or, alternatively, the number of 
securities purchased that are kept on the balance sheet until the end of the period, or 
the number of securities in the initial pool that are sold during the reference period 
(bearing in mind that each of these items can be obtained from the others based on 
identities 1 and 2). 

Method 3. Estimate revaluations for price changes as the holding gains on securities 
that are kept during the period, approximating the latter on an s-b-s basis as the 
lower of the number of securities on the opening and the closing balance sheets. 

Under Method 3, the calculation of price revaluations is simplified further by 
estimating the number of securities that are held throughout the period as 

. The main advantage of this approach is that revaluations are 
estimated only based on information available from the balance sheet at the 
𝑚𝑚𝑖𝑖𝑛𝑛(𝑁𝑁𝑡𝑡, 𝑁𝑁𝑡𝑡+1)
beginning and at the end of the reference period. Let 
under Method 3. Starting from equation 8, these can be calculated as: 

denote the revaluations 

3
𝑅𝑅𝑡𝑡+1 

ECB Statistics Paper Series No 44 

40 

 
 
 
 
= 𝑚𝑚𝑖𝑖𝑛𝑛(𝑁𝑁𝑡𝑡, 𝑁𝑁𝑡𝑡+1) ∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑡𝑡)

3
𝑅𝑅𝑡𝑡+1 
Under this approach, transactions are estimated indirectly as: 

3
𝑇𝑇𝑡𝑡+1 

= 𝑆𝑆𝑡𝑡+1 − 𝑆𝑆𝑡𝑡 − 𝑅𝑅𝑡𝑡+1 

3

=

(9) 

(10) 

                if 
                    if 

𝑝𝑝𝑡𝑡+1 ∙ (𝑁𝑁𝑡𝑡+1 − 𝑁𝑁𝑡𝑡)
𝑝𝑝𝑡𝑡 ∙ (𝑁𝑁𝑡𝑡+1 − 𝑁𝑁𝑡𝑡)

𝑁𝑁𝑡𝑡 ≤ 𝑁𝑁𝑡𝑡+1
𝑁𝑁𝑡𝑡 ≥ 𝑁𝑁𝑡𝑡+1

= � 
Method 4. Value all sales and purchases made during the reference period at the 
average of the market price at the beginning and at the end of the reference period. 

Method 4 is the method currently favoured for Securities Holdings Statistics. Under 
this approach, transactions executed during the period 
measuring all sales and purchases at the average market price: 

  are calculated by 

. 

4
𝑇𝑇𝑡𝑡+1 

Replacing 

, 

  and 

  by 

  in equation 4 yields: 

𝑝𝑝𝑠𝑠𝑖𝑖

𝑝𝑝𝑠𝑠𝑗𝑗

𝑝𝑝𝑠𝑠𝑙𝑙

𝑝𝑝̅𝑡𝑡

𝐵𝐵

𝑛𝑛

𝑆𝑆

𝑛𝑛

𝐼𝐼

𝑛𝑛

4
𝑇𝑇𝑡𝑡+1 

𝐵𝐵
= � 𝑁𝑁𝑠𝑠𝑗𝑗
𝑗𝑗=1

∙ 𝑝𝑝̅𝑡𝑡 

𝑆𝑆
− � 𝑁𝑁𝑠𝑠𝑖𝑖
𝑖𝑖=1

∙ 𝑝𝑝̅𝑡𝑡 

𝐼𝐼
− � 𝑁𝑁𝑠𝑠𝑙𝑙
𝑙𝑙=1

∙ (0) = 𝑝𝑝̅𝑡𝑡

∙ (𝑁𝑁

𝐵𝐵

𝑆𝑆

− 𝑁𝑁

)

Using identities 1 and 2, equation 11 can be written as: 

𝑝𝑝𝑡𝑡+𝑝𝑝𝑡𝑡+1

𝑝𝑝̅𝑡𝑡 =

2

(11) 

(12) 

= 𝑝𝑝̅𝑡𝑡 ∙ (𝑁𝑁𝑡𝑡+1 − 𝑁𝑁𝑡𝑡)

4
𝑇𝑇𝑡𝑡+1 
Hence, substituting equations 12 and 3 into equation 5 yields a calculation under 
Method 4 for revaluations 

which is: 

4
𝑅𝑅𝑡𝑡+1 

4
𝑅𝑅𝑡𝑡+1 

= 𝑁𝑁𝑡𝑡+1 ∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝̅𝑡𝑡) − 𝑁𝑁𝑡𝑡 ∙ (𝑝𝑝𝑡𝑡 − 𝑝𝑝̅𝑡𝑡) =

(𝑁𝑁𝑡𝑡+1 + 𝑁𝑁𝑡𝑡)
2

∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑡𝑡)

(13) 

Revaluations under this approach are therefore similar to those under Method 3, the 
difference being that the average of the number of securities held at the beginning 
and at the end of the period is used to estimate the number of securities that are 
held throughout the period, rather than 
are estimated based on information available from the balance sheet at the 
beginning and at the end of the reference period only. No information is needed on 
individual transactions (as is the case under Method 1) or on the total amounts of 
securities held throughout the period (as is the case under Method 2). 

. Also in this case revaluations 

𝑚𝑚𝑖𝑖𝑛𝑛(𝑁𝑁𝑡𝑡, 𝑁𝑁𝑡𝑡+1)

For revaluations under this approach, substituting identities 1 and 2 into equation 13 
yields: 

4
𝑅𝑅𝑡𝑡+1 

= 𝑁𝑁

𝐾𝐾

∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑡𝑡) + 𝑁𝑁

𝐵𝐵

∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝̅𝑡𝑡) + 𝑁𝑁

𝑆𝑆

∙ (𝑝𝑝̅𝑡𝑡 − 𝑝𝑝𝑡𝑡)

ECB Statistics Paper Series No 44 

(14) 

41 

 
 
 
 
 
 
 
 
 
This expression is similar to equation 6, if 

  and 

  are substituted with the 

average price 

, and intra-period transactions are ignored. 

𝑝𝑝𝑠𝑠𝑖𝑖

𝑝𝑝𝑠𝑠𝑗𝑗

𝑝𝑝̅𝑡𝑡

Method 5. Value all sales and purchases made during the reference period at the 
average market price during the reference period. 

Method 5 is similar to Method 4, but it uses the average of the daily prices during the 
  instead of the simple average of the prices at the beginning and 
reference period 
at the end of the period. Equations 11, 12, 13 and 14 can be rewritten as: 

𝑎𝑎
𝑝𝑝̅𝑡𝑡 

𝑎𝑎
=   𝑝𝑝̅𝑡𝑡 

𝐵𝐵

∙ (𝑁𝑁

− 𝑁𝑁

𝑆𝑆

𝑎𝑎
) =   𝑝𝑝̅𝑡𝑡 

∙ (𝑁𝑁𝑡𝑡+1 − 𝑁𝑁𝑡𝑡)

5
𝑇𝑇𝑡𝑡+1 
and 

5
𝑅𝑅𝑡𝑡+1 

𝑎𝑎
= 𝑁𝑁𝑡𝑡+1 ∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝̅𝑡𝑡 
𝐾𝐾

=   𝑁𝑁

𝑎𝑎
) − 𝑁𝑁𝑡𝑡 ∙ (𝑝𝑝𝑡𝑡 − 𝑝𝑝̅𝑡𝑡 
)
𝐵𝐵
∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝𝑡𝑡) + 𝑁𝑁

𝑎𝑎
∙ (𝑝𝑝𝑡𝑡+1 − 𝑝𝑝̅𝑡𝑡 

) + 𝑁𝑁

𝑆𝑆

𝑎𝑎
∙ (𝑝𝑝̅𝑡𝑡 

− 𝑝𝑝𝑡𝑡)

(15) 

(16) 

ECB Statistics Paper Series No 44 

42 

 
 
 
 
 
Annex 2 – Differences between the 
indirect methods and the benchmark 

This annex analyses the differences between the indirect methods and the 
benchmark ESA 2010 methodology in respect of the price volatility (as measured in 
this paper) of individual securities, for each type of instrument and each quarter 
under analysis. The differences are shown in relative terms (i.e. as a percentage of 
the transactions collected under the ESA 2010 methodology). To ensure the charts 
are as readable as possible, the lower and upper 5% tail of the distributions have 
been eliminated. 

As Charts A2.1-3 show, there seems to be no clear relationship between the size of 
the differences and price volatility. The correlation coefficients between the size of 
the differences and price volatility are fairly homogeneous across the methods, 
ranging, over the period, between -0.122 and 0.172 for short-term debt securities, -
0.030 and 0.031 for long-term debt securities and -0.032 and 0.027 for equity (the 
values are based on the full sample). 

ECB Statistics Paper Series No 44 

43 

 
 
Chart A2.1 
Short-term debt securities – differences between the indirect methods and the 
benchmark 

(as a percentage of the ESA 2010 transactions) 

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 

ECB Statistics Paper Series No 44 

44 

 
 
 
Chart A2.2 
Long-term debt securities – differences between the indirect methods and the 
benchmark 

(as a percentage of the ESA 2010 transactions) 

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 

ECB Statistics Paper Series No 44 

45 

 
 
 
 
 
Chart A2.3 
Equity – differences between the indirect methods and the benchmark 

(as a percentage of the ESA 2010 transactions) 

Sources: Banca d’Italia (BSI), ECB (SHSS and CSDB) and authors’ calculations. 

ECB Statistics Paper Series No 44 

46 

 
 
 
References 

Banca d’Italia (2008), “Matrice dei conti”, Circular No 272, 30 July. 

Deutsche Bundesbank (2015), “Securities holdings statistics for analysing holdings 
of securities in Germany and Europe: methodology and results“, Monthly Report, 
March, pp. 95-107. 

European Central Bank (2002), “Final report of the task force on portfolio investment 
collection systems”, June. 

European Central Bank (2010), “The Centralised Securities Database in brief”, 
February. 

European Central Bank (2015), “Who holds what? New information on securities 
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ECB Statistics Paper Series No 4x / April 2023 

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Acknowledgements 

This paper is the result of methodological research carried out as part of the work by the Expert Group on the Integrated Reporting 
Framework of the Statistics Committee of the ESCB. The current version has benefited from useful comments and advice received from 
Thorsten Radke, Riccardo Renzi, Lucia Ristori and Nuno Silva. The technical support provided by Fabrizio Timo is also gratefully 
acknowledged. In addition, the authors would like to express their gratitude to the Editorial Board of the ECB Statistics Paper Series for 
the support and the suggestions received. 

Antonio Colangelo 
European Central Bank, Frankfurt am Main, Germany; email: antonio.colangelo@ecb.europa.eu 

Asier Cornejo Pérez 
European Central Bank, Frankfurt am Main, Germany; email: asier.cornejo_perez@ecb.europa.eu 

Danilo Liberati 
Banca d’Italia, Rome, Italy; email: danilo.liberati@bancaditalia.it 

Giorgio Nuzzo 
Banca d’Italia, Rome, Italy; email: giorgio.nuzzo@bancaditalia.it 

Antonio Rodríguez Caloca 
European Central Bank, Frankfurt am Main, Germany; email: antonio.rodriguez_caloca@ecb.europa.eu 

© European Central Bank, 2023 

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This paper can be downloaded without charge from the ECB website or from RePEc: Research Papers in Economics. Information on all 
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ISBN 978-92-899-6106-6, ISSN 2314-9248, doi: 10.2866/065328, QB-BF-23-003-EN-N