Pinning of Stock Prices on Expiration Date - Equity Options

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Options Strategy Monthly: February 2006

Q4 Earnings and Volatility Patterns. We find that the average absolute price reaction immediately following earnings announcements this season has been slightly higher than the average during the past two years. On the other hand, the average change in stocks’ implied volatility heading into the announcement date has been similar to the historical average. We also find that Retailing stocks had the highest average absolute price reaction relative to what was priced in by options market. However, Semiconductors & Semiconductor Equipment stocks had the lowest average absolute price reaction relative to what the options market had priced in prior to their earnings announcements.

Rapid Growth in ETF Option Volumes. The growth rate of volume in ETF options has outpaced that of single stock options since 2004. Volume in options on Energy ETFs has grown at an even faster pace, and currently accounts for about half of the total volume in all Sector-based ETF options.

“Pinning” of Stock Prices on Expiration Date. We find that for stocks closing within 1% of a strike on the day prior to expiration, the return distribution on the expiration date exhibits slightly greater clustering around zero in comparison to the average distribution of equity price returns over the past two years. For stocks with higher open interest of outstanding contracts as a proportion of the stock’s volume, there appears to be a greater dampening of price returns, possibly a consequence of delta-hedging activity by long gamma traders.

Sector Volatility Snapshots. Our Sector Volatility Snapshots allow investors to quickly assess aggregate volatility information for each S&P 500 GICS sector. The snapshots include implied and realized volatility for each sector and sector-based ETF, along with other useful metrics such as sector put-call skews, sector term structure trends, and notable volatility increases and decreases for stocks within each of the 10 GICS sectors.

February 10, 2006

Ryan Renicker, CFA

1.212.526.9425 [email protected]

Devapriya Mallick 1.212.526.5429

[email protected]

Equity Derivatives Strategy | Options Strategy Monthly: February 2006

February 10, 2006 2

Table of Contents

Fourth Quarter Earnings Related Volatility............................................................................. 3

Volatility Trading Environment ............................................................................................. 6

Rapid Growth in ETF Option Volumes ................................................................................. 6

Recent Developments in the Options Market......................................................................... 7

Impact of Option Expiration on Underlying Price Changes..................................................... 8

Price Returns in Expiration Week versus Other Weeks ............................................................ 8

Stock Return Distribution on Expiration Dates......................................................................... 9

Impact of Open Interest on Return Distribution ..................................................................... 10

Conclusion .................................................................................................................. 11

Appendix I: Sector Volatility Snapshots.............................................................................. 12


February 10, 2006 3

Fourth Quarter Earnings Related Volatility This January, a majority of companies in the S&P 500 reported fourth quarter results. As of February 7, 2006, 367 S&P 500 constituents have reported 4Q earnings (including December 2005), of which 266 (72%) reported in January. Approximately 59% reported positive EPS surprises, outnumbering companies having negative surprises by a ratio of about 3.31.

In this section, we examine how average implied and realized volatility changed during the 20 trading days leading up to each earnings announcement. Our sample universe includes companies in the S&P 500 that reported results from 12/1/05 to 2/7/06.

Figure 1 illustrates the average absolute daily percent price change2 for S&P 500 companies that have already reported their results, during the 20 trading days leading up to and immediately following each company’s earnings announcement. We find the average absolute return immediately following earnings announcements (“absolute earnings reaction”) this season has been about 3.5%, slightly higher than the average post-earnings reaction S&P 500 companies experienced in the last two years.3 We believe this year’s slightly higher absolute earnings reaction could be influenced by the relatively large price reactions a select number of companies (such as INTC and YHOO) had in January.

We also note that one month implied volatility began rising roughly three weeks before the date of announcement (Figure 2). This is similar to the historical behavior in implied volatility before the earnings announcement.

Figure 1: Average Absolute EPS Reaction Ahead of Q4 Earnings

Figure 2: Average 1-Month Implied Volatility Ahead of Q4 Earnings

0.0%

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t-20

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t-10 t-8 t-6 t-4 t-2 t

Days to Earnings Announcement

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etur

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t

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Ave

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Average 1-Month Implied Volatility

Source: Lehman Brothers, MarketQA Source: Lehman Brothers, OptionMetrics

1 Please see Inside the Earnings Season, U.S. Strategy for additional details. 2 From a volatility trading perspective, absolute returns (the absolute value of actual returns) are more relevant than actual returns. This is because pure volatility traders would delta-hedge option positions to minimize their exposure to directional moves in the underlying stock itself.

3 Please see our report Earnings Impact on Implied and Realized, Options Strategy Monthly, January 10, 2006 for a historical analysis of changes in volatility heading into earnings announcement dates.


February 10, 2006 4

Next, we examine the degree to which actual absolute earnings reactions thus far in Q4 ‘05 have differed from expected absolute earnings reactions the options market was pricing the day prior to each company’s earnings release.

As Figure 3 illustrates, stocks within the Retailing industry group have had the highest average absolute earnings reaction versus what had been priced in by the options market. In other words, it appears that the options market has underestimated the magnitude of the absolute earnings reaction for stocks within the Retailing industry group. However, this average figure is likely skewed by a few companies (such as BBBY, BBY and AMZN) that had large downside surprises, and thus large absolute price returns (ten companies within this industry group have reported earnings so far).

On the other hand, stocks within the Semiconductors & Semiconductor Equipment industry group have exhibited relatively low average absolute price returns versus what had been priced in by the options market. We believe one of the primary factors causing this relates to how risk expectations for a relatively high proportion of stocks within an industry group can change in response to one or two company-specific events. For example, we found that option market participants began pricing in higher expected price moves for the majority of Semiconductor stocks that had yet to report in the later half of January, following the large negative reaction to INTC’s earnings announcement.

Figure 3: Difference in Implied and Actual Earnings Reaction by Industry Group

6 6 10 8

31 2711

19 21 145

1324 26 23

5 416 20

2817

517

10-4%

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3%

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Implied - Actual Absolute Reaction# Companies Reported

Source: Lehman Brothers, OptionMetrics, Bloomberg, MarketQA

We have found that immediately following earnings events, 1-month implied volatility tends to decline by about 3 vol points, on average, for stocks within the S&P 500. In this section we examine how 1-month implied volatility changed following earnings announcement this season for companies having extremely positive (> 10%) or negative (< -10%) returns, to test whether changes in implied volatility following an earnings announcement can be dependent upon the direction of a stock’s return.


February 10, 2006 5

Figure 4: Implied Volatility Change in Stocks Having Large Earnings Reactions

Stocks with Large Positive

Reactions

Stocks with Large Negative

ReactionsNumber of Stocks 15 10Average Return after Announcement 12.6% -12.3%Average Implied Vol Before Earnings 39% 40%Average Implied Vol After Earnings 31% 35%Average Change in Implied Vol -9% -6%Number of Implied Vol Decreases 15 7% of Implied Vol Decreases 100% 70%

Source: Lehman Brothers, Bloomberg

As Figure 4 highlights, only 10 (15) of the companies that have reported earnings thus far have had large negative (positive) reactions following their earnings announcement. Among the 10 companies having large negative reactions, 3 experienced an increase in 1-month implied volatility the day after their earnings were released. On the other hand, for the 15 companies having the large positive stock price reactions, none had an increase in 1-month implied volatility after their earnings were released. In addition, all of these companies’ 1-month implied volatility declined the subsequent day.


February 10, 2006 6

Volatility Trading Environment

Rapid Growth in ETF Option Volumes

As we reported in last month’s edition of the Options Strategy Monthly, the total volume for options on single stocks4 increased dramatically during 2005. This general trend has continued in 2006, during which a couple of total daily volume records were set across exchanges. On the other hand, volume on the underlying stocks remains relatively flat.

ETF Option Volume Rising Faster than Single Stock Option Volume We analyze how volume of options on Exchange Traded Funds (ETFs) has changed relative to option volume on single stocks. Our universe includes all listed ETFs based on U.S. indices and sectors that currently have options; the single stock universe includes the current constituents of the S&P 500. The sample period is from January 1, 2004 to January 31, 2006.

We find that since January 2004, although the absolute 1-month average daily trading volume of single stock options remains high vs. options on ETFs5, the rate of increase in ETF options volume has continued to outpace growth in single stock option volume (Figure 5).

We believe one of the primary reasons for this is the increase in the total number of ETFs introduced during the past two years. The total trading volume on these as well as ETFs introduced prior to 2004 has increased, leading to increasing option volumes on ETFs (Figure 6).

Figure 5: Option Volume on ETFs Outpacing that of Single Stocks

Figure 6: Volume on ETFs and Options on ETFs Rising

50

100

150

200

Feb-0

4Apr-

04

Jun-0

4

Aug-04

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Dec-04

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5Apr-

05

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5

Aug-05

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Opt

ion

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ume

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led)

ETF Option Volume (Scaled)

Single-Stk. Option Volume (Scaled)

10

15

20

25

30

Jan-0

4

Mar-04

May-04

Jul-0

4

Sep-04

Nov-04

Jan-0

5

Mar-05

May-05

Jul-0

5

Sep-05

Nov-05

Jan-0

6

0

2,000

4,000

6,000

8,000ETF Option Volume (MM contracts)

ETF Volume (MM)

Source: Lehman Brothers, OptionMetrics. Source: Lehman Brothers, OptionMetrics, Bloomberg.

ETF Option Volume versus Single Stock Option Volume: Macro Risk Matters We find the total volume of options on all U.S. ETFs tends to increase relative to options on stocks during periods of high macro risk expectations for equities, inferred by abnormally high S&P 500 implied volatility (Figure 7). It has been well established that abnormally high “macro fear” tends to result from market shocks, during which single stock performance becomes more dependent on the movement of the market/event itself, rather than company-specific factors such as earnings surprises. In other words, stocks tend to become more positively correlated with one another during periods of

4 Our sample universe includes constituents of the S&P 500 Index. 5 For example, during January 2006, the 1-month average daily option volume at the single-stock level was about 3.8 million contracts versus about 1.0 million for that of ETFs.


February 10, 2006 7

heightened macro risk expectations, particularly if the abnormally high level of implied volatility coincides with unanticipated events such as terrorist attacks, versus anticipated events such as earnings season, even if the index is realizing relatively lower volatility. Thus, one would expect investors trying to hedge their positions during periods of heightened macro uncertainty to use more “macro-related” instruments such as options on ETFs.

Figure 7: Macro Fear: ETF vs. Single Stock Option Total Volume

Figure 8: Energy ETF/Total Sector-Based ETF Option Volume Ratio

11%

12%

13%

14%

15%

16%

17%

Jan-0

4

Mar-04

May-04

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4

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Nov-04

Jan-0

5

Mar-05

May-05

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5

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6

Avg

. SPX

3-M

onth

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Vol.

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GM Credit, IBM Results, Poor Econ. Data, SPX Selloff

Q3 EPS Anxiety

Oil Spikes, MarketFears Rise

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4

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4

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5

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Energy ETF/Sector ETF Options Volume Ratio

Average Monthly Crude Price

Source: Lehman Brothers, OptionMetrics.

Source: Lehman Brothers, OptionMetric, Bloomberg

We also observe that the recent growth in volume of options on sector-based ETFs is largely driven by the increasing popularity of Energy sector ETFs (e.g. XLE, OIH). As Figure 8 illustrates, the average option volume in Energy ETFs has outpaced volume in other sectors. In addition, the total number of Energy ETF contracts currently trading represents about half of the total volume of options on all sector ETFs. This has occurred concurrently with the rally in crude oil prices and the Energy sector.

Recent Developments in the Options Market

Options on the VIX On February 24, options on the VIX index will commence trading, presenting an instrument for trading “volatility of volatility”. These options will have contracts expiring on the two near months apart from one additional expiration month on the February quarterly cycle.

Investors who wish to insure their portfolios against a large market shock could use calls on the VIX as an alternative to purchasing S&P 500 index puts. However, the VIX index has historically experienced high realized volatility (22-day realized currently trades above 90%) and upside moves in the VIX could be magnified in the event of a large negative market catalyst. The asymmetry of changes in the VIX can be illustrated by the fact that since 1990, there have been 16 instances when it experienced an upside move of more than 20%, compared with 3 cases when the index fell by more than 20%. This should imply that writers of calls on the VIX would demand a higher premium, raising the cost of insurance using calls on the VIX.

Change in Options Trading Hours U.S. option exchanges have announced a change in market trading hours for options on stocks and sector-based indexes or ETFs. Beginning February 13, the new trading hours will be from 9:30 AM ET to 4:00 PM ET, instead of the present closing time of 4:02 PM ET. Options on market indexes and ETFs on such indexes will continue to trade until 4:15 PM ET.


February 10, 2006 8

Impact of Option Expiration on Underlying Price Changes

Equity options expire on the Saturday following the third Friday of each month, although the Friday of expiration (or Thursday if the Friday is an exchange holiday) is the last trading date for the expiring contracts. We have found that average contract volume traded in options on stocks and indexes increases as expiration week progresses and reaches a peak on the last trading day of the week6.

Several studies have attempted to explore whether the introduction of options impacts the returns of their underlying stocks and indexes. In this study, we examine the effect option hedging activity has on the price movements in the stocks themselves as option expiration approaches.

Market makers in option contracts tend to delta-hedge their positions to eliminate exposure to directional changes in the underlying price. This delta-hedging activity becomes more critical as option expiration approaches and the gamma for at-the-money options increases sharply. If the number of shares represented by the outstanding contracts is a significant proportion of the average daily volume in the stock, returns in the cash market can be affected by this delta hedging activity.

Delta hedging by traders who have purchased options (long gamma positions) tend to dampen stock price moves, since they tend to hedge by buying the underlying stock when it declines, and sell shares when prices rise. For example, a volatility trader having a long call position would maintain a short position in the underlying stock to hedge against downside directional risk in the underlying. If the stock price increases, the call moves in the money and more shares are needed to be sold in order to maintain the delta-neutral position. The opposite holds if the price decreases, in which case the underlying shares would be repurchased to remain delta-neutral. Thus, if a large proportion of hedgers are long gamma, price changes in the underlying shares would tend to be dampened. On the other hand, delta-hedgers having a net short position in options tend to exacerbate moves in the underlying, since they tend to buy more shares of the underlying as its price increases and sell more when the shares decline. The extent to which stocks remain “pinned” to strikes having relatively high open interest depends on whether the majority of the delta-hedgers have a net long or short option position.

Please note that the outstanding open interest at a given strike price is only an approximate measure of the anticipated hedging activity since option market makers can hedge their positions using other option contracts rather than the underlying stock itself.

The universe we use in this study includes constituents of the S&P 500 Index. The time period used is from January 2004 to December 2005.

Price Returns in Expiration Week versus Other Weeks

We compare the average returns on days leading up to expiration Fridays with returns leading up to Fridays on non-expiration weeks. Figure 9 shows that the average return for S&P 500 stocks remains almost constant as the week progresses in non-expiration weeks. However, price variability has tended to be relatively lower toward the end of expiration weeks.

6 Please see Equity Volatility Snapshot, January 24, 2006 for additional details.


February 10, 2006 9

Figure 9: Comparative Returns in Expiration Week and Other Weeks

-0.3%

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t-2 t-1 tD

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urns

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Expiration Week Returns Returns in Other Weeks Expiration Week Absolute Returns (RHS)

Source: Lehman Brothers, OptionMetrics

Stock Return Distribution on Expiration Dates

Since the mean of the returns is distorted by extreme moves, we compare the return distribution of stocks from the close on the day before expiration to the close on expiration with the average return distribution for all companies in the universe during the last two years. We shortlist stocks whose price could potentially be impacted by hedging activity, by including stocks which close within 1% of a contract strike on the day prior to expiration. Options with such strikes are closer to their at-the-money level and hence are likely to experience wide fluctuations in their delta close to expiration.

Figure 10 illustrates the distribution of expiration date returns for the shortlisted stocks. We standardize their returns by dividing their close-to-close return on expiration by their rolling 3-month realized volatility (we do this since a large price move is a more common in stocks having relatively high standard deviations of their returns). Thus, expressing stocks’ return distribution in terms of standard deviation levels adjusts the effect of stocks having higher volatility tending to experience large percentage moves with greater frequency.

Figure 10: Distribution of Returns on Expiration Date versus Average Distribution

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Standard Deviation of Returns

Prob

abili

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Average Distribution

Distribution on Expiration

Source: Lehman Brothers, MarketQA


February 10, 2006 10

We find that there is a slightly higher probability of returns remaining closer to zero versus the non-expiration-date return distribution when stocks have a closing price before expiration within 1% of a traded strike, although the effect is not very pronounced on an aggregate basis. This could be a consequence of the hedging activity of long option holders.

We also find that the probability of large negative returns tends to be higher than that of the average distribution. However the probability of very large positive returns does not appear to be significantly different from the average distribution on non-expiration dates. The higher peak and fatter tail (representing a more leptokurtic7 distribution) are consistent with the expected delta-hedging activity, which tends to increase the frequency of very high or very low price returns at expiration.

Impact of Open Interest on Return Distribution

Having examined the return distribution when stocks close near a strike before expiration, we now analyze whether a large open interest of contracts outstanding at that strike has a tendency to cause a greater degree of ”pinning”. We can use the kurtosis of the return distribution as a “pin factor” or a rough proxy for measuring the impact of hedging activity. A higher pin factor would indicate a relatively larger number of returns clustered close to zero and a distribution having fatter tails. (Note that such a measure would be more stable in a distribution having a higher number of data points. Thus, this study only provides an approximate guide to the extent of the “pinning effect”.

The open interest outstanding at each strike (calls plus puts) allows us to gauge the stock’s demand or supply if all option holders and writers were dynamically delta hedging their option positions. Thus, we divide the open interest by the stock’s volume to account for its liquidity. We divide the distribution into quartiles on the basis of this open interest to volume ratio, with Quartile 1 representing the group with the highest ratio. Figure 11 demonstrates that the two largest quartiles (according to this metric) exhibit relatively more leptokurtosis, whereas the extent of pinning appears to be much lower for the other two quartiles. However, the probability of extreme returns (more than 0.2 standard deviations) in either direction does not appear to depend very strongly upon the quartile of the open interest to volume ratio.

Our study also indicates that high open interest as a proportion of stock volume results in a lower average absolute return for the stock on the day of expiration (Figure 12). This could imply that, on average, the delta-hedging activity of option traders who are net long options appears to have been more pronounced than the hedging activity of short gamma traders.

7 Kurtosis is the fourth moment of a distribution and is a measure of the “peakedness” or “fat-tailedness”. A leptokurtic distribution is one with a higher peak and fatter tails than the normal distribution. A platykurtic distribution has smaller tails and a flatter top than the normal distribution.



Figure 11: Extent of Pinning by Open Interest/Stock Volume Figure 12: Absolute Returns by Open Interest/Stock Volume

Open Interest/Volume

Quartile #

Avg Open Interest / Volume

Pin Factor (Kurtosis)

Probability (Returns > 0.2 Stdev)

Probility (Returns < -0.2 Stdev)

Quartile 1 (Highest) 620 62% 26 0.8% 0.3%Quartile 2 620 25% 39 0.3% 1.0%Quartile 3 620 12% 7 0.6% 0.3%Quartile 4 (Lowest) 620 4% 2 0.2% 0.6%

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etur

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Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics

Conclusion

We examined the impact option expiration has on the price returns in the underlying stocks themselves. The distribution of expiration day returns for stocks that close within 1% of a listed strike on the day before expiration tends to have a higher peak than the average return distribution for S&P 500 stocks. This might indicate a small degree of pinning of the underlying stock price to the respective strike, as a consequence of the hedging activity of long option holders. We have also found that, as a proportion of underlying stock volume, options with a higher open interest tend to cause greater “peakedness” in the return distribution. This could imply that, on average, the delta-hedging activity of option traders who are net long options appears to have been more pronounced than the hedging activity of short gamma traders.



Appendix I: Sector Volatility Snapshots

In this section, we introduce our Sector Volatility Snapshots. These snapshots allow investors to quickly assess aggregate volatility information for each GICS sector in the S&P 500 Index.

The average volatility (implied or realized) for a GICS sector is calculated as the weighted- average-volatility of each of their respective index constituents, weighted by market capitalization. The weighted-average volatility for a GICS industry group is obtained similarly. ETFs having options are mapped to one GICS sector each, although the mapping is not perfect since stocks in an ETF could be classified into multiple sectors as per the GICS sector classification methodology. However, they closely reflect the performance and volatility characteristics of the sectors and industries they represent.

• We display the weighted-average implied and realized volatility at the sector level for a one year period. For each industry group within the sector and each ETF that closely resembles the respective industry or sector, we provide the number of standard deviations the current level of the implied-realized volatility spread is trading above or below to its one-year historical mean. A highly negative standard deviation could indicate that options are trading cheap, whereas a highly positive standard deviation possibly implies such options are relatively rich. (Note that there usually do not exist actively traded options at the industry group level and the relative richness/cheapness indicators for these industry groupings should only be used only as a starting point for identifying single-stock volatility trades within that industry group. Furthermore, while options on ETFs exist, their liquidity should be taken into account before executing a trade, since many are currently thinly traded as well.)

• The “Largest Implied Volatility Increases” and “Largest Implied Volatility Decreases” denote the stocks within the sectors that have experienced the highest and lowest absolute changes in implied volatility, over one-week and one-month periods.

• The Put-Call Skew is calculated as the difference between the 3-month put-implied volatility and the 3-month call-implied volatility for 20 delta puts and 20 delta calls, divided by the 3-month at-the-money implied volatility. The weighted-average skew at the sector and industry group level are calculated similarly, except the volatility used is the market-cap weighted-average implied volatility. If the current skew level is trading a relatively high number of standard deviations above its one-year average, this could indicate the option market is pricing in increasing risk expectations for the underlying stock, since the put–call implied volatility differential implies that option traders have been bidding up put protection, rather than upside participation via long call positions.

• We show the history of the slope of the volatility term structure as measured by the difference between 12-month and 3-month at-the-money implied volatilities. Since longer term implied volatility tends to be more stable than implied volatility on shorter-dated options, a lower term structure spread relative to its historical pattern could indicate 3-month options are trading richer than they typically have in the past. Alternatively, if the slope of the term structure is relatively steep, one might infer near term options are trading at a relative discount to longer dated options.



Figure 13: Energy Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolDVN 39% 5% 36% SLB 39% 11% 35% NBR 37% -1% 36% BR 16% 0% 31%SLB 39% 5% 35% BHI 39% 10% 34% BJS 38% -1% 36% KMI 22% 1% 18%MRO 35% 3% 34% NOV 44% 6% 45% BR 16% 0% 31% APC 34% 1% 30%APA 36% 3% 30% COP 32% 6% 31% MUR 33% 1% 30% AHC 37% 1% 35%NOV 44% 3% 45% MRO 35% 6% 34% KMI 22% 1% 18% MUR 33% 2% 30%

Largest Implied Volatility Increases Largest Implied Volatility Decreases1-week Increase 1-month Increase 1-week Decrease 1-month Decrease

Implied Volatility vs Realized Volatility

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Wgt Avg Implied Vol Wgt Avg Realized Vol

3-Month Put-Call Skew (20 Delta)

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Wgt Avg 20 Delta Skew (3m) Avg + 1 Stdev Avg - 1 Stdev

Implied-Realized Spread (by Industry Groups/ETF)# of Standard Deviations from 1-year Average

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

OSX

IYE

XOI

OIH

XLE

Energy

XNG

Cheap > > > > > > > > > > > > Rich

Relative Skews (by Industry Groups/ETF)# of Standard Deviations from 1-year Average

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

XNG

Energy

OIH

IYE

XLE

OSX

XOI

Cheap > > > > > > > > > > > > Rich

12-Month - 3-Month Term Spread

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6

Wgt Avg 12m-3m Term Avg + 1 Stdev Avg - 1 Stdev

Relative Term Spreads (by Industry Groups/ETF)# of Standard Deviations from 1-year Average

-3.5-3.0-2.5-2.0-1.5-1.0-0.50.0

OSX

OIH

XOI

XNG

IYE

XLE

Energy

Cheap > > > > > > > > > > > > Rich

Note: The put-call skew is calculated by taking the difference between the 20-Delta put-implied volatility and 20-Delta call-implied volatility, divided by the 3-month ATM implied volatility. Sector level volatilities are the market cap weighted implied volatilty for each constituent. A high skew is generally associated with a relatively high demand for downside protection.

Note: The term structure spread is calculated by taking the difference between the 12-month ATM implied volatility and the 3-month ATM implied volatility. Sector level term spread is calculated from the market cap weighted implied volatilities of the constituents. A steep term structure indicates shorter-dated implied volatility could be cheap relative to longer-dated implied volatility.

Note: We calculate each sector's average implied volatility by weighting the 3-month at-the-money implied volatility of its constituents by market capitalization. Investors should consider liquidity of options of a stock or ETF before entering an options position since, although options on ETFs exist, many are thinly traded.




Figure 14: Materials Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolMWV 21% 7% 15% X 52% 12% 44% WY 21% -1% 16% EC 18% -8% 47%ATI 49% 6% 39% ATI 49% 8% 39% IP 22% -1% 18% ECL 17% -2% 14%X 52% 4% 44% NUE 42% 7% 35% NEM 37% -1% 35% IFF 20% -1% 14%

VMC 28% 3% 34% FCX 39% 4% 37% PD 40% -1% 40% DD 19% -1% 15%NUE 42% 2% 35% PTV 27% 4% 23% SIAL 17% 0% 11% SEE 21% -1% 15%



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Feb-05

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6



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6



0.0 0.2 0.4 0.6 0.8 1.0

XAU

XLB

IYM

Materials

Cheap > > > > > > > > > > > > Rich


-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Materials

XLB

XAU

IYM

Cheap > > > > > > > > > > > > Rich


-2%

-2%

-1%

-1%

0%

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Feb-05

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6



-2.5-2.0-1.5-1.0-0.50.00.51.0

Materials

XAU

IYM

XLB

Cheap > > > > > > > > > > > > Rich







Figure 15: Industrials Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolCD 29% 3% 27% NAV 39% 4% 30% TYC 23% -2% 32% PLL 21% -4% 15%

COL 21% 3% 23% IR 23% 2% 21% APCC 32% -2% 36% AW 31% -3% 26%CAT 27% 3% 23% GE 16% 2% 14% PLL 21% -1% 15% ITT 22% -3% 27%NAV 39% 2% 30% CAT 27% 1% 23% UPS 18% -1% 16% APCC 32% -2% 36%WMI 19% 1% 14% CD 29% 1% 27% ROK 23% -1% 19% NOC 15% -2% 11%



15%

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Dec-05

Jan-0

6



0.0 0.5 1.0 1.5 2.0 2.5

XAL

CYC

Capital Goods

XLI

Transportation

Commercial Services & Supplies

Cheap > > > > > > > > > > > > Rich


-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Capital Goods

CommercialServices & Supplies

Transportation

XLI

CYC

XAL

Cheap > > > > > > > > > > > > Rich


-1%

0%

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1%

2%

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-1.5-1.0-0.50.00.51.0

Commercial Services & Supplies

Capital Goods

XLI

CYC

Transportation

XAL

Cheap > > > > > > > > > > > > Rich







Figure 16: Consumer Discretionary Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolDCN 72% 12% 71% MYG 65% 28% 24% AMZN 32% -10% 30% F 43% -7% 33%MYG 65% 9% 24% DCN 72% 16% 71% UVN 28% -5% 30% MAT 23% -5% 23%SHW 28% 4% 18% GT 38% 5% 43% GT 38% -4% 43% MCD 23% -4% 19%GM 66% 3% 54% SHW 28% 4% 18% KRI 24% -3% 16% AMZN 32% -4% 30%BLI 39% 3% 29% IPG 33% 4% 23% CC 35% -2% 29% DG 21% -4% 21%



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6



-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Media

RTH

HHH

Automobiles & Components

Consumer Services

Consumer Durables & Apparel

XLY

Retailing

Cheap > > > > > > > > > > > > Rich


-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5

RTH

Consumer Services

Retailing

HHH


Media

XLY


Cheap > > > > > > > > > > > > Rich


-3%

-2%

-2%

-1%

-1%

0%

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Feb-05

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6



-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5

HHH


Media

Retailing


Consumer Services

XLY

RTH

Cheap > > > > > > > > > > > > Rich







Figure 17: Consumer Staples Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolACV 22% 2% 18% HNZ 19% 3% 11% TSN 26% -1% 32% ABS 13% -18% 33%CLX 18% 2% 19% SVU 25% 2% 24% KR 21% -1% 16% EL 23% -4% 22%

WFMI 34% 2% 30% CLX 18% 1% 19% SVU 25% -1% 24% KR 21% -3% 16%ABS 13% 1% 33% HSY 21% 1% 17% UST 22% -1% 21% UST 22% -2% 21%CVS 24% 1% 21% BUD 17% 1% 14% CCE 22% -1% 18% COST 20% -2% 16%



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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Food & StaplesRetailing

XLP

Household &Personal Products

Food, Beverage &Tobacco

Cheap > > > > > > > > > > > > Rich


-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

XLP




Cheap > > > > > > > > > > > > Rich


-1%

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-0.4-0.3-0.2-0.10.00.10.20.30.4


XLP



Cheap > > > > > > > > > > > > Rich







Figure 18: Health Care Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolBAX 24% 3% 16% GDT 23% 10% 33% BSX 38% -7% 32% THC 45% -4% 30%THC 45% 2% 30% CHIR 9% 6% 5% CHIR 9% -5% 5% AET 24% -4% 23%

AMGN 26% 2% 24% ESRX 36% 4% 26% RX 18% -3% 20% RX 18% -3% 20%PKI 28% 2% 24% BMY 25% 4% 18% AET 24% -2% 23% UNH 25% -3% 22%FRX 31% 2% 23% BSX 38% 4% 32% CVH 27% -2% 21% TMO 21% -3% 18%



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-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

Pharmaceuticals & Biotechnology

PPH

XLV

IYH

Health Care Equipment & Services

BBH

IBB

Cheap > > > > > > > > > > > > Rich


-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

BBH


IYH


IBB

XLV

PPH

Cheap > > > > > > > > > > > > Rich


-3%

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-0.8-0.6-0.4-0.20.00.20.40.6


IBB

PPH

XLV


BBH

IYH

Cheap > > > > > > > > > > > > Rich







Figure 19: Financials Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolJP 28% 5% 11% JP 28% 8% 11% CFC 28% -5% 26% CFC 28% -5% 26%

FRE 23% 3% 18% ET 35% 4% 27% C 15% -2% 15% WM 21% -4% 21%AOC 31% 2% 21% SAFC 23% 3% 16% NTRS 18% -2% 16% PRU 21% -3% 15%EQR 19% 1% 14% BSC 23% 3% 19% JNS 29% -2% 28% SPG 20% -3% 17%NFB 21% 1% 14% ALL 19% 3% 15% PFG 18% -2% 13% JNS 29% -3% 28%



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-0.5 0.0 0.5 1.0 1.5 2.0 2.5

Diversified FinancialsBKXXLFIYFIYRXBDRKHBanksInsuranceICFReal Estate

Cheap > > > > > > > > > > > > Rich


-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Banks

IYFInsuranceBKX

Diversified FinancialsIYRXLF

RKH

Real EstateICFXBD

Cheap > > > > > > > > > > > > Rich


-1%

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-2.0-1.5-1.0-0.50.00.51.0

BanksIYRRKHICFDiversified FinancialsXBDXLFIYFInsuranceReal EstateBKX

Cheap > > > > > > > > > > > > Rich







Figure 20: Information Technology Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolCIEN 63% 6% 47% AMD 52% 9% 43% PMTC 35% -9% 34% SBL 37% -9% 27%GTW 57% 5% 51% BRCM 41% 6% 47% JDSU 53% -5% 54% VRSN 32% -8% 25%AMD 52% 4% 43% GTW 57% 6% 51% AMCC 53% -3% 43% SLR 41% -7% 34%MOT 33% 3% 31% JDSU 53% 4% 54% CSCO 21% -3% 24% CSC 28% -5% 37%ADCT 45% 3% 41% MU 38% 4% 32% SFA 8% -3% 11% ALTR 29% -5% 32%



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-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0

Semiconductors & Semiconductor EquipmentSMHIGMSoftware & ServicesIYWMSHIAHBDHSOXIGVSWHWMHIGNTXXIGWXCITechnology Hardware & EquipmentXLK

Cheap > > > > > > > > > > > > Rich


-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0

XCISemiconductors & Semiconductor EquipmentMSHSoftware & ServicesIGMIGWTXXIAHWMHSOXIYWTechnology Hardware & EquipmentIGNSWHIGVSMHBDHXLK

Cheap > > > > > > > > > > > > Rich


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XLKSemiconductors & Semiconductor EquipmentSMHWMHIGWTechnology Hardware & EquipmentMSHIGVIGNIYWTXXSOXIAHSWHIGMBDHSoftware & ServicesXCI

Cheap > > > > > > > > > > > > Rich







Figure 21: Telecommunication Services Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolS 29% 3% 18% Q 43% 5% 30% T 14% -1% 13% VZ 17% -2% 15%Q 43% 2% 30% S 29% 3% 18% CTL 19% 0% 18% CZN 18% 0% 16%

CZN 18% 2% 16% AT 20% 1% 16% BLS 17% 0% 16% CTL 19% 0% 18%VZ 17% 1% 15% T 14% 0% 13% AT 20% 1% 16% BLS 17% 0% 16%AT 20% 1% 16% BLS 17% 0% 16% VZ 17% 1% 15% T 14% 0% 13%



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-2.0 -1.5 -1.0 -0.5 0.0 0.5

IYZ

TTH

TelecommunicationServices

Cheap > > > > > > > > > > > > Rich


-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0


IYZ

TTH

Cheap > > > > > > > > > > > > Rich


-1%-1%0%1%1%2%2%3%3%4%4%

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-0.8-0.6-0.4-0.20.00.20.40.60.81.0

IYZ


TTH

Cheap > > > > > > > > > > > > Rich







Figure 22: Utilities Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolAYE 34% 8% 18% AYE 34% 8% 18% AES 30% -2% 23% TXU 29% -3% 27%DYN 51% 6% 40% AES 30% 4% 23% NI 19% -2% 15% CMS 22% -3% 17%XEL 16% 2% 13% DYN 51% 3% 40% FPL 18% -2% 17% ED 16% -3% 10%PEG 20% 1% 19% DTE 15% 2% 13% DUK 16% -1% 14% NI 19% -3% 15%KSE 17% 1% 15% KSE 17% 1% 15% CEG 21% -1% 27% FPL 18% -3% 17%



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UTY

IDU

Utilities

UTH

XLU

Cheap > > > > > > > > > > > > Rich


-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

UTY

UTH

IDU

XLU

Utilities

Cheap > > > > > > > > > > > > Rich


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-1.0-0.8-0.6-0.4-0.20.00.2

IDU

XLU

Utilities

UTY

UTH

Cheap > > > > > > > > > > > > Rich







Analyst Certification: I, Ryan Renicker, hereby certify (1) that the views expressed in this research email accurately reflect my personal views about any or all of the subject securities or issuers referred to in this email and (2) no part of my compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this email.

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Pinning of Stock Prices on Expiration Date - Equity Options

Economy & Finance

Transcript of Pinning of Stock Prices on Expiration Date - Equity Options