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

© Brammertz Consulting, 20092Date: 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

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

© Brammertz Consulting, 20094Date: 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

© Brammertz Consulting, 20095Date: 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

© Brammertz Consulting, 20096Date: Emerging standard

© Brammertz Consulting, 20097Date: Why unique data is not sufficient The rational for Contract Types > Example of a set of contract data > Value date: > Principal: 1000 > Interest payment cycle: quarterly > Interest rate: 5%, fixed > Maturity date: > What are the expected cash flows?

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

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

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

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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.

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: High level architecture

© Brammertz Consulting, Date: Basic Contract Types

© Brammertz Consulting, Date: Combined Contract Types

© Brammertz Consulting, Date: Parent/child relationships

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: Example 1: Discounted paper

© Brammertz Consulting, Date: Example 2: PAM fixed

© Brammertz Consulting, Date: Example 3: PAM variable

© Brammertz Consulting, Date: Example 4: Classical annuity, fixed

© Brammertz Consulting, Date: Example 5: Classical annuity, variable

© Brammertz Consulting, Date: Example 6: Regular amortizer with step-up

© Brammertz Consulting, Date: Example 7: Plain vanilla swap

© Brammertz Consulting, Date: Example 8: FRA

© Brammertz Consulting, Date: Example 9: European bond option

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

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

© Brammertz Consulting, Date: An alternative analogy DNA and gene expression DATAALGORITHMS

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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