Euan Sinclair - Positional Option Trading

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.

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.

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.

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.

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.

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.