Using Implied Volatility And Volume

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by Scott Castleman

T

Using Implied Volatility

Construct a trend-following system that adjusts to

current market conditions.

And Volume

can be a futures contract, a stock, or an

exchange-traded fund (E

TF

)†. Implied volatility is usually used

for trading options on the underlying, but you can

also use it to trade the underlying itself. Calculating

volatility can be a problem, because you are trying to

measure something that will occur in the future.

Implied volatility is based on an expectation as

opposed to a posted value, but it is a necessary

component of any option pricing model.

There are various ways to calculate volatility. The

Black-Scholes† model is the most popular method.

Since implied volatility itself represents a one-year

standard deviation of price movement, traders can

use this number to estimate the extent of price changes

over the next year. Specifically, the higher the

im-plied volatility, the more the market can be expected

to move up and down over the next year.

The implied volatility of the Standard & Poor’s

100, calculated by the Chicago Board Options

Ex-change, is called the volatility index (V

IX

). The V

IX

is often referred to as “the investor fear gauge,” and

is used extensively by contrarians. Its value tends to

rise as financial markets decline and investors

be-come increasingly fearful about the future. You can

use this relationship between prices and implied

volatility to help identify market tops and bottoms,

since extreme levels in implied volatility tend to

accompany extreme levels in prices.

raditionally, technicians have relied

on historical prices to analyze the

mar-ket. They have created many different

indicators to predict the direction of

prices by basing their calculations on

past data. However, these indicators may fail to work

prospectively when markets do not repeat their

his-torical patterns. Thus, using these indicators to

fore-cast market direction is like trying to drive a car by

looking in the rearview mirror. Any change in the

road ahead could lead to disaster.

When we are searching for ways to forecast the

direction of the market, it is essential to characterize

the market with current information. Two such ways

of describing the market are with implied volatility

and volume. Using implied volatility and volume as

parameters, you should be able to construct a

profit-able trend-following system by adjusting the number

of days referenced in a simple Donchian†-style

breakout system. This allows the system to adjust

itself to reflect current market conditions.

I

MPLIEDVOLATILITY

The implied volatility is volatility that the market is

currently anticipating for the underlying asset, which

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In Figure 1 you can see that extremely high levels of the VIX are associated with market bottoms, while extremely low levels of the VIX are associated with market tops. For the trend-following system, I compared the current level of the VIX to the VIX levels over the previous 252 trading days (there are 252 trading days in a year). When the VIX is at extremely high levels, the market should be making a bottom and the system should be more inclined to take a long position. Conversely, when the VIX is at extremely low levels, the market should be making a top and the system should be more inclined to take a short position.

V

OLUME

In addition to implied volatility, volume is another important tool for analyzing the markets with current information. Volume refers to the number of shares or contracts bought and sold over a given time. This value is a measure of buying and selling interest in the

Market top

Market bottom

Market bottom Market bottom

OEX

VIX _{Up arrows indicate high VIX levels}

Down arrows indicate low VIX levels

Market top Market top

Market top

Market bottom

Red line = 10-day simple moving average of volume Blue line = 50-day simple moving average of volume

Market bottom Market bottom

Volume S&P 500

Up arrows indicate high levels of volume

Down arrows indicate low levels of volume

FIGURE 1: THE S&P 100 INDEX (OEX) VERSUS THE IMPLIED VOLATILITY INDEX OF THE S&P 100 OPTIONS (VIX). The top price chart is the S&P

100 index (OEX), and the bottom price chart is the implied volatility index of the S&P 100 options (VIX). The red up arrows indicate high levels of the VIX, while the blue down arrows indicate low levels. The high VIX levels reached in September 2001, July 2002, and October 2002 are associated with market bottoms. Low VIX levels reached in July 2001, March 2002, and August 2002 are associated with market tops.

FIGURE 2: 10-DAY VERSUS THE 50-DAY SMA OF VOLUME. The lower part

of the chart compares the 10-day SMA of volume to the 50-day SMA of volume. The red up arrows indicate high levels of volume, while the blue down arrows indicate low levels. Note that high volume levels in September 2001, July 2002, and October 2002 are associated with market bottoms. Low volume levels in August 2001, late March and early April 2002, and late August 2002 are associated with market tops. These time periods coincide with the extreme high and low levels reached in the VIX.

marketplace. Price Headley, the author of Big Trends In Trading, uses Bollinger Bands to analyze volume levels in the financial indexes. He notes that high levels of volume are associated with market bottoms, as investors rush to protect their portfolios by shorting the financial market indexes. Low levels of volume are associated with market tops, as investors become complacent over their recent gains and feel little need to protect their portfolios.

In my analysis of volume, I have chosen to smooth the data using simple moving averages because of the large day-to-day fluctuations. Figure 2 compares the short-term (10-day) simple moving average (SMA) of volume to the long-term (50-day) SMA of volume for the S&P 500 index. From the chart, it is evident that high levels of volume are associated with market bottoms, while low levels of volume are associated with market tops.

For this trend-following system, I have chosen to compare the 10-day SMA of volume to the highest and lowest volume

TRADEST

A

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Red line = 10-day simple moving average of volume

Solid red line = days referenced for long position

Blue line = 50-day simple moving average of volume

Dashed blue line = days referenced for short position Volume

VIX S&P 500

FIGURE 3: DAYS REFERENCED BY THE SYSTEM FOR LONG AND SHORT TRADES. On the bottom part of the

chart, the solid red line represents the number of days referenced for long trades, while the dashed blue line represents the number of days referenced for short trades. Note that the system reacts to the spikes in the VIX and in volume in the middle of September 2001 by referencing fewer days for long trades. This causes the system to become increasingly likely to take a long trade. The S&P 500 index proceeded to rally 21% off the lows made on September 21, 2001.

levels over the past 50 trading days. If the 10-day SMA of volume is at extremely high levels, then the market should be making a bottom, and the system will be more inclined to take a long position. Conversely, if the 10-day SMA of volume is at extremely low levels, the market should be making a top, and the system will be more inclined to take a short position.

M

ETHODSANDRESULTS

The logic of this system is based on the Donchian breakout system, which is a stop-and-reverse (SAR) trend-following system based on recent high and low prices. The Donchian system suggests going long if today’s high exceeds the highest price of the previous four weeks, and entering a short posi-tion if today’s low exceeds the lowest price of the previous four weeks. By using the implied volatility and volume as inputs, you can incorporate current market conditions into this analysis. Specifically, the number of days referenced by the system varies along with the implied volatility and volume.

As the implied volatility and volume reach extremely high levels, the system becomes more and more likely to take a long trade as the market makes a bottom. As the implied volatility and volume reach extremely low levels, the system becomes more and more likely to take a short trade as the market makes a top. This relationship is illus-trated in Figure 3.

FIGURE 4: TRADESTATION CODE. Use

any financial index for dataset 1 and the corresponding volatility index for dataset 2.

TRADESTATION CODE

{Use any finanical index for data set 1

and the appropriate volatility index for data set 2.}

Inputs: refLength1(29),refLength2(25),lookBackIV(252), lookBackVolume(50);

Var: dayLengthLong(0), dayLengthShort(0), currentIV(0),lowestIV(0),highestIV(0), avgVolume(0),

lowestVolume(0),highestVolume(0); currentIV = close of data2;

lowestIV = lowest(close of data2,lookBackIV); highestIV = highest(close of data2,lookBackIV); avgVolume = average(volume,10);

lowestVolume = lowest(volume,lookBackVolume); highestVolume = highest(volume,lookBackVolume); If (highestIV - lowestIV) <> 0 and (highestVolume-lowestVolume) <> 0 then

dayLengthLong = intportion(refLength1 - 0.5*refLength2* ((currentIV - lowestIV)/(highestIV - lowestIV) +

(avgVolume - lowestVolume)/

(highestVolume-lowestVolume)));

If (highestIV - lowestIV) <> 0 and (highestVolume-lowestVolume) <> 0 then

dayLengthShort = intportion(refLength1 - 0.5*refLength2* ((highestIV - currentIV)/(highestIV - lowestIV) +

(highestVolume - avgVolume)/

(highestVolume-lowestVolume))); If high>highest(high[1],dayLengthLong) then

buy next bar on open; If low<lowest(low[1],dayLengthShort) then

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FIGURE 5: PERFORMANCE SUMMARY FROM JANUARY 1996 TO DECEMBER 2002

The longest period of days referenced by the system is 29, and the shortest is four. According to author Howard Simons’ analysis of recent highs and lows, a period of fewer than three days does not provide enough information, and a period of more than 29 days provides too much. The EasyLanguage code for this system is provided in Figure 4.

Figure 5 shows the performance summary from January 1996 to December 2002 for the trend-following system, tested on the Russell 2000 futures contract. There were a total of 64 trades, which is enough to be statistically significant. Two key numbers are the percentage of trades that were profitable and the ratio between the average winning trade and the average losing trade.

Total net profit $199,750.00 Open position P/L ($600.00)

Gross profit $403,225.00 Gross loss ($203,475.00)

Total # of trades 64 Percent profitable 46.88%

Number winning trades 30 Number losing trades 34

Largest winning trade $52,050.00 Largest losing trade ($18,800.00)

Average winning trade $13,440.83 Average losing trade ($5,984.56)

Ratio avg win/avg loss 2.25 Avg trade (win & loss) $3,121.09

Max consec. winners 4 Max consec. losers 5

Avg # bars in winners 42 Avg # bars in losers 14

Max intraday drawdown ($38,375.00)

Profit factor 1.98 Max # contracts held 1

Account size required $38,375.00 Return on account 520.52%

Performance summary: Long trades

Total net profit $86,000.00 Open position P/L $0.00

Performance summary: Short trades

Total net profit $113,750.00 Open position P/L ($600.00)

With 46.88% of the trades being profitable and with an average win/loss ratio of 1.93, the system shows a significant edge. In addition, both long and short trades contribute almost evenly to the overall profitability of the system. The most profitable trade accounts for about 25% of the total profit, and

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FIGURE 6: ANNUALIZED PERFORM-ANCE SUMMARY FROM JANUARY 1996 TO DECEMBER 2002

C

ONCLUSION

This trend-following system has yielded im-pressive results on the Russell 2000 futures contract for the past six years. Using the implied volatility and volume, this trend-following system attempts to in-corporate current information about the marketplace. Whereas high levels of implied volatility and volume appear to coincide with market tops, low levels of implied volatility and volume appear to coincide with market bottoms. This system adjusts to current market conditions by increasing or decreasing the reference period used to take long and short positions based on current levels of implied volatility and volume. This dynamic capability of the system increases the likelihood that it will continue to work in the future as markets continually change. Because of today’s choppy market conditions, it would be advisable to test the system with additional indicators to filter out some of the whipsaws. Using indicators such as the average directional movement index (ADX) or requiring the price to move in the direction of the trade one average true range before entering the market may eliminate some of the losing trades.

Scott D. Castleman is a professional trader from Rochester Hills, MI. Previously, he was a trader at the Chicago Mercan-tile Exchange, where he worked for Option Insight Trading Group. He currently trades financial index futures.

ANNUAL TRADING SUMMARY Annual analysis (marked-to-market):

% Profit # %

Period Net profit gain factor trades profitable

YTD $0.00 N/A N/A 0 N/A

12 month $45,550.00 17.96% 3.39 10 40.00% 02 $34,075.00 12.85% 1.28 10 90.00% 01 $91,000.00 52.28% 3.14 5 120.00% 00 ($3,100.00) (1.75%) 0.96 9 44.44% 99 $17,675.00 11.08% 1.45 13 61.54% 98 $12,625.00 8.60% 1.13 14 57.14% 97 $35,450.00 31.82% 4.06 8 62.50% 96 $11,425.00 11.42% 1.26 12 58.33%

Annual rolling period analysis (marked-to-market):

% Profit # %

Period Net profit gain factor trades profitable

02 $34,075.00 12.85% 1.28 10 90.00% 01-02 $125,075.00 71.85% 1.76 14 107.14% 00-02 $121,975.00 68.84% 1.48 22 86.36% 99-02 $139,650.00 87.55% 1.48 34 79.41% 98-02 $152,275.00 103.68% 1.39 47 74.47% 97-02 $187,725.00 168.48% 1.47 54 74.07% 96-02 $199,150.00 199.15% 1.45 65 72.31% S&C

†See Traders’ Glossary for definition

S

UGGESTEDREADING

Headley, Price [2002]. Big Trends In Trading: Strategies To

Master Major Market Moves, John Wiley & Sons.

Murphy, John J. [1999]. Technical Analysis Of The Financial

Markets, New York Institute of Finance.

Natenberg, Sheldon [1994]. Option Volatility & Pricing:

Ad-vanced Trading Strategies And Techniques, revised ed.,

McGraw-Hill.

Simons, Howard J. [1999]. The Dynamic Option Selection

System: Analyzing Markets And Managing Risk, John Wiley

& Sons.

Whaley, Robert E. [1994]. “Derivatives On Market Volatility: Hedging Tools Long Overdue,” Journal of Derivatives. _____ [2003]. “The Investor Fear Gauge,” http://

faculty.fuqua.duke.edu/%7Ewhaley/pubs/fear_gauge.pdf, February 20.

the ratio between net profit and maximum drawdown is ap-proximately 5 to 1.