Prof. Dr. rer. nat. (F) Wolfgang Breymann Zurich University of Applied Sciences - E-Finance Lab
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IDP Institut für
Datenanalyse und
Prozessdesign
Modelling and Identification
of Financial products –
the ACTUS Approach
Prof. Dr. rer. nat. (F) Wolfgang Breymann
Zurich University of Applied Sciences
Joint Spring Conference 2016 of E-Finance Lab and IBM 2016:
“Identifiers and Identification management in the Financial World and Beyond –
Requests, Solutions, and Applications”
Frankfurt, February 16th, 2016
Outline IDP Institut für
Datenanalyse und
Prozessdesign
• What is ACTUS?
• Why ACTUS?
• The ACTUS principles
• ACTUS proof of concept
• Putting together the elements
needed for analyzing the whole system
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 2
IDP Institut für
Datenanalyse und
Prozessdesign
What is ACTUS?
Algorithmic Contract
Types Unified Standard IDP Institut für
Datenanalyse und
Prozessdesign
• The ACTUS Financial Research Foundation
is building a data standard specifically designed to enable
the full range of financial analyses of importance for risk
management and financial regulation.
• It is not merely a classification system, but a computational
infrastructure for consistent, transparent and efficient financial
analysis (return, risk, stress tests, etc.)
• ACTUS consists of
1. A Data Dictionary which defines all contract terms required for
financial analysis
2. A set of Contract Types (CT) which are computable algorithms
that are able to precisely generate state-contingent cash flows at
the individual contract level
To our knowledge, there is no other current effort that aspires to
create a data standard with this capability
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 4
IDP Institut für
Datenanalyse und
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Why ACTUS?
Interactions
in the Financial Network IDP Institut für
Datenanalyse und
Prozessdesign
Ownership Legal entities
structure
Source: Vitali, Glattfelder, Battiston, The Network of Global Corporate Control. Open Access 6 (10), e25995 (2011).
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0025995. Accessed April 2014.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 6
Interactions
in the Financial Network IDP Institut für
Datenanalyse und
Prozessdesign
Financial
interactions
mediated through
financial contracts
Transaction Transaction
Processing Processing
System 1 System 2
• Different institutions use different modeling of contract types
• Results
Expected +cfl +cflare
1 2 …. not comparable
n+cfl -cfl …. -cfl -cfl
n Expected2 1
• Standardization necessary
Cash-Flows
t t
Cash-Flows
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 7
Algorithmic Contract
Types Unified Standard IDP Institut für
Datenanalyse und
Prozessdesign
• The financial contract is the elementary building block
of a bank’s balance sheet and the whole financial system.
• Therefore, it is the “atomic” element of a granular approach.
• The input for all financial analysis is the expected cash flow stream
generated by a financial contract.
• The legal text of a financial contract establishes these cash-flow
generating rules.
• The cash flow stream is subject to the values of external factors such as
market risk (interest rates, FX rates, etc.) and counterparty risk.
• The cash flow generating rules give financial meaning to the data
elements in the contract and the impact of the external environment.
• The ACTUS contract types encode these rules algorithmically, which
creates the ACTUS standard.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 8A closer look at
financial contracts IDP Institut für
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Endre vullumsandio dion endipsummy nos dolobore vel ut alis
amet autem dionseq uismodigna feumsan dionse dolor
ullandre magna feuipsummy nullum ad tin ….
Bank shall pay the sum of __________
1000 USD on 2013.01.01
__________ (date)
to Mr.
______
Smith (obligor). Obligor will pay an interest 10
of ____ % on a
semi-annual basis and repay the full amount 3in ____ years.
Date, Signature
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 9Algorithmic Contract
Types Unified Standard IDP Institut für
Datenanalyse und
Prozessdesign
• The financial contract is the elementary building block
of a bank’s balance sheet and the whole financial system.
• Therefore, it is the “atomic” element of a granular approach.
• The input for all financial analysis is the expected cash flow stream
generated by a financial contract.
• The legal text of a financial contract establishes these cash-flow
generating rules.
• The cash flow stream is subject to the values of external factors such as
market risk (interest rates, FX rates, etc.) and counterparty risk.
• The cash flow generating rules give financial meaning to the data
elements in the contract and the impact of the external environment.
• The ACTUS contract types encode these rules algorithmically, which
creates the ACTUS standard.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 10Data and
algorithmic rules IDP Institut für
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Prozessdesign
Example of contract data:
Notional amount : 100 USD
Value date : 1.1.00
Maturity date : 31.12.04
Interest payment cycle : 6 months
Interest rate : 10%, 30/360
1.1.0 1.1.1 1.1.2 1.1.3 1.1.4 31.12.4
16.02.2016 1.1.0 Joint Spring
1.1.1 1.1.2
Conference 2016 of E-Finance1.1.3
Lab and IBM 1.1.4 31.12.4
11Algorithmic Contract
Types Unified Standard IDP Institut für
Datenanalyse und
Prozessdesign
• The financial contract is the elementary building block
of a bank’s balance sheet and the whole financial system.
• Therefore, it is the “atomic” element of a granular approach.
• The input for all financial analysis is the expected cash flow stream
generated by a financial contract.
• The legal text of a financial contract establishes these cash-flow
generating rules.
• The cash flow stream is subject to the values of external factors such as
market risk (interest rates, FX rates, etc.) and counterparty risk.
• The cash flow generating rules give financial meaning to the data
elements in the contract and the impact of the external environment.
• The ACTUS contract types encode these rules algorithmically, which
creates the ACTUS standard.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 12Building blocks of
contract algorithms IDP Institut für
Datenanalyse und
Prozessdesign
• Low level:
• Treatment of time
• Anchors and cycles
• Day count conventions
• High Level:
• Interest Payment Events
• Notional Principal Events
• Rate Reset Events
• Dividends
• Fees
• Margining
• Optionality
• Settlement
• Credit Enhancement
• Ordering (sequencing) of event types with equal time stamp
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 13IDP Institut für
Datenanalyse und
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The ACTUS ConceptACTUS
Modeling Logic IDP Institut für
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Brammertz, Akkizidis, Breymann,
Market Entin, Rustmann, Unified Financial
Risk Analysis. Wiley, Chichester, 2009.
Inputs
Counterparty Behavior
Risk Contracts Risk
Contract Events e1 e2 e3 …. en-1 en
t
Cash-Flows cfl1 cfl2 …. cfln
conditional on
t
risk factor states
Liquidity Income Value
Analytical
Results
Liq. @ Risk Inc. @ Risk Value @ Risk
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 15Analytical Metrics IDP Institut für
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Liquidity:
• Aggregate cash-in and cash-out
Exposure:
• Aggregate cash-flows w/r to a given counterparty
Value:
• Aggregate discounted (risk neutral) expected cash flows
Income:
• Aggregate cash flows over time
Sensitivities:
• Compute derivatives of value w/r to risk factors
Risk:
• Apply risk measure to probability distribution of a quantity under
consideration
All operations except risk measures are linear.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 16Aggregation IDP Institut für
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No aggregation at the
level of contract data
(NON-LINEAR)
Aggregation only at the
level of cash flow data
(LINEAR)
Aggregation is possible at different levels up to group level
or even the financial system by linear mathematical operations.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 17IDP Institut für
Datenanalyse und
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ACTUS Proof of ConceptPoC with ECB Data –
The Portfolio IDP Institut für
Datenanalyse und
Prozessdesign
Sample overview: Number of Observations =3809
Sectors: Only General Government (S13), subdivided into
Central Govt. State Govt. Local Govt. Social security fund
No. obs: 1290 1944 491 84
S
Countries: AT BE CY DE ES FI FR GR IE IT MT NL PT SI
K
No. obs 149 413 46 1712 346 31 478 102 29 219 81 108 42 33 20
Contract Maturity Cycle Of Interest Payment
Deal Date monthly quarterly bi-annually annually zero coupon
Date
4 391 474 2237 703
Earliest 1986-06- 2015-04-01
20 (matured bond)
Notional Principal Nominal Interest
Latest 2015-03- 2090-11-08 Rate
31 (data quality issue)
Min 0.0 (data quality issue) 0.0% (zero-coupon bond)
Median 76’690’000 1.55%
Max 38’530’000’000 2.319%
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 19PoC with ECB Data –
Raw cash-flow results IDP Institut für
Datenanalyse und
Prozessdesign
Sample contract events with cash flows per 5/1/15
Event Event Event Time Nominal Nominal Nominal
Contract ID Date Type Value (in years) Value Rate Accrued Currency Country Sector
DE0000000001 2015-05-01T00:00Z[UTC] AD0 0.0 0.086111 50000000.0 0.0352 151555.6 EUR DE S_1312
DE0000000001 2015-12-02T00:00Z[UTC] IP 1183111.0 0.586111 50000000.0 0.0352 0 EUR DE S_1312
DE0000000001 2016-12-02T00:00Z[UTC] IP 1760000.0 1 50000000.0 0.0352 0 EUR DE S_1312
DE0000000001 2017-12-04T00:00Z[UTC] IP 1769778.0 1.05556 50000000.0 0.0352 0 EUR DE S_1312
DE0000000001 2018-12-03T00:00Z[UTC] IP 1755111.0 0.997222 50000000.0 0.0352 0 EUR DE S_1312
DE0000000001 2019-12-02T00:00Z[UTC] IP 1755111.0 0.997222 50000000.0 0.0352 0 EUR DE S_1312
DE0000000001 2019-12-02T00:00Z[UTC] MD 50000000.0 0 0.0 0 0 EUR DE S_1312
GR0000000001 2015-05-01T00:00Z[UTC] AD0 0.0 0.038889 3000000000.0 0.0475 5541667 EUR GR S_1311
GR0000000001 2016-04-18T00:00Z[UTC] IP 142895833.0 0.963889 3000000000.0 0.0475 0 EUR GR S_1311
GR0000000001 2017-04-17T00:00Z[UTC] IP 142104167.0 0.997222 3000000000.0 0.0475 0 EUR GR S_1311
GR0000000001 2018-04-17T00:00Z[UTC] IP 142500000.0 1 3000000000.0 0.0475 0 EUR GR S_1311
GR0000000001 2019-04-17T00:00Z[UTC] IP 142500000 1 3000000000.0 0.0475 0 EUR GR S_1311
GR0000000001 2019-04-17T00:00Z[UTC] MD 3000000000 0 0.0 0 0 EUR GR S_1311
The 4,000 bonds generate a total of 3,866,785 contract events.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 20PoC with ECB Data –
Liquidity results IDP Institut für
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Prozessdesign
Aggregate liquidity (i.e. state-contingent cash flows) from central
government issued bonds expected over the next years:
by cash flow type (interest or principal) by country of issuance
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 21PoC with ECB Data –
Interest rate stress test IDP Institut für
Datenanalyse und
Prozessdesign
Stress testing 1: Market exposures
Base scenario:
Use Euro-area yield curve
observed on 5/1/15 for
discounting
Stress scenarios:
We apply 100 yield curve “shocks”
(shift, steepening, bending, etc.) in
order to assess the impact on “Fair
value”
(Gives a better metrics than
duration)
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 22PoC with ECB Data –
Credit default stress test IDP Institut für
Datenanalyse und
Prozessdesign
Stress test 2: Credit exposures:
Analysis of exposure to govern-
ment credit.
We show the aggregate, yearly cash
flows by government credit ratings
(S&P).
Stress testing (histogram bars):
Assuming default of e.g. all
“speculative” bonds in year 1 will lead
to a loss of the dark blue colored
aggregate cash flows (no recovery).
Monte-Carlo (red line):
Simulation of defaults based on a
stochastic credit rating migration
matrix model provides an expected
value for liquidity (no recovery).
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 23IDP Institut für
Datenanalyse und
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Putting together the elements
needed for analyzing
the whole financial systemFinancial Network Modeling
with ACTUS IDP Institut für
Datenanalyse und
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Stress Test
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 25The Financial Data
Supply Chain IDP Institut für
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Contract Risk Factor
Terms Financial
State of the State
Analysis
Risk Factors Contingent
Results
Cash Flows
Computational Steps:
1. Simulate risk factor scenarios
2. For each contract and each risk factor scenario
generate the cash flow
3. Aggregate the cash flows according to the
desired analytical metrics and levels of
aggregation
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 26Big Data
System Architecture IDP Institut für
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16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 27Conclusions IDP Institut für
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• The ACTUS standard is suitable to describe all financial
transactions between market participants.
• The initial POCs have shown ACTUS’ ability to ease the
stressful process of stress testing and financial analysis.
• Exchanging ACTUS data is technically simple because it is
highly structured and precisely defined.
• The ACTUS approach enables far more analytical flexibility than
just the ability to undertake stress tests.
Examples are:
o Going concern analysis
o Dynamic analysis
o Monte-Carlo simulations, and
o Meaningful network analysis using real data.
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 28ACTUS Foundations IDP Institut für
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Two non for profit organizations:
• ACTUS Financial Research Foundation & ACTUS Users Association
Board members:
• Hon. Allan I. Mendelowitz, Ph.D. (President)
Served as chairman of the Federal Housing Finance Board, the regulatory agency responsible for overseeing the
safety and soundness of the trillion dollar Federal Home Loan Bank System
• Dr. Willi Brammertz (Chairman)
“Father” of riskpro®, which was sold to more than 300 banks
Lead author of “Unified Financial Analysis” (UFA) and “Father” of ACTUS
• John Bottega
Served as Chief Data Officer of Citi, FRB of New York, and Bank of America
Member of the EDM Council
• Jefferson Braswell (B.A. Princeton, M.Sc Computer Science Berkley)
Co-founder and CEO/CTO of Risk Management Technology Radar (sold to Fair Isaac)
Member of the Board of Directors of the Global LEI Foundation (GLEIF)
• Prof. Wolfgang Breymann (PhD Physics)
Head of Research Area, Zurich University of Applied Sciences (ZHAW)
Co-Author of “UFA”; enabled ACTUS-takeoff through collaboration with ZHAW
• Thomas E. Day
Managing Director, PricewaterhouseCoopers
• Jan Klein
CFO of MTC WorldWide; formerly executive in residence, Stevens Institute of Technology
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 29Building The Future of
Financial Data IDP Institut für
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Prozessdesign
Questions, Comments,
and
Offers of financial support
are welcome ...
www.projectactus.org
Visit our Website for:
• An introduction to the ACTUS Standard
• Descriptions of each Contract Type
• The ACTUS Data Dictionary
• The ACTUS Academy with online educational lectures on how to use ACTUS
• Relevant documents
• Access to the first 12 programmed algorithms, so that anyone can take
ACTUS for a test drive, 6 more nearly ready.
wolfgang.Breymann@zhaw.ch
16.02.2016 Joint Spring Conference 2016 of E-Finance Lab and IBM 30You can also read
