Positional Option Trading (Wiley Trading)

by Euan Sinclair

  Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

  Published simultaneously in Canada.

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  Library of Congress Cataloging-in-Publication Data

  Names: Sinclair, Euan, 1969- author.

  Title: Positional option trading : an advanced guide / Euan Sinclair.

  Description: Hoboken, New Jersey : Wiley, [2020] | Series: Wiley trading series

  | Includes bibliographical references and index.

  Identifiers: LCCN 2020008849 (print) | LCCN 2020008850 (ebook) | ISBN

  9781119583516 (hardback) | ISBN 9781119583523 (adobe pdf) | ISBN

  9781119583530 (epub)

  15

  Subjects: LCSH: Options (Finance) | Financial futures.

  Classification: LCC HG6024.A3 S56225 2020 (print) | LCC HG6024.A3 (ebook)

  | DDC 332.64/53—dc23

  LC record available at https://lccn.loc.gov/2020008849

  LC ebook record available at https://lccn.loc.gov/2020008850

  Cover Design: Wiley

  Cover Image: © blackred/Getty Images

  16

  INTRODUCTION

  You know nothing, Jon Snow.

  —Ygritte in A Storm of Swords by George R. R. Martin.

  He is not the only one.

  We are not in a time where reason is valued. In economics, the

  idea that marginal tax cuts pay for themselves is still advanced,

  even though all evidence says they don't.

  Forty percent of Americans do not believe in evolution. Forty-five

  percent believe in ghosts. These beliefs are not based on any

  evidence. They are manifestations of another philosophy, whether

  it is economic, religious, or sociological. Usually these opinions

  reveal more about what people want to be true rather than any

  facts that they know. And many people know few facts anyway.

  Evidence is seen as irrelevant and arguments are won by those

  who shout loudest and have the best media skills.

  The idea that opinions are as valid as facts also affects trading and

  investing. Many investors rely on methods that are either

  unproven or even proven to be ineffective. The few of these

  investors who keep records will see that they are failing but rely on

  cognitive dissonance to continue to believe in their theories of how

  the markets behave. One would think that losing money would

  prompt reexamination, but the persistence of losers is remarkable.

  And even when some people give up or are forced out, there is

  always new money and new participants to replace the old.

  The only way to learn about anything is through the scientific

  method. This is an iterative procedure where theory is modified by

  evidence. Without evidence we are just in the realm of opinion.

  Most of what I present here is backed by evidence. There are also

  some opinions. My justification for this is that experience is also a

  real thing. But I'm no less prone to self-delusion than anyone else,

  so feel free to pay less attention to these ideas.

  Trading is fundamentally an exercise in managing ignorance. Our

  ability to judge whether a situation presents a good opportunity

  will always be based on only a simplified view of the world, and it

  is impossible to know the effects of the simplification. Our pricing

  17

  model will be similarly compromised. It will be a simplification

  and possibly a very unrealistic simplification. Finally, the

  parameters the models need will have estimation errors and we

  generally won't know how large these are.

  It is impossible to understand the world if you insist on thinking

  in absolute terms. The world is not black and white. Everything

  has shades of gray. You won't learn much from this book is you

  aren't comfortable with this.

  This is clear for “risk.” Everyone has a different risk tolerance,

  whether this is personal or imposed by management or investors.

  But more important, risk is multidimensional. We are comfortable

  with this idea in some areas of life. Imagine you have a choice of

  going on vacation to either Costa Rica or Paris. Both are nice

  places and any given person could reasonably choose either one.

  But Paris has no beaches and Costa Rica doesn't have the

  Pompidou Centre. There is no one correct choice in this situation.

  And that is also the case in most investing decisions.

  Many of the ideas I write about can be extrapolated to a ludicrous

  level. But if you do, don't blame me or credit me with the resulting

  conclusions.

  In no particular order here are some facts that are often

  misrepresented:

  There is usually a variance premium. This does not mean there

  is always a variance premium.

  There is usually a variance premium. This does not mean you

  should always be short volatility.

  Short volatility can be risky. This does not mean that short

  volatility has to have unlimited risk.

  Some theories (e.g., GARCH, BSM, EMH, returns are normally

  distributed) have limitations. This
does not mean the theories

  are stupid or useless.

  The Kelly criterion maximizes expected growth rate. This does

  not mean you should always invest according to it.

  If what I write is unclear or incorrect, that is a problem of my

  making. But if you choose to ignore nuance, that is your issue.

  18

  Trading as a Process

  I have made no attempt here to write a comprehensive option

  trading book. I don't cover the definitions and specifications of

  various types of options. There are no derivations of option pricing

  models. I expect the reader to know about the common option

  structures such as straddles, spreads, and strangles. Many books

  cover these topics (e.g., Sinclair, 2010). A very brief summary of

  the theory of volatility trading is provided in Chapter One, but this

  is not a book for beginners.

  This is a book for experienced traders who want the benefits of

  including options in their strategies and portfolios but who are

  unwilling or unable to perform high-frequency, low-cost dynamic

  hedging. Again, there are many books on this type of positional

  option trading, but none are theoretically rigorous, and most

  ignore the most important part of trading anything: having an

  edge.

  One of the things that distinguishes professionals from amateurs

  in any field is that professionals use a consistent process. Trading

  should be a process: find a situation with edge, structure a trade,

  then control the risk. This book documents these steps.

  The book's first section explores how to find trades with positive

  expectation.

  In Chapter Two, we look at the efficient market hypothesis and

  show that the idea leaves plenty of room for the discovery of

  profitable strategies. This insight lets us categorize these

  “anomalies” as either inefficiencies or risk premia. These will

  behave differently and should be traded differently. Next, we

  briefly review how behavioral psychology can help us and also its

  limitations. We examine two popular methodologies for finding

  edges: technical analysis and fundamental analysis.

  Chapter Three looks at the general problem of forecasting. No

  matter what they say, every successful trader forecasts. The

  forecast may not be one that predicts a particular point value, but

  probabilistic forecasting is still forecasting. We introduce a

  classification of forecasting methods. Forecasts are either model

  based, relying on a generally applicable model, or situational,

  taking advantage of what happens in specific events. We very

  briefly look at predicting volatility with time-series models before

  moving on to our focus: finding specific situations that have edge.

  19

  The most important empirical fact that an option trader needs to know is that implied volatility is usually overpriced. This

  phenomenon is called the variance premium (or the volatility

  premium). Chapter Four summarizes the variance premium in

  indices, stocks, commodities, volatility indices, and bonds. We

  also present reasons for its existence.

  Having established the primacy of the variance premium, Chapter

  Five gives eleven specific phenomena that can be profitably

  traded. The observation is summarized, the evidence and reasons

  for the effect are given, and a structure for trading the idea is

  suggested.

  The second section examines the distributional properties of some

  option structures that can be used to monetize the edge we have

  found. We need to have an idea of what to expect. It is quite

  possible to be right with our volatility forecasts and still lose

  money. When we hedge, we become exposed to path dependency

  of the underlying. It matters if a stock move occurs close to the

  strike when we have gamma or away from a strike when we have

  none. If we don't hedge, we are exposed to only the terminal stock

  price, but we can still successfully forecast volatility and lose

  because of an unanticipated drift. Or we can successfully forecast

  the return and lose because of unanticipated volatility.

  Chapter Six discusses volatility trading structures. We look at the

  P/L distributions of straddles, strangles, butterflies, and condors,

  and how to choose strikes and expirations.

  In Chapter Seven we look at trading options directionally. First,

  we extend the BSM model to incorporate our views on both the

  volatility and return of the underlying. This enables us to

  consistently compare strikes on the basis of a number of risk

  measures, including average return, probability of profit, and the

  generalized Sharpe ratio. Chapter Eight examines the P/L

  distributions of common directional option structures.

  The final section is about risk. Good risk control can't make

  money. Trading first needs edge. However, bad risk management

  will lead to losses.

  Chapter Nine discusses trade sizing, specifically the Kelly

  criterion. The standard formulation is extended to allow for

  parameter estimation uncertainty, skewness of returns, and the

  incorporation of a stop level in the account.

  20

  The most dangerous risks are not related to price movement. The

  most dangerous risks are in the realm of the unknowable.

  Obviously, it is impossible to predict these, but Chapter Ten

  explores some historical examples. We don't know when these will

  happen again, but it is certain that they will. There is no excuse for

  blowing up due to repeat of a historical event.

  It is inevitable that you will be wrong at times. The most

  dangerous thing is to forget this.

  Summary

  Find a robust source of edge that is backed by empirical

  evidence and convincing reasons for its existence.

  Choose the appropriate option structure to monetize the edge.

  Size the position appropriately.

  Always be aware of how much you don't know.

  21

  CHAPTER 1

  Options: A Summary

  Option Pricing Models

  Since all models are wrong the scientist must be alert to what

  is importantly wrong. It is inappropriate to be concerned

  about mice when there are tigers abroad.

  —Box (1976)

  Some models are wrong in a trivial way. They clearly don't agree

  with real financial markets. For example, an option valuation

  model that included the return of the underlying as a pricing input

  is trivially wrong. This can be deduced from put-call parity.

  Imagine a stock that has a positive return. Naively this will raise

  the value of calls and lower the value of puts. But put-call parity

  means that if calls increase, so do the values of the puts. Including

  drift leads to a contradiction. That idea is trivially wrong.

  Every scientific model contains simplifying assumptions. There

  actually isn't anything intrinsically wrong with this. There are

  many reasons why this is the case, because there are many types of

  scientific models. Scientists use simplified models that they know

  are wrong for several reasons.

  A reason for using a wrong th
eory would be because the simple

  (but wrong) theory is all that is needed. Classical mechanics is still

  widely used in science even though we now know it is wrong

  (quantum mechanics is needed for small things and relativity is

  needed for large or fast things). An example from finance is

  assuming normally distributed returns. It is doubtful anyone ever

  thought returns were normal. Traders have long known about

  extreme price moves and Osborne (1959), Mandelbrot (1963), and

  others studied the non-normal distribution of returns from the

  1950s. (Mandelbrot cites the work of Mitchell [1915], Olivier

  [1926], and Mills [1927], although this research was not well known.) The main reason early finance theorists assumed

  normality was because it made the equations tractable.

  22

  Sometimes scientists might reason through a stretched analogy.

  For example, Einstein started his theory of the heat capacity of a

  crystal by first assuming the crystal was an ideal gas. He knew that

  this was obviously not the case. But he thought that the idea might

  lead to something useful. He had to start somewhere, even if he

  knew it was the wrong place. This model was metaphorical. A

  metaphorical model does not attempt to describe reality and need

  not rely on plausible assumptions. Instead, it aims to illustrate a

  non-trivial mechanism, which lies outside the model.

  Other models aim to mathematically describe the main features of

  an observation without necessarily understanding its deeper

  origin. The GARCH family of volatility models are

  phenomenological, and don't tell us why the GARCH effects exist.

  Because these models are designed to describe particular features,

  there will be many other things they totally ignore. For example, a

  GARCH process has nothing to say about the formation of the bid-

  ask spread. The GARCH model is limited, but not wrong.

  The most ambitious models attempt to describe reality as it truly

  is. For example, the physicists who invented the idea that an atom

  was a nucleus around which electrons orbited thought this was

  actually what atoms were like. But they still had to make

  simplifying assumptions. For example, when formulating the

  theory, they had to assume that atoms were not subject to gravity.