© DEVnet 2013

Dr. Ulrich Nögel, Partner, Quantitative Finance

HPCFinance, Tampere 14.May 2013

How new regulations turn into new challenges for business, computing and data management

Post Crisis HPC for Finance

2 Confidential

Outline

• DEVnet in brief (commercial)

• Pre-Crisis: Pricing and hedging of new complex derivatives (MC simulation)

• Large risk simulations in insurance: Hedging of VA„s

• New regulations after the crisis: Counterparty risk (Basel III, DFA, EMIR,....)

• No market for complex derivatives: Time to market more important than sophisticated

pricing models

•Risk-Triangle: Collateralization turns counterparty to liquidity risk

• Focus on funding costs: multi curve discounting, real time risk management, real time pre-

deal checks

• Real time analytics requires database technology which can handle real time data

3 Confidential

DEVnet Group in Brief

2009 2010 2011

Employees 82 95 111

Continuous growth with a solid corporate strategy

driven by long term client relationships.

DEVnet has a Dun & Bradstreet Score of 96%.

Services

• Project Management

• Business Analysis and Architecture

• SW and HW Architecture

• Implementation

• Managed Services & Hosted

Solutions

Domains

DEVnet is strictly investing in selected domains:

• Trading Architectures

• Quant Solutions

• Reporting Solutions

• Risk Architectures

• Product Automation

• Market Data and Data Management Services

60%

40%

Sector Distribution

FinancialServices Industry

Corporates andothers

4 Confidential

Our Customers and Teams have always been distributed across Europe

Amsterdam

Istanbul

Milano

London

Dublin

Stockholm

Vienna

Berlin Hannover

Kiel

Moscow

Zurich

Munich

Frankfurt

Hamburg

Wroclaw

The competence center extends our

capabilities required in trading and risk

management:

1. R&D: Cooperation with academic

institutions

2. Quantitative methodology: market data,

model development, product pricing, risk

methodology

3. Technical expertise:

• GUI development: Panopticon, Flex,

Silverlight, Webmethods, Eclipse, Web

Frontends, Excel and others

• Applications: Calypso, Sophis Risque,

Murex, kdb+, Asset Control, Pacemaker

and others

• Programming frameworks: Java, .Net,

C++ and others

• Financial engineering frameworks: R,

MATLAB, Quantlib, kdb+ analytics and

others

• Database technology: mySQL, Oracle,

DB2, Sybase, MS SQL Server, kdb+

• Market connectivity: monitoring and

management for 200+ market data

feeds

5 Confidential

Partners worldwide contribute

essential capabilities

6 Confidential

Selected Clients of Kx Systems

DEVnet, strategic partner of Kx Systems

Kx Systems, market leader in high performance databases since 1993

Typical Uses

Cross-market Analytics

Automated Trading

Real-time Business Intelligence

Risk Management

Data Capture and Distribution

Big Data Analytics

Event Processing (CEP)

Network Management

7

Academic Network and Cooperations

DEVnet„s holds relationships to academic institutions and individuals enriching our knowledge and facilitating results of highest quality for our clients

Hochschule für angewandte Wissenschaften, München (Prof. Dr. Ralf Werner, Operation Research)

Technische Universität Kaiserslautern (Prof. Dr. Ralf Korn)

Technische Universität München (Prof. Dr. Arndt Bode, Leibniz-Rechenzentrum, HPC)

Ludwig-Maximilians-Universität München (Prof. Stefan Mittnik, Ph.D., Institut für Statistik)

Fraunhofer-Institut für Techno- und Wirtschaftsmathematik

Universität Hamburg (Prof. Dr. Alexander Szimayer)

HVB Institute for Mathematical Finance (Prof. Dr. Rudi Zagst, Prof. Dr. Matthias Scherer)

Frankfurt School of Finance & Management

8

Outline

• DEVnet in brief (commercial)

• Pre-Crisis: Pricing and hedging of new complex derivatives (MC simulation)

• Large risk simulations in insurance: Hedging of VA„s

• New regulations after the crisis: Counterparty risk (Basel III, DFA, EMIR,....)

• No market for complex derivatives: Time to market more important than sophisticated

pricing models

•Risk-Triangle: Collateralization turns counterparty to liquidity risk

• Focus on funding costs: multi curve discounting, real time risk management, real time pre-

deal checks

• Real time analytics requires database technology which can handle real time data

9

Pricing complex Derivatives:The Zoo of Exotics

• Digital Options

• Barrier Options

• Rainbow and Basket Options

• Chooser Options

• Currency Options

• ESOs

• Options on Options

• Asian Options

•…. E.G. Haug. The complete guide to option pricing formulas. McGrawHill, 2.Ed. 2006

10

• Mediobanca Bond Protection 2002-2005 offers guaranteed principal redemption, plus annual coupon (payable Dec 02) each year)

where MinCoupon=0.02 and monthly return

• Mediobanca Reverse Cliquet Telecommunicazioni 2000-2005 on basket of telecom stocks, offers guaranteed principal redemption plus a final premium P

with MaxCoupon=100% and each semi-annual return

(s. Gatheral 2006, Ch.10)

10

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MinCouponrCt

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tStSrt

11

Heston model

Wiener processes with correlation

1

2

( ) ( ) ( ) ( ) ( )

( ) ( ( )) ( ) ( )

dS t S t dt v t S t dW t

dv t v t dt v t dW t

market price of risk

dtdWdW 21)(tdWi

drift

mean reversion speed

mean reversion level

volatility of volatility

risk-neutral measure

**

qr

12

DEVnet Facts and Figures – ASAP Architecture ( ), Munich

13

Outline

• DEVnet in brief (commercial)

• Pre-Crisis: Pricing and hedging of new complex derivatives (MC simulation)

• Large risk simulations in insurance: Hedging of VA„s

• New regulations after the crisis: Counterparty risk (Basel III, DFA, EMIR,....)

• No market for complex derivatives: Time to market more important than sophisticated

pricing models

•Risk-Triangle: Collateralization turns counterparty to liquidity risk

• Focus on funding costs: multi curve discounting, real time risk management, real time pre-

deal checks

• Real time analytics requires database technology which can handle real time data

14

Counterparty Risk: The financial crisis and regulatory requirements

• Financial crisis

According to the Basel Committee of Banking Supervision (BCBS)1:

"roughly two-thirds of counterparty credit losses were due to CVA losses and only one-third through actual defaults"

• Changing regulatory landscape

• Accounting: FAS 157, FAS 159, IAS 39, IFRS 13

• Regulatory: Basel III

• Formalizsation of CVA upfront as a regulatory capital charge („standard“, „advanced“, volatility of CVA, subtract CVA

from counterparty EAD)

• Including of Wrong way risk

• Framework for central counterparties (CCP) on full colleraliziation (no CVA charge)

• Introduction of Central Counterparties (CCPs)

• Trading of standardized OTC derivatives on exchanges with clearing from central counterparties

• Basel III

• Dodd-Frank Act (DFA)

• European Market Infrastructure Regulation (EMIR)

1C. Albanese. “From CVA to Margin Lending”. ICBI Risk Capital Conference. Frankfurt. Sept. 2011

15

Counterparty Risk in Context

Counterparty risk = credit risk between derivatives counterparties

• OTC Derivatives

Dramaticall growth of the last decade, mainly IR derivatives, outstanding notional ca. 350 trillion USD first half of 2008.

Even with some debate market supposed to develop strongly. „Financial weapons of mass destruction“

• Counterparty risk (derivatives) players

Large (large bank), medium (smaller bank, hedge or pension fund), small (large coorperate)

• „to big to fail“

Myth of the credit worthiness of the „to big to be allowed to fail“ institutions. No more strong credit quality of large players

• Credit derivatives

Credit default swaps (CDS) simple way of trading credit risk, but can be highly toxic (sub prime).

• Counterparty vs lending risk

Only, one party takes lending risk (bondholder takes risk). Not true for derivatives! Couterparty risk always bilateral!

Counterparty risk is a challenge for ALL financial institutions!

16

Counterparty Risk in the context of other financial Risks

Revolution of Financial risk management over the last two decades, driven by infamous financial desasters:

Barings (1995), Long Term Capital Management (1998), Enron (2001), Worldcom (2002),

Parmalat (2003), Lehman Brothers (2008),….

Couterparty risk: Intersection of different Risk types highly complex area of Risk Management

• Market risk

(Short term) risk from from movement of market prices IR, FX, CR etc, well studied, Basle I (1995), VaR

counterparty risk as combination of market and credit risk.

• Credit risk

Probability of counterpart to default, central to counterparty„s credit quality

• Operational risk

Hard to quantify, management of netting and collaterals leads to operational risk

• Liquidity risk

Asset liquidity risk and funding liquidity risk (death spiral), important to leveraged positions (margin calls),

risk in settling the collateral

17

Couterparty Credit Exposure: Definitions and Risk Measures

• Present Value of contract (no callable features)

with (generally stochastic) cashflows at times and numeriare

• If PV of contract is negative at default:

bank has a net loss of zero

• If PV of contract is positive at default:

bank has a net loss of PV

• Expected Positive Exposure (EPE)

• Positive Future Exposure (PFE)

• Expected Tail Loss (ES)

Bondowner takes credit risk, but not issuer! Not true for most derivatives!

Counterparty risk usually bilateral! Value of underlying contract is uncertain (magnitude, sign)!

N

i i

i

T

tT

t

T

T

tN

XNtV )(

tX iT tN

single contract exposure

)(:)0),(max()( tVtVtE

)()()( tVtEtEPE

xtVxtqtPFE )(:inf)()(

)()()()( tPFEtVtVtES

18

Swap Exposure Profile (diffusion vs amortization)

G. Cesari, J.Aquilina, N. Charpiollon,

Z.Filipvic, G.Lee, I. Manda. Modelling,

Pricing and Hedging Couterparty

Credit Exposure.Springer 2009

19

Credit Value Adjustment (CVA): Market Value of Couterparty Risk

• Discounted Loss of single contract

with loss given default ,discount factor and maturity of the contract

• CVA defined as risk-neutral expectation of discounted loss

with risk neutral probability of default between s and t (e.g. from CDS spreads). Conditioning

cruicial when dependence between exposure and credit quality (right/wrong way risk)

• Assuming independence

• In gerneral simulation of exposure needed at fixed set

M. Pykhtin, S. Zhou. A Guide to Modelling Counterparty Credit Risk. 2007

)(),0(1 EBLGDL T

),( stBiT

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QQ

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20

High Level Architecture

FO/RISK Booking

Systems Fixed Income

Vanillas

Fixed Income

Exotics

Credit

Derivatives FX

Trading Desks

Postions/Market

Data/Derived Data

Postions/Market

Data/Derived Data

Postions/Market

Data/Derived Data

Postions/Market

Data/Derived Data

Portfolio

Management Portfolio Aggregation

Analytics Szenario Generation Instrument Pricing

EPE, PFE, CVA, etc Reporting

21

Implementing Challenges 1. Market Risk Factor Scenario Generation

• estimation of correlation

• thousends of factors for large portfolios (performance! , HPC)

2. Consistency of scenarios across systems

• more stringent then generally in FO or Risk Systems

• correlation, models, discount and credit curves

3. Consistency of pricing libraries and calibration

• not designed to be integrated in CVA framework

• large number of different systems

4. Valuation of Instruments

• most exotics priced with PDEs or MC (unfeasible inside MC!!)

• approximation of products by simplified representations

22

Credit Valuation Adjustments Steps

1. Market Risk Factor Scenario Generation Simulation Models, Risk Scenario Generation

2. Position Pricing and Valuation Single Instruments, American Monte Carlo (AMC), Sensitivities

3. Exposure and Risk Metrices PFE, EPE, CVA

4. Portfolio Aggregation and post trade processing Netting, collateral posting, Close-out Risk, Right-way/Wrong way exposure,

5. Database and Reporting Market data, trade details, limits, legal information

6. Steering and Mangement Hedging, Profitability, Economic Capital

23

Outline

• DEVnet in brief (commercial)

• Pre-Crisis: Pricing and hedging of new complex derivatives (MC simulation)

• Large risk simulations in insurance: Hedging of VA„s

• New regulations after the crisis: Counterparty risk (Basel III, DFA, EMIR,....)

• No market for complex derivatives: Time to market more important than sophisticated

pricing models

•Risk-Triangle: Collateralization turns counterparty to liquidity risk

• Focus on funding costs: multi curve discounting, real time risk management, real time pre-

deal checks

• Real time analytics requires database technology which can handle real time data

24

• Integrate existing

methodology into an

enterprise solution

• Leverage market standards

• Enable users to deploy new

methodology efficiently

• Reduce complexity

• Ensure efficient operations

Objectives

Situation, Challenges and Objectives

• Project costs and time-to-

market

• Operational risks

• Performance

• Errors

• Availability

Implications

• New regulatory

requirements

• New functionality

• Rising complexity of the

environment

Situation

25

Functional Architecture – Development and Code Integration

Calculation Engine

Methodology

Data Service

DWH/DB Excel Other Data Sources

Model Executor

Data Access API Data Access API Data Access API

Model Executor

Model Executor

Model Executor

Model Executor

Model Executor

Model Executor

Execution Controller

Tool Set 1 Tool Set 2 Tool Set n

Code Repository

Compiler

Deployment

Development

Environment Tool Set

Da

ta A

cce

ss

AP

I

26

Repositories

Case Study: Quant Development Process

Position Data Market Data Issuers /

Counterparties

CRM Fund Data

Instrument Data

Quant Matlab Development Central Compiler Code Repository Deployment

Functions and

pricing models

DEVnet Gateway (Communication Hub)

27

Outline

• DEVnet in brief (commercial)

• Pre-Crisis: Pricing and hedging of new complex derivatives (MC simulation)

• Large risk simulations in insurance: Hedging of VA„s

• New regulations after the crisis: Counterparty risk (Basel III, DFA, EMIR,....)

• No market for complex derivatives: Time to market more important than sophisticated

pricing models

•Risk-Triangle: Collateralization turns counterparty to liquidity risk

• Focus on funding costs: multi curve discounting, real time risk management, real time pre-

deal checks

• Real time analytics requires database technology which can handle real time data

28

kdb+ by Kx Systems – a unique approach for managing large datasets

Three technical concepts in one technology

1. Stream processing and CEP

2. High speed in-memory database

3. Historical database

Performance – kdb+ provides a performance advantage in any aspect

Support for parallel access to large databases (Petabytes+)

Columnar storage as well as publish and subscribe mechanisms

Analytics – kdb+ includes a programming language with direct support for in-database processing

Operates on data directly – no extract or export required

The query language q is highly optimized for time-series analysis and data aggregation

Architecture – Uses industry standard and commodity hardware

Runs on all major platforms, e.g. Linux, Solaris, Windows and Mac OS X

Very simple API available for any environment, e.g. C/C++, .Net, Java, R, MATLAB

Allows virtually unlimited flexibility to grow with profitability requirements – reducing TCO

29

Business benefits

kdb+ reduces the total cost of ownership (TCO) due to three main factors:

Performance kdb+ with its Q programming language continuously outperforms its competitors (see STAC benchmarks), i.e.

kdb+ efficiently uses available hardware.

kdb+ significantly reduces hardware and data center costs required to achieve the defined business

performance.

Flexibility kdb+ provides a programming language (Q) that is optimized for storage and analytics of large datasets, i.e.

kdb+, the Q language and the integrated tools significantly reduce development cycles.

kdb+ reduces development costs for new or changing business requirements.

Scalability kdb+ is designed for parallelized architectures providing tools to scale across CPU cores, CPUs, machines and

locations, i.e.

kdb+ and the infrastructure costs scale linearly with the business requirements.

30

When should you consider evaluating kdb+?

Are your data management costs exploding?

kdb+ offers the lowest TCO for large databases and data analytics.

Do you manage large amounts of time-ordered data (multiple terabytes or petabytes)?

kdb+ provides best in class time series solutions.

Is your business slowed down due to long time-to-market intervals on even small change requests?

kdb+ enables rapid development on large datasets with its integrated programming language.

Is your trading infrastructure getting more and more complex increasing your operational risk?

kdb+ helps understanding and monitoring your operations in real-time.

Do you lack essential business information flowing through your organization in fragmented streams?

kdb+ captures virtually any amounts of data and provides immediate access to the contained information.

Do you need to speed up your business, e.g. from daily risk views to continuous risk monitoring?

kdb+ seamlessly integrates real-time stream processing and database analytics, e.g. to enable real-time

business intelligence and real-time risk management.