Contents

Cover

Half Title page

Title page

Copyright page

List of Figures, Tables and Big Pictures

Preface

Chapter 1: Introduction

1.1 What is a Trading Platform?

1.2 Quants and Quantitative Developers

1.3 Need for Speed

1.4 Implementation Options

1.5 Current Trends

1.6 Technical and Business Aspects of Platform Design

1.7 Importance of Processes

1.8 Objectives and Organization

Quiz

Chapter 2: Overview of Banking

2.1 The Offices

2.2 Front Office

2.3 Middle Office

2.4 Back Office

2.5 Supporting Units

2.6 Summary

Quiz

Chapter 3: Trade Life Cycle

3.1 Pre-Trade Activities

3.2 Inception

3.3 Validation

3.4 Regular Processing

3.5 Life-Cycle Events

3.6 Termination and Settlement

3.7 Post-Trade Activities

3.8 Summary

Quiz

Chapter 4: Trade Perspectives

4.1 Trade-Centric View

4.2 Model-Centric View

4.3 Product-Centric View

4.4 Asset-Class View

4.5 Queues and Status Flags

4.6 Aggregate Views

4.7 Bottom-Line View

4.8 Other Perspectives

4.9 Summary

Quiz

Chapter 5: Programming Languages – Basics and Choices

5.1 Language Choice

5.2 Basics of Computing

5.3 Object-Oriented Languages

5.4 Functional Programming

5.5 Summary

Quiz

Chapter 6: Trading Platform Design

6.1 General Design Considerations

6.2 Architecture Components

6.3 Example architecture

6.4 Advanced architecture

6.5 Summary

Quiz

Chapter 7: Computing Patterns for Trading

7.1 Façade Pattern

7.2 Visitor Pattern

7.3 Singleton Pattern

7.4 Factory Patterns

7.5 Component Object Model

7.6 Summary

Quiz

Chapter 8: Flexible Derivatives Pricing Tool

8.1 Design Scope

8.2 Design Goals and Features

8.3 User Interface

8.4 Summary

Quiz

Chapter 9: Pricing Tool to Trading Platform

9.1 Pricing Tool: Internals

9.2 Future Enhancements

9.3 Summary

Quiz

Chapter 10: Summing Up

10.1 Epilogue

10.2 Further Reading

Appendix

A.1 CD Contents

A.2 Historical Perspective

Glossary of Terms

Index

Principles of Quantitative Development

For other titles in the Wiley Finance series
please see www.wiley.com/finance

This edition first published 2010
© 2010 Manoj Thulasidas

Registered office
John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England

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Library of Congress Cataloging-in-Publication Data

Thulasidas, Manoj.
Principles of quantitative development / Manoj Thulasidas.
p. cm.
ISBN 978-0-470-74570-0 (hardback), ISBN 978-0-470-66737-8 (ebk),
ISBN 978-0-470-97151-2 (ebk), ISBN 978-0-470-97152-9 (ebk)
1. Speculation. 2. Risk management. I. Title.
HG6015.T48   2010
332.10681dc22

2010005779

A catalogue record for this book is available from the British Library.

ISBN 978-0-470-74570-0

List of Figures, Tables and Big Pictures

Figures

2.1 High-level overview of the banking structure

2.2 The structure of a typical front office

2.3 The structure of a typical middle office

3.1 Pre-trade activities of the trade life cycle

3.2 Trade inception activities

3.3 Trade validation process

3.4 Regular processes during the life of a trade

3.5 Trade termination activities

4.1 The model-centric view of trading activities

4.2 The product-centric view as in the trading platform

4.3 A sample scenario analysis of one trade

4.4 Middle-office process queues

5.1 Flowchart of a simple portfolio processing

5.2 Binomial tree pricing model

6.1 Settlement workflow through a vended trading system

6.2 Grid-enabled trading platform

6.3 Information flow in credit replication

6.4 An example architecture of a trading platform

6.5 A high-level schema of a professional architecture of a trading platform

7.1 Façade pattern in action

7.2 The UML diagram for a façade pattern

7.3 Visitor pattern in a trading platform

7.4 The factory method pattern

7.5 The abstract factory pattern

8.1 User interaction with the pricing tool

8.2 Main control interface of the pricing tool

8.3 Context menus in the pricing tool

8.4 Product definition in the pricing tool

8.5 Model definition in the pricing tool

8.6 Code generation in the pricing tool

8.7 Help on the code generation in the interface

8.8 Viewing Vector, Curve and Matrix

8.9 Plotting complex data types

8.10 Pricing interface of the attached program

8.11 Plotting interface (two-dimensional plots)

8.12 Plotting interface (three-dimensional plots)

8.13 Plotting interface (parametric plots)

8.14 Finite difference Greeks computation interface of the pricing tool

9.1 Use case depiction of the pricing tool

9.2 UML class hierarchy diagram of the pricing tool

9.3 UML diagrams of the product parameter and model output classes

9.4 Mapping of use cases to user interface modules in the pricing tool

9.5 Main user interface of the pricing tool and its active regions

9.6 Product editing interface showing its dynamic nature

Tables

4.1 Example of a trade scenario risk analysis

6.1 Components that make up the market data set

8.1 Example of Vector and DateVec data types

8.2 Flexible date input formats

Big Pictures

1.1 Conflicts by design

2.1 Operational risk in in-house platforms

2.2 Riskreward seesaw

3.1 Collateral versus credit lines

3.2 Workflow engine

3.3 To hedge or not to hedge

3.4 Exchange-traded versus over-the-counter

4.1 Back-to-back trades

4.2 Pluses and minuses of VaR

4.3 Big Picture and narrow perspectives

5.1 Zeros and ones

5.2 Magic of object-oriented languages

5.3 Back to basics

6.1 Straight-through processing

6.2 Throwing the baby out with the bath water 105

6.3 Access control

6.4 Untenable supports

7.1 Paradigms in computing

7.2 Paradigms versus patterns

9.1 Market data quantization errors

10.1 Effect of modelling on models

10.2 Group dynamics

10.3 Philosophy of money

A.1 Software nightmares

Preface

Times have changed. Not long ago, all that you needed for a successful career in the financial industry were good people skills, and modest arithmetic and accounting abilities. The derivatives market requiring advanced quantitative (or computing) skills used to be miniscule. Now, with the explosive growth of derivatives, those simple times are gone for good. Derivatives and structures have taken the centre stage in recent years.

The world of trading and structuring today relies heavily on mathematical models and sophisticated computational techniques. In order to stay competitive, financial institutions need quantitative talent now. They cannot expect their bankers, specialized professionals themselves, to be adept in other professional domains like computer science and applied mathematics as well. Banks, therefore, import talent from other unorthodox domains. They find a helping hand among physicists and mathematicians who can model and price complex derivatives. They also employ a small army of computer scientists and programmers to deploy the pricing models in systems and platforms that help their traders make money.

This development has created a knowledge gap between the conventional banking staff and the newcomers. Bankers and regulators do not fully understand what the quantitative professionals are up to. The professionals themselves have very little appreciation for how the business side of the financial institution works. Bridging this gap is one of my objectives in embarking on this work.

Coming from a quantitative professional’s perspective, I may have tilted this book a bit in our favour. That bias notwithstanding, I feel that it is important for ‘quants’ (the mathematicians who develop pricing models) and ‘quantitative developers’ (computer scientists who deploy the models) to understand and appreciate the business aspects of trading so that our efforts may be as fruitful as possible. Once, one of our top traders pointed out that even the most intelligent pricing model is useless unless it can be deployed effectively and in a timely manner. Effective deployment crucially depends on our appreciation of the ‘Big Picture’.

Most quantitative professionals, especially at junior levels, are indifferent to the Big Picture. They think of it as a distraction from their real work of taming stochastic differential equations and writing programs. Through this book, I hope to change their mindset to some degree.

However, the scope of the book is bigger than the world of quantitative professionals. There is a flip side to our reluctance fully to appreciate the rest of the value chain. It is the inability of other business groups to realize what is possible and desirable in the quant world or in a trading system. Every business group in a bank is so specialized and requires skills and focus so specific to their duties that they all end up being distinct silos of knowledge. The professionals in other business units, especially in the middle office, will benefit from this book, in being able to formulate their requirements within the realms of feasibility. More than that, they will also gain an insight into how the quantitative work is done and how a mathematical model finally becomes profit-generating systems.

Another group of professionals who are supposed to know everything this book has to say are business analysts whose job it is to interface business needs to systems development. They usually work for technology consultancy firms and talk to their banking clients in order to come up with solutions specifically targeted to the needs of trading, financial computing and risk management. However, their existence is not confined to the technology side of the value chain. Some business associates and analysts work in financial institutions, translating internal business requirements to quantitative development objectives and rolling out new products and processes. These professionals will find this book invaluable as well.

When we change our narrow, albeit efficient, focus on the work at hand to an understanding of our role and value in the organization, we will be able to leverage off each other’s work and enhance the overall productivity by leaps and bounds. This big picture view is especially relevant in the current climate (2008–2009) of financial turmoil and in our efforts to avoid or mitigate similar crises in the future. Of the many causes behind the turmoil that we see clearly with the benefit of hindsight, one that this book addresses is the general lack of a high-level understanding of the whole trading process. For instance, most quants, who are often blamed for the hole in which we find ourselves, do not fully appreciate the processes and methodology in managing the credit and market risks, or the interplay between them. Most professionals who manage credit and market risks do not quite understand the risk profiles straddling these two classes of risks that they manage independently of each other. Yet, this deficiency in understanding is neither deliberate nor premeditated. It is a natural consequence of the silo-like concentration and focus, which this book aims to change. In setting its sight at this holistic picture, this book indeed starts with an ambitious goal, but a timely one.

Manoj Thulasidas
Singapore

Chapter 1

Introduction

Purpose-built trading platforms have become indispensable tools for exotic trading in most modern banks. One reason for their success and prevalence is that exotics and structured products business is highly lucrative to a financial institution. One prerequisite for success in exotic business is the ability to price the products. For pricing model development and hedging or risk analysis, the financial institution can resort to its mathematicians and the quantitative models they develop.

Another vitally important prerequisite is the capability to assess and manage the associated risks. The risks arising from market movements are related to pricing and are managed through sensitivity analyses. However, in order to manage credit and operational risks, especially when the business volume grows, banks need robust trading platforms supporting and augmenting the mathematical effort.

A carefully designed and deployed trading platform can easily recuperate its development costs within a few short months of going live, and turn into an important profit centre for banks. Once they started appreciating the necessity and profitability of an in-house trading system, banks and other financial institutions began to invest resources into developing in-house trading systems.

Resource allocation attracts professionals with highly specialized skills to move into the field. However, their skills, at times, work against them. They think of quantitative development as a purely technical endeavour. Although, at its heart, trading platform development is a technical challenge, it is the myriad of business processes around it that will eventually make or break its viability. The proverbial devil is really in the details.

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!