Preface: Models at Work
I am not a Quant.
I always wanted to be one but it took a single meeting with Goldman’s Firmwide risk team in London to clear any delusions I may have harbored. All remaining reservations were removed in the single PhD elective in Finance that I took with Maria Vassalou at Columbia. Despite Maria’s kindness and dedication it was obvious in April 1999 that I was just an ordinary mortal and not a Quant.
In 1999, the realization wasn’t heart breaking. If one couldn’t live in the exotic world of high finance, the less exalted levels in the banking and trading world offered enough to keep you engaged and happy as a professional.
But that was then. The last six years have left no doubt that the impact of quantitative models travels beyond the inner circle of the more mathematically inclined amongst us. Imagine being a board member at a large bank or a financial institution; imagine the board meeting dossier filled with numbers and graphs that come with no cheat sheet or Rosetta Stone and then envisage the need for you, as a board member, to initial and certify it all with your name and reputation.
The challenge is, that armed without a PhD in the subject or years of experience on the trading desk, most of us are hopelessly lost when it comes to dissecting models at work. Even if one wants to learn there is little material available in a language that ordinary mortals can understand. The foundation of the field of risk management is based on well aged sciences of mathematics and statistics. It is but natural that books heavy on mathematical and statistical treatment of the subject are common and abundant, while those relying on simple layperson language and do it yourself modeling in EXCEL are not.
Ideally, a book should introduce a framework for managing risk and follow it through with a number of real world illustrative examples with numbers and data. If you are interested, it should allow you to build and test simple models that you can then use to strengthen your understanding of the conditions under which models can breakdown or predict where things can go wrong. A great text would educate you enough to not only ask the right questions but also evaluate and digest the answers provided.
Over the last decade, as we put together teaching notes for participants in our workshops for bankers, traders, treasurers and executive MBA students, we found that the above design on teaching risk management worked well. The challenge most professionals face is not with theoretical derivations but practical applications and translation into the real world.
What is this book about?
The book uses four sections to present frameworks, tools, cases and context around risk assessment and management. Here is a quick review of each section:
Section I – What is Risk assessment?
A framework for thinking about risk assessment begins with a desire to evaluate complexity in analysis versus complexity in models. The chapters in this section start with an introduction to dealing with volatility, measuring risk using Value at Risk, managing risk using target accounts and with two short chapters on risk policy and risk regulation.
Section II – Monte Carlo Simulation.
A multi-chapter crash course in Monte Carlo Simulation using a simplified approach in EXCEL. We begin with simple simulation models for generating prices for equities, currencies and commodities. The simple models are then used to build a second layer that evaluates the impact of changes in simulated prices on business and performance metrics. You can’t assess risk, if you can’t measure it.
Building up on the complexity in analysis themes, while the models used are simple, the objective is to understand relationships that drive the risk distribution. While results are always qualified, model builders who don’t fall in love with their models, end up with a better understanding of the risk they are trying to manage.
Section III – Dissecting Commodity Models.
Armed with frameworks and simple tools, the third section presents an opportunity to apply them. Rather than build models we focus on identifying relationships, drivers and data across commodity markets. Four cases are presented from the point of view of a research analyst. They include:
- Rolling volatility & correlations in commodity markets
- Drivers of crude oil & gold pricing
- The relationship between crude oil price shocks & inflation rates in emerging markets
- Real interest rates in India and Pakistan
To get the most out of the frameworks and tools presented in the first two sections, each case can be used as the foundation of a more detailed modeling exercise. For example in the two cases that cover drivers behind crude oil and gold price changes we identify price drivers that are left as black boxes in the case. If you are interested, there is enough data in the book for you to replace the black boxes with your own models.
Section IV – Basics of Derivative Pricing.
A text on risk management cannot be complete without a review of the product universe, pricing and valuation models. While a more detailed treatment is available in Hull, Wilmott, Tuckman & Fabozzi, we attempt a short introduction to the product and pricing world to ensure the book remains self contained for our audience. The decision to add the section was taken once we included the section on Monte Carlo Simulation, since many of the simulations exercises would remain incomplete without product and pricing context
Who is this book for?
If you are looking for detailed mathematical derivations, differential equations or easy answers, you will be disappointed.
The book is about building intuition around risk and using simple tools in EXCEL to test that intuition against the real world and occasionally with economic drivers. Taleb calls it “playing with the generator function”. My mentors in the field have called it the “Build, Test, Dissect, Decode” mode of learning. Till you figure out how to break it, you won’t really learn how it works.
The book shows you how to build some models, shares the framework that you can use to test and stretch the same and in some instances gives you the data to extend them. But it stops short of putting it all together. It will show you the way and partially unlock the door, but you have to make the effort to open it and walk inside.
This book is for you if you ever wondered about risk, risk assessment and risk management and their usage in the real world; if you wanted to model risk but felt awed by the terminology; if you like to question assumptions and test them in EXCEL; if your board is a “What if” board and you want to put a better process around that one troubling question; and if you wanted to be a quant, but like me, are not.
Happy reading. Go forth, build, test and learn.
30 October 2013