Positional Option Trading

An Advanced Guide

Euan Sinclair

• Publisher: Wiley
• Year: 2020
• Print ISBN: 9781119583516
• eBook ISBN: 9781119583530
• Edition: 1
• Topic: Business
• Subtopic: Investments & Securities
• Index: Business

A detailed, one-stop guide for experienced options traders

Positional Option Trading: An Advanced Guide is a rigorous, professional-level guide on sophisticated techniques from professional trader and quantitative analyst Euan Sinclair. The author has over two decades of high-level option trading experience. He has written this book specifically for professional options traders who have outgrown more basic trading techniques and are searching for in-depth information suitable for advanced trading.

Custom-tailored to respond to the volatile option trading environment, this expert guide stresses the importance of finding a valid edge in situations where risk is usually overwhelmed by uncertainty and unknowability. Using examples of edges such as the volatility premium, term-structure premia and earnings effects, the author shows how to find valid trading ideas and details the decision process for choosing an option structure that best exploits the advantage.

Advanced topics include a quantitative approach for directionally trading options, the robustness of the Black Scholes Merton model, trade sizing for option portfolios, robust risk management and more. This book:

• Provides advanced trading techniques for experienced professional traders
• Addresses the need for in-depth, quantitative information that more general, intro-level options trading books do not provide
• Helps readers to master their craft and improve their performance
• Includes advanced risk management methods in option trading

No matter the market conditions, Positional Option Trading: An Advanced Guide is an important resource for any professional or advanced options trader.

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 theory 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.

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. And, in only trivial situations could the equations be analytically solved. The Black-Scholes-Merton (BSM) model was meant to be of this type.

But it isn't used that way at all.

The inventors of the model envisaged that the model would be used to find a fair value for options. Traders would input the underlying price, strike, interest rate, expiration date, and volatility and the model would tell them what the option was worth. The problem was that the volatility input needed to be the volatility over the life of the option, an unknown parameter. Although it was possible for a trader to make a forecast of future volatility, the rest of the market could and did make its own forecast. The market's option price was based on this aggregated estimate. This is the implied volatility, which became the fundamental parameter. Traders largely didn't think of the model as a predictive valuation tool but just as an arbitrage-free way to convert the quickly changing option prices into a slowly changing parameter: implied volatility. For most traders, BSM is not a predictive model; it is just a simplifying tool.

This isn't to say that BSM can't be used as a pricing model to get a fair value. It absolutely can. But even traders who do this will think in volatility terms. They will compare the implied volatility to their forecast volatility, rather than use the forecast volatility to price the option and compare it to the market value. By using the model backwards, these traders still benefit from the way BSM converts the option prices into a slowly varying parameter.

We need to examine the effects of the model assumptions in light of how the model is used. Although the assumptions make the model less realistic, this isn't important. The model wasn't used because it was realistic; it was used because it was useful.

Obviously, it is possible to trade options without any valuation model. This is what most directional option traders do. We can also trade volatility without a model. Traders might sell a straddle because they think the underlying will expire closer to the strike than the ...

Table of contents

• COVER
• TABLE OF CONTENTS
• INTRODUCTION
• CHAPTER 1: Options
• CHAPTER 2: The Efficient Market Hypothesis and Its Limitations
• CHAPTER 3: Forecasting Volatility
• CHAPTER 4: The Variance Premium
• CHAPTER 5: Finding Trades with Positive Expected Value
• CHAPTER 6: Volatility Positions
• CHAPTER 7: Directional Option Trading
• CHAPTER 8: Directional Option Strategy Selection
• CHAPTER 9: Trade Sizing
• CHAPTER 10: Meta Risks
• CONCLUSION
• APPENDIX 1: Traders' Adjustments to the BSM Assumptions
• APPENDIX 2: Statistical Rules of Thumb
• APPENDIX 3: Execution
• REFERENCES
• INDEX
• END USER LICENSE AGREEMENT