Risk Management using the Percentage Volatility Model

Jan. 01, 2012 9:32 AM ET
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Contributor Since 2011

Andras aka tj is an ex-corporate "slave", gambler and speculator. Born and raised in Budapest, Hungary he had come to the US in 1979, at age 21 with nothing but a clothes on his back. Once an aspiring poker player, he always had affinity to Wall Street, speculation and risk management. He has written and published four different books on finance, risk management and Forex trading. In the 1980's he had worked as a computer programmer, a Stock broker, later a futures broker and finally as a floor trader in Chicago. Later in the 90's he moved to New York City where he had learned the find art of equities trading from Bob Kanter. A computer expert by training he worked on and off as an independent computer consultant for 17-years, his clients included Schwab, Morgan Stanley, Robertson Stevens, Microsoft, IBM, HP, Sybase and other Fortune 500 companies. He has tried every forms of gambling from Poker to the Stock and Commodities markets. He now writes and manages his and his wife's 401K and IRA's in Woodland California.

Why do some people succeed in the markets, while others go bankrupt? 
Some possible clues can be found when reviewing the psychological research within the behavioral finance. Let us consider an example: investors adjust slowly and as a possible effect, they do not see when the bull-market turns into a bear-market. This leads them to hold their positions longer then expected. Why does this happen? We will discuss this in the coming sections and the implications in details in the chapters to follow.

Getting back to the psychological research within the behavioral finance, it has been noticed that when we as humans make decisions under uncertainty, our choices are influenced by the situation rather than the absolute value of the result. When we perceive the situation as a loosing scenario, we tend to be risk seeking. Consequently, if a scenario is perceived as positive we will become risk-averse. This explains why investors take greater risks during the big decline than they otherwise would judge as reasonable.

Altogether, these human foibles make investing or trading in the stock markets a difficult task. So how could one possibly become a successful market player? This article is a key to this question; lets look at a root solution - One of the recipes of success is to control one’s risk and utilize proper “Money Management”. When we discuss Money Management here, it is not “risk control” per se, “diversification" or “how one makes trading decisions” as, stated most of the times.

Basic Rules for Risk Control

The trader/investor has to maintain some level of trading appropriate for the equity. Trading bases that could be measured are: 


  • Contracts
  • Shares of stock
  • Aggregate value
  • Risk-to-stops
  • Volatility  


Equity can also measured as:


  • Liquidation value of the account 
  • Liquidation value less risk


So a reader’s job in our way of Investing is just to keep these well proportioned.

In periods of losing (with the equity curve turning up), adding proportionally one additional position across markets is a good start. In periods of winning (with the equity curve turning down), subtracting proportionally one additional position across markets is a good start.


Traditionally, economic theory is based on the idea that market is rational and therefore makes rational decisions. Feelings and biases do not influence the investor’s judgment, only relevant information affects their behavior. Decision-makers decide on basis of the probability of each alternative outcome and select the alternative giving the maximum return. This view is not supported without exception.  In Risk Management a trader should avoid following making mistakes:


  • Costs, that is, losses, made at an earlier time may predispose decision-makers to take risks.
  • Situation framing also affects trader’s decision.
  • Negative games may cause a trader to go broke.
  • A positive expectation means that the trader has an edge.

§       Keeping what shows loss and selling what shows profit is one of the common mistakes committed by the trader.


The importance of cutting losses short is obvious. Large losses can be avoided if the trader only risks a small amount of capital in each and every trade and not letting a streak of losses compound into a big portion of initial capital.


When one enters the market, an expectation of winning that is positive must be present. To enter the market without a market expectation or a negative expectation is to lose money. One should feel comfortable wagering money with this positive expectation.


Risk Control – Break-even facts

  • A loss of 2% requires a gain of 3% on remaining capital in order to break even.


  • A loss of 30% requires a gain of 43% on remaining capital in order to break even.


§       A loss of 50% requires a gain of 100% on remaining capital in order to break even.


§       A loss of 90% requires a gain of 1000% on remaining capital in order to break even.


Streaks/Gamblers Fallacy: When probability of winning is 50%, then there will be an equal number of wins and losses over a large number of draws. A common misconception is that a winner will follow after a loser has been drawn.


Prospect theory/Disposition Effects: 

Psychological research has found that most individuals are risk seeking when the situation at hand is perceived as a losing situation and risk-averse when the situation is perceived as a winning situation.


R – multiples: The losing trades should be small R-multiples and the winning trades large R-multiples. A low "hit-rate" (probability of winning/losing) with large winners and small losers (e.g. 10R winners and 1R losers) is preferable to a high probability of winning with small winners and big losers.


Computing: A basic knowledge of computing the possibility of a certain gain is essential.


There are several alternate methods that can be used for managing risk;


·       New Position Risk

·       Ongoing Risk Exposure

·       Daily Volatility


The key is reliance on volatility and account size in determining the proper portfolio to trade.


Analyzing Size, Risk/Reward Ratio, And Percent Accuracy

As stated earlier in the money management chapter, choices made are influenced by how a situation is framed. As a consequence, there is greater risk and gamble in a losing situation, holding on to the position in hope that prices will recover. In a winning situation the circumstances are reversed. Investors will become risk averse and quickly take profits, not letting profits run.


The prudent speculators hunt for the outsized large move; a few big trades make up the bulk of profits and many small trades make up the losses. Winning trades can range from 35-50%, but that percentage reveals little information since we expect more losses (of smaller value) than winners (of much larger value). Win/loss ratio, while a favorite of the novice trader, is useless in terms of our analysis. Smart Money uses a 2% money management stop on each position they take, so they limit risk to 2% of their capital. The concept of the Speculators sitting out the next signal if the prior position was a winner is an important aspect of the entry signal process. If the last trade within the system was a loss, initiation at the current signal has more positive mathematical expectation. Even if the last trade was a loss on a short position, the next signal, even if it is a long signal, still has greater expectation.

 The Percentage Volatility Model

To gain perspective on risk and volatility one must use the ATR

Also referred to as the Trading Range, this system was introduced by J. Welles Wilder Jr. in his book "New Concepts in Technical Trading Systems." Wilder has found that high Average True Range values often occur at market bottoms following a "panic" sell-off. Low Average True Range values are often found during extended sideways periods, such as those found at tops and after consolidation periods.


The True Range indicator is the greatest of the following:


Ø     The price difference from today's high to today's low.

Ø     The price difference from yesterdays close to today's high.

Ø     The price difference from yesterdays close to today's low.

Ø     The Average True Range is a moving average (typically 7-days) of the True Ranges.

Ø     The True Range (TR) is exactly what the name implies;

Ø     An average value for a certain time-span.


The time span is 7-14 days. How do we calculate it? The 14 day's of ATR is accumulated. The formula used is rather simple;


MA(TR, Type, Period)



ABS(HI - CL.1),

ABS(CL.1 - LO)


Why is the TR so important? The TR is a very important measuring block for the system for reasons below:

1.     Number of contracts that make up each unit

2.     Using stops

3.     Determining when to pyramid (add additional units)

4.     In addition;

5.     Average True Range peaks before price bottoms.

6.     Low readings means the market is ranging.

7.     Average True Range peaks before the market top.

8.     Average True Range peaks after a secondary rally.

9.     Average True Range peaks during the early stages of a major price fall.


Characteristics and benefits of ATR


ATR is a truly adaptive and universal measure of market price movement.


Here is an example that might help illustrate the importance of these characteristics:


If we were to measure the average price movement of Corn over a two-day period and express this in dollars it might be a figure of about $500.00.


If we were to measure the average price movement of Yen contracts it would probably be about $2,000 or more. If we were building a system where we wanted to use the set appropriate stop losses in Corn and Yen we would be looking at two very different stop levels because of the difference in the volatility (in dollars).


We might want to use a $750 stop loss in Corn and a $3,000 stop loss in Yen. If we were building one system that would be applied identically to both of these markets it would be very difficult to have one stop expressed in dollars that would be applicable to both markets. The $750 Corn stop would be too close when trading Yen and the $3,000 Yen stop would be too far away when trading Corn.


However, let's assume that, using the information in the example above, the ATR of Corn over a two day period is $500 and the ATR of Yen over the same period is $2,000. If we were to use a stop expressed as 1.5 ATRs we could use the same formula for both markets. The Corn stop would be $750 and the Yen stop would be $3,000.

Now lets assume that the market conditions change so that Corn becomes extremely volatile and moves $1,000 over a two-day period and Yen gets very quiet and now moves only $1,000 over a two day period. If we were still using our stops as originally expressed in dollars we would still have a $750 stop in Corn (much too close now) and a $3,000 stop in Yen (much too far away now).


However, our stop expressed in units of ATR would adapt to the changes and our new ATR stops of 1.5 ATRs would automatically change our stops to $1500 for Corn and $1500 for Yen.


The ATR stops would automatically adjust to the changes in the market without any change in the original formula. Our new stop is 1.5 ATRs the same as always.


The value of having ATR as a universal and adaptive measure of market volatility cannot be overstated.


ATR is an invaluable tool in building systems that are robust (this means they are likely to work in the future) and that can be applied to many markets without modification.


Using ATR you might be able to build a system for Corn that might actually work in Yen without the slightest modification. But perhaps more importantly, you can build a system using ATR that works well in Corn over your historical data and that is also likely to work just as well in the future even if the nature of the Corn data changes dramatically.

Risk Determination and utilizing the concept of Units

On average, trend following CTAs and big time traders use a figure from 1 percent (the very conservative) and 3 percent (the very aggressive).

To summarize; the ATR concept is to act as measurement of funds at hand for a given risk tolerance. Units, allows the Trend trader to adopt and switch between futures markets with seamless ease, while maintaining the global risk parameter.


The problems with futures that contract - tick sizes differ from contract to contract as well as volatility.


To really comprehend the Units concept; when we take risk capital available and compute units per account we force ourselves to adjust unit size depending on how much we have. We must think in terms of units not how much we have in dollars. The mistake of thinking in dollars is that we speculate with a horrendous leverage.


I would suggest having a maximum unit rule; for example the limit is 5 units per market, 5-7 units per market group (such as the soybean complex). 10 units is the absolute total with the note that 1 unit long and 1 unit short is viewed as 1 unit. Hedged (short versus long) positions are considered a lower risk profile.


Unit reduction rule


Whenever you lose 10% you must cut back the unit trading size by 20%.


This type of trading/investing works with commodities as well as stocks. It is designed to ride the long term trend and when losing it is designed to lose mostly unrealized profits. A portfolio of unrelated instruments should be used. Ideally for a geberal public this type of speculating works well with the foreign exchange markets. 


Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

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