What is your edge?
Every successful investor has an answer to this question before they enter any new position. Every successful trader has an idea about the expected return and possible risk in a given position, but also about where that risk and return comes from. Stock investors who use a value approach will point to the fundamental economic worth of a company as a reason to think that the company’s shares are mispriced. Growth and trend-following traders will point to the empirical history of momentum as a key driver of stock returns.
Volatility traders rely on a different source of edge: human psychology. A permanent feature of human psychology is that we experience more pain from a loss than we experience pleasure from an equally-sized gain. Prospect theory, the Nobel prize-winning work of psychologists Daniel Kahneman and Amos Tversky, was the first to demonstrate this tendency toward loss aversion as a widespread cognitive bias.
For instance, imagine that you and I each bet $500 on the outcome of a coin toss and you correctly call heads. After you have collected your winnings, since each of us has seen a change in our wealth of equal size, the effects on our sense of well-being should be symmetrical: I should feel exactly as bad after losing as you feel good after winning. But that’s not what usually happens with most of us: the happiness you feel from your winning bet will tend to be less intense than the pain I feel from having lost. In some cases, studies have found losses to be felt twice as strongly as wins.
Loss aversion in option pricing
This cognitive bias has widespread effects in economics and finance, and the fact that our human responses to returns are asymmetrical creates opportunity for investors who can act more reasonably. One place we can identify loss aversion in financial markets is in the pricing of options contracts.
One way to interpret the volatility implied by the price of an option contract is in relation to the recent volatility of the underlying asset. Since the market tends to discount, more or less, the available public information about a stock, we can take the price of a stock at any given time as reflecting all of that information. While markets are not perfectly efficient, they are also not wildly inefficient, and in fact one of the best predictors of a stock’s volatility in the future is its recent volatility in the past. So we should expect the implied volatility of an option contract to be similar, absent some other priced factors, to the recent historical volatility of the stock.
But that’s almost never what we find! Instead, options on most stocks and stock indexes tend to be priced significantly higher than what the trailing volatility of the underlying would suggest. For example, in June 2013, with the Dow Jones Industrial Average quoted at 13,544, the prior month of closing price returns exhibited an annualized volatility of about 7.3%. But options on the Dow index with about one month to expiration were priced not at 7%, but at 12%. The key insight about the effects of loss aversion on options markets is that investors who can overcome this cognitive bias and take advantage of the excessive fear of others can earn economically meaningful returns.
Is it possible that “overpriced” options are just a quirk of some assets and not others? Perhaps options traders misprice options on biotechnology stocks but not conservative utilities, or misprice volatile commodities like crude oil but not safer assets like Treasury bonds. However, as I discussed in Options and the Volatility Risk Premium, academics studying this phenomenon have identified it as a persistent feature of options on nearly every asset class in the world – including not just individual U.S. equities but also commodities, fixed income products, indexes, and international stocks. The fact that this risk premium is so widespread gives us reason to believe it is a feature of the actors in the market and not just the nature of the assets themselves.
Capturing this premium in an optimal way is the purpose of our core newsletter strategy.