Using Implied Volatility And Volume

Transcription

1 BASIC TECHNIQUES Forecasting Trends With Indexes Using Implied Volatility And Volume Construct a trend-following system that adjusts to current market conditions. T raditionally, technicians have relied on historical prices to analyze the market. 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 historical patterns. Thus, using these indicators to forecast 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 profitable 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. IMPLIED VOLATILITY The implied volatility is volatility that the market is currently anticipating for the underlying asset, which by Scott Castleman can be a futures contract, a stock, or an exchangetraded fund (ETF). 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 implied 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 Exchange, is called the volatility index (VIX). The VIX 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 become 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. When we are searching for ways to forecast the market direction, it is essential to characterize the market with current information. Two such ways of describing the market are with implied volatility and volume.

2 BORIS LYUBNER

3 OEX TRADESTATION VIX Up arrows indicate high VIX levels Down arrows indicate low VIX levels 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. 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 trendfollowing 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. VOLUME 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 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 S&P 500 Volume Red line = 10-day simple moving average of volume Blue line = 50-day simple moving average of volume Up arrows indicate high levels of volume Down arrows indicate low levels of volume 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.

4 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, VIX Volume Red line = 10-day simple moving average of volume Blue line = 50-day simple moving average of volume Dashed blue line = days referenced for short position Solid red line = days referenced for long position 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. METHODS AND RESULTS 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 position 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 illustrated 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 sell Short next bar on open;

5 TRADESTATION STRATEGY PERFORMANCE REPORT (1/10/ /31/2002) Performance summary: All trades Total net profit $199, Open position P/L ($600.00) Gross profit $403, Gross loss ($203,475.00) Total # of trades 64 Percent profitable Number winning trades 30 Number losing trades 34 Largest winning trade $52, Largest losing trade ($18,800.00) Average winning trade $13, Average losing trade ($5,984.56) Ratio avg win/avg loss 2.25 Avg trade (win & loss) $3, 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, Return on account Performance summary: Long trades Total net profit $86, Open position P/L $0.00 Gross profit $187, Gross loss ($101,475.00) Total # of trades 32 Percent profitable Number winning trades 17 Number losing trades 15 Largest winning trade $34, Largest losing trade ($18,800.00) Average winning trade $11, Average losing trade ($6,765.00) Ratio avg win/avg loss 1.63 Avg trade (win & loss) $2, Max consec. winners 4 Max consec. losers 4 Avg # bars in winners 36 Avg # bars in losers 14 Max intraday drawdown ($43,450.00) Profit factor 1.85 Max # contracts held 1 Account size required $43, Return on account Performance summary: Short trades Total net profit $113, Open position P/L ($600.00) Gross profit $215, Gross loss ($102,000.00) Total # of trades 32 Percent profitable Number winning trades 13 Number losing trades 19 Largest winning trade $52, Largest losing trade ($14,500.00) Average winning trade $16, Average losing trade ($5,368.42) Ratio avg win/avg loss 3.09 Avg trade (win & loss) $3, Max consec. winners 2 Max consec. losers 4 Avg # bars in winners 51 Avg # bars in losers 14 Max intraday drawdown ($55,825.00) Profit factor 2.12 Max # contracts held 1 Account size required $55, Return on account 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. With 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 Using implied volatility and volume, this trend-following system attempts to incorporate current information about the marketplace.

6 FIGURE 6: ANNUALIZED PERFORM- ANCE SUMMARY FROM JANUARY 1996 TO DECEMBER 2002 the ratio between net profit and maximum drawdown is approximately 5 to 1. Figure 6 shows the annual performance of the system from the beginning of 1996 to the end of Each year was profitable, except for 2000, with 2001 being the most profitable (as the system took advantage of the large downtrend in the financial markets). CONCLUSION This trend-following system has yielded impressive results on the Russell 2000 futures contract for the past six years. Using the implied volatility and volume, this trend-following system attempts to incorporate 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 Mercantile Exchange, where he worked for Option Insight Trading Group. He currently trades financial index futures. ANNUAL TRADING SUMMARY Annual analysis (marked-to-market): Period Net profit gain Profit factor # trades profitable YTD $0.00 N/A N/A 0 N/A 12 month $45, $34, $91, ($3,100.00) (1.75) $17, $12, $35, $11, Annual rolling period analysis (marked-to-market): Period Net profit gain Profit factor # trades profitable 02 $34, $125, $121, $139, $152, $187, $199, SUGGESTED READING 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: Advanced 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, faculty.fuqua.duke.edu/7ewhaley/pubs/fear_gauge.pdf, February 20. See Traders Glossary for definition S&C