Understanding Financial Contracts and their Unique Position in Risk and Finance Analysis

1. Input elements Unified Financial Analysis The Risk&Finance Lab Chapter 3: Financial Contracts Willi Brammertz / Ioannis Akkizidis

2. Table of Content Central and Unique Position of Financial Contracts Bank Data and Data Warehouses A Closer Look at Financial Contracts Concept of Contract Type ACTUS and Ariadne

3. Contract: The focal point of finance • The contract is the container of the rules of exchange • The contractual agreement is the only hard fact of finance • Financial contract: • Exchange of cash against cash • Cash is a number: Therefore number against number • Therefore only contract that can be fully represented by algorithms = Smart Contract • This unique feature has not been exploited so far

4. Where is the complexity? • Financial contracts represent the most complex part of finance • Financial contracts are the least standardized part of finance • Why?

5. Table of Content Central and Unique Position of Financial Contracts Bank Data and Data Warehouses A Closer Look at Financial Contracts Concept of Contract Type ACTUS and Ariadne

6. Interfacing Transaction Systems with Analytic Systems Data from Transaction Systems Analytical Systems n Transaction Systems, m Analytical Systems = n*m Interfaces

7. From Many SmallTo One Huge Data Heap Transaction Systems Data Warehouse Analytical Systems n Transaction Systems, m Analytical Systems = n+m Interfaces

8. Table of Content Central and Unique Position of Financial Contracts Bank Data and Data Warehouses A Closer Look at Financial Contracts Concept of Contract Type ACTUS and Ariadne

9. The ReconciliationProblem BANK Cost Ratio Cost Saving Potential 60% Analysis Level Financial Analysis i = f(E[Cash-Flows],,,) Expected CFL’s under forecasted conditions 60+% Transaction Processing Level Real exchanged CFL’s Under current conditions -40%

10. Financial contracts today 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 __________ USD on __________ (date) to ______ (obligor). Obligor will pay an interest of ____ % on a semi-annual basis and repay the full amount in ____ years. Date, Signature Judex non calculate 1000 2013.01.01 10 Mr. Smith 3

11. The real AgreementContracts and Algorithms

12. Another Agreement

13. Contract TermsParameters of Functions 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 __________ USD on __________ (date) to ______ (obligor). Obligor will pay an interest of ____ % on a semi-annual basis and repay the full amount in ____ years. Date, Signature 1000 2013.01.01 10 Mr. Smith 3

14. The Real Contract:Algorithmic Representation public class […] AccrualCalculator{ […] /** calculate accrued interest **/ public double nominalAccrued(StateSpace states){ return states.getNominalValue()* states.getTimeFromLastEvent()* states.getNominalRate(); } } public class InterestPaymentEvent […]{ […] /** calculate accrued interest **/ yearFraction = dayCount.yearFraction( states.getLastEventDate(), super.getCalculateDate()); states.setTimeFromLastEvent(yearFraction); nominalAccrued = accrualCalculator.nominalAccrued(states)+ states.getNominalAccrued(); /** update state variables **/ […] /** set event value (interest cash flow) **/ super.setEventValue( (1+ scalingEffect.getInterestPayments()*(states.getScalingMultiplier()-1))* nominalAccrued); }

15. Effect of CentralData Stores Analysis Level Value Balance Sheet Add Risk Factors „Reinvent“ Algorithms ~ Data Warehouse Data WITHOUT Algorithms …. …. -cfln -cfl1 -cfl2 +cfln +cfl2 +cfl1 t t Expected Cash-Flows Expected Cash-Flows Transaction Processing Data AND Algorithms

16. How can we aggregate algorithms?

17. ACTUSAlgorithmic Contract Standard THE SMART FINANCIAL CONTRACT

18. ACTUS and Data Stores Analysis Results Add Risk Factors = CFL-Algorithms Data and Algorithms STANDARDIZED Data Store …. …. -cfln -cfl2 -cfl1 +cfln +cfl2 +cfl1 t t = Expected Cash-Flows Expected Cash-Flows Data and Algorithms UNSTANDARDIZED

19. ACTUSTarget Architecture Analysis Results Add Risk Factors = Data Store Data and Algorithms ACTUS STANDARD

20. ACTUS Contract Types Overview, State of Development ACTUS Contract Types Basic Combined Non- Maturities Credit Enhancements Credit Derivatives Securiti- zation Maturities Symmetric Options PAM CSH CEG SWAPS OPTNS CDSWP SCRMR ANN UMP CEC SWPPV CAPFL TRSWP SCRCR NAM STK FXOUT BNDCP CLNTE LAM COM FUTUR BNDWR ANX CFXOP NAX IRXOP completed in planning planned LAX STXOP CLM CMXOP PBN FXXOP * Definitions and explanations to the Contract Types may be found in the «CT-Description» Excel sheet.

21. World Bond PortfolioCoverage with PAM (Q1 2015)

22. Discoverynot an Invention …. …. -cfl1 -cfl2 -cfln +cfl1 +cfl2 +cfln t t Expected Cash-Flows Expected Cash-Flows

23. The Entire System will Benefit ..... .....

24. ACTUS is Open Source Homepage: http://www.projectactus.org Demo: http://www.projectactus.org/ACTUS/contractCalculator

25. Dataflow in Practice Market Data Counterparty Data Contract Data .... Hierarchy Internal Data .... External Data ...... Reuters Bloomberg Swaps Savings Bonds Qualitative scores Statistical ETL Behavioural + Adjustment up/down Interface V_D M_D C_P … EUR.SWA USD.GOV USD/EUR ... Select Aggregate Behavioral Assumptions Static Analysis Dynamic Analysis ID Name Rating ... Results Historization Central Data Store SummixTM

26. Table of Content Central and Unique Position of Financial Contracts Bank Data and Data Warehouses A Closer Look at Financial Contracts Concept of Contract Type ACTUS and Ariadne

27. Why unique data is not sufficientThe rational for Contract Types • Example of a set of contract data • Value date: 15.3.00 • Principal: 1000 • Interest payment cycle: quarterly • Interest rate: 5%, fixed • Maturity date: 15.3.05 • What are the expected cash flows?

28. Possible solution 1:Classical Bond Total Principle . . . . Time Value Date Maturity Date

29. Possible solution 2:Classical Annuity Total Principle IP+PR . . . . Time Value Date Maturity Date

30. Possible solution 3:Linear amortizer Total Principle . . . . Time Value Date Maturity Date Expected csh-flows can only be unanimousely derived, if the CT is known.

31. Contract Mechanisms and Contract Types

32. Common Sub-Mechanisms • Principal amortization • Principal draw-down • Interest payment • Rate adjustment • FX rates • Stock and commodity patterns • Simple options • Exotic options • Credit risk related • Behavioral • Margining On-Balance Loans

33. Basic Contract Types

34. Combined Contract Types

35. Parent/child relationships

36. Example 1:Discounted paper (PAM no IP)

37. Example 2:PAM fixed

38. Example 3:PAM variable

39. Example 4:Classical ANN, fixed

40. Example 5: Classical ANN, variable

41. Example 6:LAX with step-up

42. Example 7:Plain vanilla swap SWPPV

43. Example 8: FRA

44. Example 9:European bond option

45. Table of Content Central and Unique Position of Financial Contracts Bank Data and Data Warehouses A Closer Look at Financial Contracts Concept of Contract Type ACTUS and Ariadne

46. ACTUS and AriadneFacts and Assumptions Credit Risk Factors Market Risk Factors Behavior Risk Factors Contracts e1 e2 e3 en-1 en …. t cfl1 cfl2 cfln …. t Additional Reports e.g. Book Value, FTP, Monte Carlo, etc. Liquidity Income Value

47. Relational Structure CID LEI MOC CID AD0 MOC CID CID LEI CID AD0 MOC Results Contract Data Book Keeping Data Counterparty Data (+Rep&Aggreg Criteria) (Regulatory) Reporting &Aggregation Criteria Valuation Model Parameters Market Data Dynamic Instructions / Cash-Flow Algorithms