1. © Brammertz Consulting, 20091Date: 22.11.2015 Unified Financial Analysis The Risk&Finance Lab Chapter 3: Financial Contracts Willi Brammertz / Ioannis Akkizidis Input elements

2. © Brammertz Consulting, 20092Date: 22.11.2015 Contract: The focal point of finance > The contract is the prime container of the financial rules > The contractual agreement is the only hard fact of finance > „Mechanical“ part of finance, therefore pivotal > Modeling of non-mechanical part: Behavior

3. © Brammertz Consulting, 20093Date: 22.11.2015 Where is the complexity?

4. © Brammertz Consulting, 20094Date: 22.11.2015 The need for standardization > Risk factors have certain degree of standardization > Markets: Bloomberg, Reuters > Counterparties: LEI > Behavior: Must be an open dimension > Financial contract is pivotal element > Most complex part and „mechanically representable“ > Despite this center stage: No standard yet

5. © Brammertz Consulting, 20095Date: 22.11.2015 Stock answers > Data has to be standardized > Semantic Depositories > Data Warehouses Data A1 A2… A3 An Contract Algorithms Data A1 A2 A3 … An Contract Events State Contingent Cash Flows

6. © Brammertz Consulting, 20096Date: 22.11.2015 Emerging standard

7. © Brammertz Consulting, 20097Date: 22.11.2015 Why unique data is not sufficient The 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?

8. © Brammertz Consulting, 20098Date: 22.11.2015 Time Total Principle Value Date Possible solution 1: Classical Bond.. Maturity Date

9. © Brammertz Consulting, 20099Date: 22.11.2015 Time Total Principle Value Date Possible solution 2: Classical Annuity IP+PR.. Maturity Date

10. © Brammertz Consulting, 200910Date: 22.11.2015 Time Total Principle Value Date Possible solution 3: Linear amortizer Maturity Date..

11. © Brammertz Consulting, 200911Date: 22.11.2015 Necessary condition for non-ambiguous interpretation > Well defined data > Knowledge about > intended cash-flow exchange pattern > The underlying algorithms > The algorithms must represent the legally defined intention > A strict separation between data, algorithms and results is not possible in finance 11

12. © Brammertz Consulting, 200912Date: 22.11.2015 Different stages of standardization 1. The cash flow generation rules are defined for each real- life contract individually. 2. Using a set of predefined rules: one defines elementary financial rules such as repricing patterns, amortization patterns and so on. These rules are then combined on an ad hoc basis to replicate the behavior of real life financial contracts. 3. Using a set of predefined standard contract types, where each contract type is a fixed combination of rules. Each real life financial contract is then mapped into one of these contract types. 4. Method 3 for the big bulk (100-x%) and method 1 or 2 for the rest.

13. © Brammertz Consulting, 200913Date: 22.11.2015 Principal role of Contract Types (Choice 4) > CT’s describe the exact transmission mechanics between > Financial contract > Risk factor environment > Expected financial events > State contingent cash flows > Given a > Financial contract > Exact risk factor environment > The state contingent cash flows are unambiguously defined > Special solution for contracts outside the standard. 13

14. © Brammertz Consulting, 200914Date: 22.11.2015 Further reasons for choice 4 > Factual low variety of financial contracts > 98+% of all real life patterns can be represented with two and a half dozen patterns > Historical Experience > Practitioners thinking | transaction systems > Dynamic Simulation

15. © Brammertz Consulting, 200915Date: 22.11.2015 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 On-Balance Loans

16. © Brammertz Consulting, 200916Date: 22.11.2015 High level architecture

17. © Brammertz Consulting, 200917Date: 22.11.2015 Basic Contract Types

18. © Brammertz Consulting, 200918Date: 22.11.2015 Combined Contract Types

19. © Brammertz Consulting, 200919Date: 22.11.2015 Parent/child relationships

20. © Brammertz Consulting, 200920Date: 22.11.2015 Contract Types Taxonomy Mapping Interface Real-Life financial contracts Non Maturities SCI Contracts Maturities CFL CLM DSC ZCB PAM PAX RGM UMP CSH STK COM IDX RGX ANN ANX NGM NGX PBN Fixed Income Basic Contract_Types Credit Risk CRL GAR COL LIM Any contract type can have the Collateral role

21. © Brammertz Consulting, 200921Date: 22.11.2015 Example 1: Discounted paper

22. © Brammertz Consulting, 200922Date: 22.11.2015 Example 2: PAM fixed

23. © Brammertz Consulting, 200923Date: 22.11.2015 Example 3: PAM variable

24. © Brammertz Consulting, 200924Date: 22.11.2015 Example 4: Classical annuity, fixed

25. © Brammertz Consulting, 200925Date: 22.11.2015 Example 5: Classical annuity, variable

26. © Brammertz Consulting, 200926Date: 22.11.2015 Example 6: Regular amortizer with step-up

27. © Brammertz Consulting, 200927Date: 22.11.2015 Example 7: Plain vanilla swap

28. © Brammertz Consulting, 200928Date: 22.11.2015 Example 8: FRA

29. © Brammertz Consulting, 200929Date: 22.11.2015 Example 9: European bond option

30. © Brammertz Consulting, 200930Date: 22.11.2015 What to do with the 2-%? Non-standard Contract Types

31. © Brammertz Consulting, 200931Date: 22.11.2015 Risk factor states, state contingent events/cash flows and analysis elements

32. © Brammertz Consulting, 200932Date: 22.11.2015 An alternative analogy DNA and gene expression DATAALGORITHMS

33. © Brammertz Consulting, 200933Date: 22.11.2015 Comparing DNA and CT DNA > DNA information > Gene expression > Context sensitive > Result: > Proteins > Some other results > Important: Cells are autark in reproduction CT > Contract information > CT specific algorithm > Risk factor sensitive > Result: > State contingent cash flows > Analysis elements > Contracts must become autark in results production

34. © Brammertz Consulting, 200934Date: 22.11.2015 Static Analysis Dynamic Analysis ReutersBloomberg...... Market Data Behavioral Assumptions Contract Data Bonds Savings Swaps.... USD.GOVEUR.SWAUSD/EUR... Select Aggregate Counterparty Data External Data Internal Data Hierar chy.... IDNameRating... Summix TM ETL V_DM_DC_P Interface … Behavioural Statistical + Adjustment up/down Qualitative scores Dataflow in practice Results Historization Central Data Store