The Sandpiper and Trading – Volatility Trading Part 1
Volatility and Trading: Why
As a trader do you find yourself sometimes looking at a price chart and wondering why a stock reversed at a specific level? Or possibly more often you may want to know where it will stop falling so you can buy it. If so, you are thinking about what the extreme is…“what is the farthest?”
You are thinking about volatility.
You are not alone. Every person and institution involved in traded markets thinks about volatility. The difference is you are not likely thinking about volatility the same way most of your competitors are.
The real point of this series of articles is to show how you can use volatility measurement to gain an advantage in your trading.
When I use the term ‘competitors’ I’m referring to participants in markets that determine price direction. The big money is what moves markets; investment banks, hedge funds, pension funds, large proprietary trading firms, private and public corporations hedging their risks.
Here’s the difference that really matters: The big money rarely measures prices. Most of their trading is based on volatility measurement.
Read on and I’ll explain why this is.
The Big Money
Institutional firms comprising of big money do most transactions in the form of options, futures and derivative contracts. They find it necessary to be highly specific about what they are trying to achieve, and outright ownership positions rarely accomplish that. However this also makes their trading very complex and opaque. No matter that we all see excessive claims of ‘transparency’ advertised, they try hard to make transactions confusing; mostly so they can charge clients larger fees.
Along came the year 2008. This market crash was specifically a liquidity crisis. The lack of liquidity was due to excessively leveraged positions of the derivatives contracts mentioned above.
When central banks were forced to bailout and backstop the world’s largest financial institutions, they did so. But not without future restrictions. It has taken some years to implement, but the iron-clad rules are currently implemented and being enforced.
For instance in the US the Federal Reserve requires that a multitude of risk calculations be performed daily and reported to them. These calculations are complex risk models and directly affect the amount of cash a bank must hold as capital reserves; which directly affect the amount of money to be made by bank traders and executives.
Always follow the money right?
Why Care About the Whims of the Central Banks?
The principal input into any risk model is volatility. More specifically it is Implied Volatility.
In order to see a very real and practical example of this, let’s look at how a stock option is priced. Below is an illustration of inputs to the common Black Scholes option pricing model.
The model above calculates theoretical option price. It takes seven inputs. The input with the most effect on the price is Historical Volatility.
In the real world the pricing model is different in that it swaps the position of volatility with price.
As you can see above, once someone has actually paid money for the option the model is changed to produce a volatility value called Implied Volatility. Specifically the ‘historical volatility’ value at upper right is now the option price. The result of the model then produces a volatility value. This implied volatility value is a direct result of what the market is telling us.
Demand for purchasing derivatives contracts (options, futures, OTC derivatives) moves prices of those contracts. As you can see above, the price of those contracts changes the implied volatility. The implied volatility data is what institutions use to make decisions.
Last and most importantly: Commitments made via derivatives contracts forces buying and selling to fulfill the contract terms.
Big Money Pays for Volatility Data
I spent a year working on a consulting contract at a major US bank. Because of my experience in volatility data analysis, I was part of a team that evaluated large sets of implied volatility data for use in risk models. This was a very high profile project because it directly affected the paychecks of some important people in the bank’s upper hierarchy.
These institutions buy daily feeds of overnight implied volatility for literally thousands of global derivatives contracts. Some of the sources were Thomson Reuters, Bloomberg, Ivolatility, MarkIT, etc. I was astonished to learn that the coming year’s budget for this data was growing over 50% to north of three million dollars annually.
Volatility measure is central to all of their risk and trading decisions. For instance if volatility increases, then risk increases and they must decrease the institution’s positions. That means selling. They do this because they have no choice. The central bank regulators require it. Negotiating is not an option for them.
This is why volatility measurement is the single most important technical component for any form of financial trading.
Volatility is not a new phenomenon. It’s all around us. What follows is an excerpt from my book Volatility –Based Technical Analysis.
Volatility In the Natural World: The Sandpiper
Do you know what a sandpiper and successful trader have in common? This is not a jest, or a trick question. Before Part 2 of this book is done, you will understand their common ground.
The lowly little sanderling, a type of beach-dwelling sandpiper, stands just four to eight inches tall. This small bird eats by dodging the surf and digging up tiny marine life at the water’s edge. Even a very small wave is much taller than the sanderling, and this little guy can only see the next wave in front of him.
Its challenge is to gauge when to run out into the dangerous crashing waves, and do it without hesitation. The water will not pause for the convenience of a simple little bird. The waves come in regularly, then recede to reveal all the little aquatic invertebrates on which it feeds. The best food is the furthest out, nearest to the greatest wave height. Timing is everything!
Watch a sanderling a bit longer and you notice something even more incredible than their innate sense of wave timing. They know exactly how far to go into the surf’s receding wave to dig for the best food, then dart back towards the shore just ahead of the next incoming wall of bird bone-breaking water. Think about it. They know the farthest point which can be reached before they need to turn around and come back, all the while achieving a successful bite of food necessary for sustaining another ten minutes of life on the beach. This bird does this over and over, all day long.
Does this challenge sound familiar? When to move? How far to go? When to get out? These are all critical choices we make each time we trade. If you could make these decisions as flawlessly while trading as the sandpiper does while eating, wouldn’t riches be just around the next bend? But how does the bird do it?
Each wave comes in at different heights with varying levels of stored energy which propel it towards the beach. But there is much more to consider if you want to get really scientific about it. After a succession of incoming waves, there is a surplus of water rushing back to sea and thus opposing the effectiveness of the next wave. Our little feathered friend on the beach is so good at gauging all of these factors that he is actually measuring volatility.
Yes, somewhere in that tiny brain is a volatility computer. It’s measuring all of the factors mentioned, plus more that we probably don’t comprehend. The sandpiper’s computational ability is able to calculate, based on what has been happening all around him, just where the next wave will break. He observes the past and predicts the future very accurately. Perhaps most importantly, the bird knows the best food is to be had at the extremes. That is to say, the best eating is when the sea has receded the farthest.
This last concept is one most technical traders overlook very often. The middle ground is safe but there is not much there to eat. It’s out near the edge of perceived danger where most of the money can be made. Maybe Wall Street should be recruiting sandpipers instead of quantitative analysts and traders!
Volatility and TA
Instead of looking at how far price moved, technical analysis calculations should consider the movements of volatility.
Indicators and oscillators typically measure movement of price, and sometimes volume. While this is helpful, it only gives a small part of what your competitors (big money) are using in their trading decisions. Remember the big money is driving supply and demand with volatility data fed into trading algorithms and risk models.
Wouldn’t it be more appropriate that an oscillator calculation measures the movement of the instrument’s volatility? Yes it is, and that’s the secret sauce.
Even more important is the proximity of price to previous volatility extremes. Remember that out near those extremes is where the sandpiper finds the best food, and where we find the best trading decisions. To do this we must first convert volatility into price. Specifically for technical analysts, we need volatility to be plotted as a price level on a chart.
In my next article I’m going show you how:
- Applying volatility measurement to technical indicators gives you an advantage.
- Plotting volatility extremes on the price chart tells you where the ‘big money’ support and resistance is.
Read Part 2 | Redefining Support & Resistance
Kirk Northington, CMT
Co-founder of Northington Dahlberg Research, LLC
Kirk is a co-founder of Northington Dahlberg Research, LLC and is a quantitative technical analyst. He is a member of the Market Technicians Association, and is a Chartered Market Technician. Kirk is a frequently requested speaker on the topic of volatility trading and volatility based market analysis.
He is the author of Volatility-Based Technical Analysis: Strategies for Trading the Invisible, Wiley Trading Series, John Wiley & Sons Publishers; in which he pioneered new concepts in technical and quantitative analysis. Kirk has a BS degree from Nicholls State University, in Thibodaux, Louisiana. He has extensive experience in institutional market risk, process control system design, and software engineering.