options strategy monthly - 2006 - low volatility in the 7th inning? housing market, credit markets...

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Options Strategy Monthly: Low Volatility in the 7th Inning?

Low Volatility Environment Continued in 2005. In 2005, the S&P 500 was largely range-bound, resulting in the continuation of the nearly 3-year low volatility regime. Although the 12-month minus 3-month term spread remained positive, the Q4 market rally, accompanied by the substantial decline in short-dated volatility at the end of the year led the term spread higher. This relatively steep term spread could be a signal that the options market is pricing in higher short-dated implied volatilities during the following year.

Volatility Trading Environment in 2005. Call overwriting continued to be an increasingly popular strategy. This year, the BXM performed roughly in-line with the S&P 500 Total Return Index. Systematically selling volatility was a more difficult strategy in 2005, as implied volatility continued to decline relative to subsequently realized volatility. In October 2005, the CBOE launched options with weekly expirations on the SPX and OEX. “Weeklys” allow investors to trade nearer-term volatility more efficiently than traditional option contracts.

Higher Volatility for 2006? We believe both implied and realized volatility for the S&P 500 and single stocks should trade a few volatility points higher in 2006 relative to 2005. Potential catalysts include credit concerns, an uncertain interest rate outlook, housing market weakness, volatile energy prices, reversion in the volatility risk premium, and event risks such as the 2006 U.S. Congressional elections, geopolitical concerns, avian flu, etc.

Low Volatility Regime to Continue in 2006? Potential risks to our forecast arise if energy prices stabilize, consumers’ financial condition improves, strength in employment continues, fears of interest rate hikes dissipate, and strong EPS figures and/or multiple expansion allows the S&P 500 to break out of its range.

Forecasting Expected Stock Price Moves For Earnings Announcements. We find stocks exhibit average absolute returns of over 3% following earnings reports and their implied volatility gradually rises during the weeks leading up to the announcement. The options market tends to efficiently price in event risks, with higher implied volatility corresponding to stocks that tend to realize relatively large moves following earnings. This was found to be consistent across sectors since the first quarter of 2004.

Introducing the Sector Volatility Snapshots. We introduce our Sector Volatility Snapshots, which allow investors to quickly assess aggregate volatility information for each S&P 500 GICS sector. The snapshots will be included in each Options Strategy Monthly, and 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 we analyze.

January 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: Low Volatility in the 7th Inning?

January 10, 2006 2

Table of Contents

Volatility Overview: 2005 ..........................................................................................3 Low Volatility Environment Continues: Will the Status Quo Persist? ............................................ 3

Implied Volatility Term Spread and Forward Implied Volatility .................................................. 4

Patterns in Implied Volatility Skew....................................................................................... 5

Volatility Trading Environment in 2005 ..........................................................................6 Call Overwriting Strategies ............................................................................................... 6

Volatility Risk Premium Convergence ................................................................................... 6

Dispersion Trading .......................................................................................................... 7

Options with Weekly Expirations (“Weeklys”)....................................................................... 7

Option Volumes Continue to Accelerate, Currently Stand at Record Levels.................................. 8

Volatility Outlook for 2006 .........................................................................................9 Potential Catalysts for Higher Volatility................................................................................. 9

Low Volatility Regime to Continue in 2006?....................................................................... 11

Earnings Impact on Implied and Realized Volatility .........................................................12 Higher Realized Volatility When Earnings Are Reported ....................................................... 12

Elevated Risk Expectations as Earnings Date Approaches...................................................... 13

Expected Stock Price Reaction to Earnings Announcements ................................................... 14

Event Volatility Predicts Announcement Across Sectors .......................................................... 15

Predictability of Earnings Impact by Quarter ....................................................................... 16

Conclusion .................................................................................................................. 16

Appendix I: Forward Implied Volatility and 1-Day Expected Price Move .............................17

Appendix II: Sector Volatility Snapshots .......................................................................19


January 10, 2006 3

Volatility Overview: 2005 Low Volatility Environment Continues: Will the Status Quo Persist?

Throughout the majority of 2005, the S&P 500 Index traded in a relatively tight range, as investors weighed micro factors such as consistently strong earnings growth, against macro and geopolitical concerns, such as rising short-term interest rates and historically high energy prices.

These opposing forces tended to partially offset one another, preventing the market from experiencing a strong move to either the upside or downside. This, in turn, resulted in relatively low implied volatility for the S&P 500 for most of the year. In addition, S&P 500 90-day realized volatility averaged a mere 10% for the year. Implied volatility, which tends to trade at a premium to realized, averaged slightly over 12%. In addition, there were three occasions - February, July, and November - when S&P 500 short-dated implied volatility approached 10-year lows.

However, credit concerns among U.S. automotive manufacturers took center stage in April, leading (briefly) to widening spreads in credit markets. This credit-driven shock – and accompanying market sell-off – resulted in a temporary spike in short-dated S&P 500 implied volatility, indicating investors were becoming increasingly concerned that credit deterioration could negatively impact the aggregate equity market. This fear quickly subsided, however, and implied volatility retreated from its intra-year high and the S&P 500 rebounded to its multi-year highs.

We observed a relatively brief increase in equity risk expectations in October, as investors braced for the economic consequences of Hurricanes Katrina and Rita and 3rd quarter earnings reports, and anxiously evaluated the degree to which rising interest rates would impact the domestic economic environment. However, heading into November, the market rallied before trading relatively sideways throughout most of December, and implied volatility once again retraced to its multi-year lows.

Figure 1: Key Developments in 2005 and their Impact on the S&P 500 and 3-Month Implied Volatility

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Source: Lehman Brothers, OptionMetrics


January 10, 2006 4

Implied Volatility Term Spread and Forward Implied Volatility

The term structure of implied volatility and the forward volatility interpolated from longer-dated options can provide insights into how short-dated risk expectations are expected to change in the future. The volatility term spread, measured by the difference between 12-month and 3-month implied volatility, typically moves in inversely with near-term volatility, since longer-dated implied volatility tends to be more stable than shorter-dated implied volatility.

In 2005, the 12-month – 3-month term spread bottomed out in April, as investors bid up shorter-term protection during the market downturn associated with widening auto credit spreads. The term spread again narrowed in October as the demand for near-term protection accelerated. Heading into year end, the term spread steepened as near-term catalysts for equity volatility subsided (Figure 2).

Figure 2: 12-Month – 3-Month Term Spread

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To glean additional information from the term structure, we compare the expected 3-month implied volatility level, in 9 months’ time, (3-month forward volatility in 9 months) with the current 3-month at-the-money implied volatility (Figure 3). While 3-month forward volatility has tended to trade in line with “current” 3-month implied volatility during the past five years (with the exception from mid 2002 to early 2003), we have recently observed the spread between the two metrics widening throughout 2005. We believe this likely indicates the options market is pricing in expectations for higher short-dated implied volatility in the months ahead.

Figure 3: 9-Month, 12-Month Forward Implied Vol. vs. 3-Month at-the-money Implied Volatility

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January 10, 2006 5

Patterns in Implied Volatility Skew

Examining changes in the absolute level of at-the-money implied volatility for short-dated options tends to be a common means of estimating risk expectations in the market.

However, we believe another useful metric for estimating the degree of market “fear” or “complacency” is the change in the put–call skew, which, compares the relative levels of out-of-the-money implied volatility on 20 delta put contracts with that of 20 delta calls.

In Figure 4, we observe two instances when the implied volatility skew experienced rather dramatic increases – once in mid April, and again in mid October – similar to what was observed for at-the-money implied volatility levels. A rise in skew implies investors were likely bidding up downside protection to hedge their existing long portfolios against a possible market downturn.

We observed a decline in the demand for downside protection, and perhaps an increased demand for upside exposure in the market during the rally from mid May to the middle of June, as evidenced by the declining put–call skew. A similar trading pattern also occurred during the November rally, as the demand for downside protection subsided when the market rebounded from its late October lows.

Figure 4: S&P 500 Index 20 Delta Put – Call Skew (3-Month Constant Maturity Implied Volatility)

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January 10, 2006 6

Volatility Trading Environment in 2005

Call Overwriting Strategies

Call overwriting has become an increasingly popular strategy during the past few years, as investors sought to obtain incremental alpha during the largely range-bound markets of recent years. In an overwrite strategy, an investor holds a long position in a stock or index portfolio and simultaneously writes at-the-money or out-of-the-money calls against the long position.

According to the Chicago Board Options Exchange (CBOE), more than $13 billion has recently been allocated by asset managers to buy-write products, many of which are benchmarked to CBOE S&P 500 BuyWrite Index (BXM).

During the last five years, the BXM has not only generated additional yield over the S&P 500 (Figure 5), but also displayed relatively lower standard deviation. Of course, the effectiveness of any overwriting strategy largely depends on the direction of the underlying portfolio itself1. Thus, overwriters did not participate in the November market rally, and if going forward the market breaks out of its range, the popularity of the strategy could decline.

Figure 5: Performance of Passive Call Overwriting (BXM) vs. S&P 500 Total Return Index

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Source: Lehman Brothers, Bloomberg, OptionMetrics

Volatility Risk Premium Convergence

From a pure volatility trading perspective, the average spread of 3-month at-the-money implied volatility over the subsequent three months’ realized volatility (ex-post realized volatility) can be a useful measure to gauge the premium demanded by option sellers for incurring short volatility risk. This spread traded lower in 2005 than it had in previous years, partly reflecting the growth of strategies that systematically implement net short volatility positions (such as call overwriting funds).

1 Please see Renicker, R.N. and Mallick D. (2005) Enhanced Call Overwriting, Lehman Brothers, Equity Derivatives Strategy, (pages 3, 5 and 9) for further details.


January 10, 2006 7

As Figure 6 and Figure 7 demonstrate, the continued compression of the volatility risk premium was witnessed both in options on the S&P 500 Index as well as on single stock options.

Figure 6: Volatility Risk Premium (SPX Index Options)

Figure 7: Average Implied and Realized Volatility (Equity Options)

YearAverage Premium ( 3-month Implied - Future Realized)

1996 2.93%1997 2.21%1998 3.52%1999 4.89%2000 -0.61%2001 2.02%2002 -1.84%2003 5.73%2004 4.16%2005 1.48%

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

Dispersion Trading

The last twelve months provided a few attractive opportunities for entering into index dispersion trades in response to periods of elevated implied correlation relative to realized correlation. The implied-realized correlation spread first peaked in April (Figure 9), as investors demanded a relatively high degree of systematic protection to hedge their portfolios from market retracements, such as the credit-driven downturn in the spring. However, there was an opportunity to profitably unwind the position shortly thereafter, when the equity market rallied into May and market-related anxiety dissipated.

Figure 8: 3-Month Implied and Realized Correlation (S&P 500)

Figure 9: Correlation Spread (3-Month) Versus Index Level

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There were several other opportunities to enter dispersion trades between August and October, again driven by elevated systematic anxiety resulting from the uncertain interest rate outlook. However, as the market recovered in November, reduced demand for market level protection provided an attractive exit point for dispersion trades, as implied correlation again retreated to levels below what had been realized.

Options with Weekly Expirations (“Weeklys”)

In October 2005, the CBOE launched index options with weekly expirations on the SPX and the OEX. Weeklys have similar contract specifications as other options on their respective underlying indexes. However, they are listed each Friday and expire the following Friday. No Weeklys are listed on the


January 10, 2006 8

Friday before the expiration for standard options. Weekly options on the SPX are European-style with AM settlement, while those on the OEX are American-style, with PM settlement.

In our opinion, Weeklys allow investors to more efficiently trade nearer-term volatility than traditional option contracts. Weeklys can provide additional flexibility if one wishes to express directional views on stocks or indices immediately prior to an upcoming catalyst.

Figure 10: Average Daily Volumes in Weekly Options SPX Weekly Options Average Daily Volume

Volume in Weeklys

Volume in Standard Options

% Volume in Weeklys

November 1,459 344,295 0.42%December 2,301 278,543 0.83%OEX Weekly Options Average Daily Volume

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% Volume in Weeklys

November 2,220 69,404 3.20%December 2,397 52,090 4.60%

Source: CBOE

While the average volume in Weeklys (Figure 10) currently represents a relatively small proportion of the total volume on standard option contracts (less than 1% market share for S&P 500 index options; less than 5% market share for S&P 100 options), their popularity might increase in the coming year.

Option Volumes Continue to Accelerate, Currently Stand at Record Levels

In 2005, the use of options as speculative investment vehicles or hedging instruments increased in popularity as the volume of total option contracts traded, as well as open interest, continued to exhibit remarkable growth. In fact, as Figure 11 illustrates, the total number of contracts traded on all U.S. options exchanges (including both calls and puts) increased from 392 million in 2003 to roughly 683 million contracts during 2005, a nearly 75% increase.

Figure 11: Total Monthly Volume for Options on S&P 500 Stocks Versus S&P 500 Constituent Share Volume

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2003 33 592004 44 842005 57 101


In addition, the total open interest of option contracts at the end of the year increased from roughly 59 million contracts in 2003 to about 101 million at 2005 end, a nearly 72% increase. On the other hand, total trading volume in shares of S&P 500 constituents generally remained flat over the same period.


January 10, 2006 9

Volatility Outlook for 2006

Potential Catalysts for Higher Volatility

Below we highlight several potential catalysts could lead to higher equity market volatility for the year ahead.

Credit Concerns Although credit spreads have tightened since their recent peak in April 2005, Lehman Brothers Credit Strategists are forecasting a widening of investment grade spreads by about 10 bps in 2006 on the back of lingering uncertainty over auto credit and increased LBO activity for the overall market2.

Historically, credit spreads and equity volatility tend to display a positive relationship, since both are viewed as risk metrics for an underlying security (Figure 12).

Figure 12: High-Grade Credit Spreads vs. Implied Volatility

Figure 13: High-Yield Credit Spreads vs. Implied Volatility

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This relationship has been weaker heading into year end, as credit spreads have continued to widen, whereas equity implied volatility has drifted lower. To the extent credit spread widening is driven by company-specific default concerns and event risks, as opposed to shareholder-friendly actions by management (share buybacks, special dividends etc), it could be a factor supporting the case for higher equity implied volatility in the year ahead.

Interest Rate Outlook Historically, the onset of a monetary tightening cycle has been a precursor to elevated equity volatility. However, during the most recent tightening cycle, the Fed has induced relatively low uncertainty by consistently raising interest rates at a “measured pace”. As the end of the rate hikes approaches, there is lack of consensus about the future direction of monetary policy and its ultimate impact on the economic climate.

Furthermore, the recent yield curve inversion (measured by the 10-year minus 2-year treasury yield differential) which occurred at the end of 2005, might lead investors to seek greater protection from a possible economic downturn, since inverted yield curves have historically tended to forecast recessions. Although the two most recent instances of yield curve inversion (1998 and 2000) coincided with rising

2 Please see “2006 Credit Outlook: Shifting Gears”, Lehman Brothers U.S. Credit Strategy, December 19, 2005 for further details.


January 10, 2006 10

equity volatility (Figure 14), it should be noted that a portion of the elevated volatility in these instances was driven by macroeconomic factors such as the Russian crisis in 1998 and the domestic stock market bubble in 1999 and 2000.

Figure 14: Equity Volatility Following Yield Curve Inversions

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Housing Market Weakness Strength in the domestic housing market has in large part supported consumption expenditure, with increasing levels of home equity extraction by home owners. Lehman Brothers economists forecast a cooling off in the housing market, with home prices remaining flat nationally and decreasing about 10% in nearly one third of the real estate market over 20063. A potential negative impact on economic growth could flow through to the stock market and cause options market participants to bid up implied volatility.

Energy Prices Although crude oil prices declined after their post-Katrina peak of nearly $70 per barrel, and fell to about $56 in mid-November, they have since rallied back above $60 at year end. While the economy has proven more resilient to this oil shock relative to expectations and consumer sentiment has not suffered significantly, persistently higher energy prices and their pass-through effects to core inflation and discretionary spending could factor in negatively toward the economy and corporate valuations.

Volatility Risk We highlighted above how the ongoing systematic sale of volatility has been a less profitable strategy in 2005 than it had been in prior years. If realized volatility increases in 2006, the premium for taking on volatility risk could revert from its current lows and implied volatility would likely rise.

Event Risks In addition to the factors above, several near-term events could serve as catalysts for heightened equity risk expectations and, thus, higher implied volatility for U.S. equity markets. These include another active hurricane season, uncertainty surrounding the outcome of the 2006 U.S. Congressional midterm elections, geopolitical concerns, avian flu, etc.

3 Please see “Outlook 2006: Plain Sailing – For Now”, Lehman Brothers Global Economics, December 12, 2005 for additional details.


January 10, 2006 11

Low Volatility Regime to Continue in 2006?

Of course, there are several factors that could enable the current low volatility regime to persist into 2006.

For example, if energy prices indeed stabilize or even trend downward - consumers’ financial condition and disposable income would likely improve aggregate sales figures and employment, which combined with the possible strength in corporate earnings, could result in lower risk expectations for the U.S. economy and the equity market in general. If anxiety surrounding a prolonged period of interest rate hikes dissipates, the current low implied volatility regime might remain intact.

Lehman’s Equity Strategy team has a 12-month price target of 1400 for the S&P 500, driven by expectations of a benign macroeconomic environment and more than $900 billion to be returned to investors in the form of cash. The 1400 price target implies a 12% gain in the S&P 500 Index for 2006. In addition, the strategy team anticipates multiple expansion in 2006, which would support equity market appreciation. Such a scenario would likely lead to a muted volatility environment.4

On balance however, it is our belief that the catalysts in favor of higher equity market volatility are likely to overweigh the events that would favor a continuation of the current low-volatility regime which began in earnest in the spring of 2003. Thus, we believe both implied and realized volatility for the equity market should trade, on average, a few points higher in 2006 versus 2005.

4 Please see “2006 – A Positive Year for the S&P 500”, Lehman Brothers U.S. Strategy, December 12, 2005 for further details.


January 10, 2006 12

Earnings Impact on Implied and Realized Volatility

Participants in volatility markets tend to focus on earnings events as significant catalysts for realized as well as implied volatility. In this study we explore volatility changes on and around earnings dates and discuss how investors can position themselves for these changes using option strategies.

Higher Realized Volatility When Earnings Are Reported

Since earnings day can be a potentially significant event for a company’s merits as an investment candidate, on average one would expect stocks to display a relatively higher degree of price variability when earnings figures are released than on other days. To test this hypothesis, we compare the distribution of close-to-close share price returns immediately following earnings announcements with the return distribution for other days. Our universe includes the constituents of the S&P 500 Index over the last two years. Our sample period begins in January 2004 and ends in December 2005.

We analyze press releases for each company in the S&P 500 on each quarterly earnings announcement for the past 8 quarters to determine the exact earnings report date and, more importantly, the release time. Thus, if a firm reported before the open or intra-day, the price impact was calculated using stock prices from the close of the day prior to the earnings report date to the close of trading on the earnings report date itself. On the other hand, if a company reported earnings after the close, we calculate the stock’s earnings reaction as the return from the close on the report date to the close of trading the on the following day.

As Figure 15 and Figure 16 illustrate, stock prices do, indeed, tend to exhibit relatively larger price moves following earnings announcements than they typically do on non-earnings-report days. The return distribution on earnings days has a relatively high standard deviation and lower predictability, along with the appearance of “fat tails” (Figure 15). On the other hand, stocks’ return distribution on non-earnings days tends to resemble a normal distribution, which has a relatively high concentration of returns centered at zero and a lack of fat tails.

Figure 15: 1-Day Return Distribution on Earnings Report Dates

Figure 16: 1-Day Return Distribution on Non-Earnings Report Dates

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This observation is also apparent in Figure 17, which plots the equal-weighted-average absolute 1-day price return for each constituent in the S&P 500 Index, before and after each earnings event included in our study. While stocks tend to move roughly 1% up or down on an average day, the magnitude of the change immediately following an earnings release is greater than 3%.


January 10, 2006 13

Figure 17: Absolute 1-Day Price Returns Around Earnings

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day after earnings announcement

Source: Source: Lehman Brothers, Bloomberg

Note: “t” represents the day when the market reacts to an earnings announcement. “t-2” is two days prior to the event, “t+2” is two days following the earnings reaction, and so on.

It is also interesting to note that there tends to be excess movement in stocks on the day after companies have reported earnings. We find that on the day after the initial reaction, stocks move about 1.5% in either direction. The magnitude of absolute price moves should be of interest to investors who have taken long gamma positions ahead of the earnings event, and would hope to realize a high level of 1-day volatility to compensate them for the theta decay.

Elevated Risk Expectations as Earnings Date Approaches

As an earnings date approaches, investors express their views on the expected price move in stocks by trading near-month contracts. Hence, we would expect short-term implied volatility to rise ahead of earnings. In Figure 18, we illustrate how one-month implied volatility5 changes as an earnings announcement approaches. We find that option market participants tend to bid up implied volatility more than two weeks ahead of the report date. However, after the event passes the option’s implied volatility declines, and then tends to stabilize at relatively low levels.

Figure 18: 1-month Implied Volatility Leading up to, and Following Earnings Announcements

22%

23%

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25%

26%

27%

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29%

t-20

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t-10 t-5 t

t+5t+10 t+15 t+20

Working Days

Avg

Impl

ied

Vol

Average Implied Vol

Implied Volatility picks up steadily as earnings approaches

Implieds drop sharply once catalyst has passed


5 For ease of calculation, we use the one-month constant-maturity implied volatility instead of the implied volatility corresponding to the traded constant-expiration contract. As the earnings date approaches, the increase in one-month implied volatility tends to be less than the increase in the volatility of the front-month contract. Thus, the change in one-month implied volatility likely understates the actual change experienced by investors through their contracts.


January 10, 2006 14

Since implied volatility tends to drift higher prior to earnings announcements, and the underlying stocks generally do not realize abnormally high volatility (except following the announcement), one possible way for investors to position for this would be to purchase ratio calendar spreads with net zero gamma and a positive vega. This position involves selling one shorter-dated option and buying a larger number of longer-dated options, and would benefit from a rise in implied volatility for the longer-dated option. Since the trade is constructed gamma neutral, it should remain relatively immune to changes in realized volatility, particularly if it is unwound shortly prior to the earnings date.

On the day prior to the earnings release, a potentially profitable strategy could be to sell delta-hedged calendar spreads with net positive gamma and negative vega exposure, and unwind the position two days later. This trade would benefit from the abnormally high volatility realized owing to the event (long gamma), and from the reversion in implied volatility once the catalyst has passed (short vega). Investors should note that the volatility patterns above are merely averages of a large sample of stocks, and idiosyncratic factors should be taken into account prior to initiating trades such as these. In addition, the success of the trade also depends on the relative impact of changing implied volatility on the two contracts having different maturities. Moreover, the expected profit potential can be negatively impacted by the options’ bid/ask spreads.

Expected Stock Price Reaction to Earnings Announcements

Having compared changes in implied and realized volatility leading up to an earnings event, we now discuss the earnings-related change in the one-day price move implied by the options market. As earnings season approaches, a stock’s front-month implied volatility tends to be higher than longer dated implied volatility. We can infer from this that the option market anticipates a potentially larger price move following the earnings announcement than what the stock typically realizes. (In this example, the expiration dates for both options fall after the next earnings date). We extract an option’s forward volatility from the 1-month and 2-month implied volatilities and use it to estimate of the “fair value” of non-event implied volatility. The 1-month volatility is considered to be comprised of the fair value volatility and an event volatility component. Once the embedded 1-day event volatility has been calculated, we can use it to estimate the expected value of the stock’s absolute return priced in by options market. Please see Appendix I: Forward Implied Volatility and 1-Day Expected Price Move.

In Figure 19, we examine how the 1-day event volatility behaves, on average, as an earnings date approaches. As the diagram illustrates, option market participants tend to begin pricing in a higher price move about two weeks prior to the event date. The 1-day forward volatility estimation steadily increases to its peak immediately prior to the earnings announcement. This pattern is similar to how one-month implied volatility changes ahead of earnings events.

Figure 19: Average Change in Options’ Implied 1-Day “Event Volatility” Ahead of Earnings

0%

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ied

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Avg 1-Day Event Vol

Market prices in highest 1-day event volatility on

earnings date



January 10, 2006 15

Next, we empirically examine the effectiveness of using event volatilities by plotting the 1-day event volatility implied by the options market against the actual price move realized following the earnings announcement. If options markets are indeed efficient, one would expect stocks that have historically exhibited relatively large reactions to earnings announcements and other potentially significant catalysts would exhibit high 1-day event volatility as well. On the other hand, one would expect stocks that have historically had relatively muted reactions to earnings announcements and other events are likely to have low 1-day event volatility.

In Figure 20, we empirically demonstrate that this hypothesis appears to be validated. The 1-day price move immediately following earnings announcement tends to be higher in cases when options market participants were implying a relatively high degree of 1-day event volatility ahead of earnings. Likewise, stocks that tend to exhibit minimal impact due to earnings announcements have had a relatively low degree of 1-day event volatility priced into their options immediately prior to the earnings report.

Figure 20: 1-Day Event Volatility Versus Absolute Return on Earnings

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s

Absolute Return on Earnings


Thus, identifying instances when event volatility is incorrectly priced would require one to analyze the behavior of a specific security during its earnings announcement period. This illustrates the difficulty in constructing systematically-based strategies by buying or selling a portfolio of options prior to earnings season. Several idiosyncratic variables, such as a stock’s implied volatility surface, existence or lack of recent earnings pre-announcements, as well as atypical fundamental catalysts highlight the need to examine event volatility estimates on a case-by-case basis.

Event Volatility Predicts Announcement Across Sectors

In Figure 21, we find that the volatility market’s prediction of a company’s expected absolute price move ahead of an event is relatively reliable, regardless of the sector to which the stock belongs. We find that stocks in the Information Technology, Consumer Discretionary and Industrials sectors tend to exhibit the largest returns (positive or negative) on their earnings announcement date. For these sectors, investors also price in the highest expected moves before the event. On the other hand, Telecommunication Services, Utilities, and Financials tend to exhibit relatively mild reactions to earnings events, and have lower event volatility priced in on average, ahead of their announcements.


January 10, 2006 16

Figure 21: Predictability of Earnings Impact Across GICS Sector

Sector Avg 1-Day Event Volatility

Absolute Return on Earnings

Information Technology 6.29% 5.12%Consumer Discretionary 4.54% 3.23%Industrials 3.40% 3.15%Health Care 4.29% 3.10%Consumer Staples 2.84% 2.73%Materials 3.76% 2.51%Energy 3.46% 2.05%Financials 2.55% 2.04%Utilities 2.77% 1.61%Telecommunication Services 2.90% 1.49%

MaterialsEnergy

Information Technology

Consumer DiscretionaryIndustrials

Health CareConsumer Staples

FinancialsUtilities

Telecommunication Services

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1-Day Implied Event Volatility

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etur

n on

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s


Predictability of Earnings Impact by Quarter

The option market does not appear to price in event risk across quarters in as uniform a manner (Figure 22). While changes in realized and implied volatility in response to Q4 earnings tended be lower than in other quarters, earnings announcements during the Q3 earnings season tend to price in the highest level of event risk. The stock price reaction to Q2 earnings, on the other hand, was the greatest across quarters, even though option market participants likely anticipated lower risk expectations in response to Q2 earnings relative to the Q3 earnings season.

Figure 22: Predictability of Earnings Impact by Quarter

Quarter Avg 1-Day Event Volatility

Absolute Return on Annoucement

Q1 3.90% 3.21%Q2 3.92% 3.44%Q3 4.48% 2.99%Q4 3.85% 2.84%


Conclusion

We examined how implied and realized volatility change heading into earnings announcements for constituents within the S&P 500 during the eight quarters since Q1 2004. While stocks tend to exhibit average absolute returns of over 3% following earnings announcements, their average price moves in the days leading up to the announcement were not significantly different from what they exhibited during periods void of significant catalysts.

On the other hand, stocks’ implied volatility tends to exhibit a gradual rise during the week or two leading up to a company’s earnings announcement date. In addition, we found that options tend to display significantly lower implied volatility following earnings announcements. We observed a similar pattern for the 1-day price move implied by the options market. We also found investors tend to effectively price in earnings event risk, as stocks that have tended to experience sharp price moves following prior earnings announcements tended to display relatively high earnings-specific implied volatility estimates. On the other hand, firms that typically incur relatively minor stock price reactions following earnings announcements generally display relatively low earnings-driven implied volatility expectations heading in to their announcements. Our study found that these relationships tend to be consistent across all sectors since Q1 2004.


January 10, 2006 17

Appendix I: Forward Implied Volatility and 1-Day Expected Price Move

We describe the methodology used for calculating an option’s forward volatility and the corresponding expected price move on its earnings announcement day. Theoretically, investors should price in the same event volatility using options having 1-month or 2-month expiration terms. However, the 1-month forward volatility between the first month and second month expirations6 does not include the embedded “earnings event risk”, and can be used to estimate the “fair value”, or non-event volatility (Figure 23).

Note that the 1-month forward volatility in 1 month’s time following the earnings announcement is only one possible estimate of base volatility and choosing an alternate metric would likely result in a different estimate of the expected stock price move. In addition, investors should consider stock-specific characteristics, and other potentially non-earnings-related catalysts during an options term, prior to selecting the term used to calculate the option’s base volatility.

Figure 23: Calculation of Forward Implied Volatility

Source: Lehman Brothers

Formally, using the fact that variances are additive,

Variance1m + Varianceforward = Variance2m, or

(IV1m)2 * T1 + (IVf)

2 * (T2-T1) = (IV2m)2 * T2,

where IV1m, IV2m and IVf are respectively the 1-month, 2-month and forward volatilities. T1 and T2 are the number of days to the 1-month and 2-month maturities respectively.

Using the forward volatility as a base volatility and decomposing the 1-month variance into a base variance and an additional variance on account of the event,

(IVf)2 * (T1-1) + (IVevent)

2 * 1 = (IV1m)2 * T1,

where IVevent is the annualized event volatility. The annualized volatility is converted to a 1-day event volatility for estimating the absolute value of the expected stock price movement.

6 The constant-maturity implied volatilities we consider do not correspond to an actual expiration on a traded contract. However, the process for calculating forward volatility would be similar when using implied volatility with constant expiration dates.

Earnings Announcement

One Month Maturity

Two Month Maturity

One Month Implied Volatility (IV1m), T1

Two Month Implied Volatility (IV2m), T2

Forward Implied Volatility (IVf)


January 10, 2006 18

Assuming stock returns are normally distributed, the expected absolute return can be described by a half-normal distribution, which is similar to a normal distribution, although the negative half of the density function is overlapped onto the positive half (Figure 25).

Figure 24: Theoretical Distribution of Price Returns (Normal)

Figure 25: Theoretical Distribution of Absolute Returns (Half-Normal)

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

Standard Deviations

Prob

abili

ty D

ensi

ty

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

Standard Deviations

Prob

abili

ty D

ensi

ty

Source: Lehman Brothers Source: Lehman Brothers

Hence, the probability density function of a half-normal distribution is,

P(x) = ( )

−

−−2

1

2

1

2exp12

dayday IV

x

IVπ, for x >= 0

where x is the absolute 1-day return and IV1-day is the event implied 1-day volatility.

The expected value of this distribution is,

E(x) = ( )∫∞

−−

−

02

1

2

1

2exp12 dxIV

xx

IV daydayπ

Since the quantity within the integral is the differential of ( ) ( )

−−

−− 2

1

2

21

2expday

day IV

xIV , we have

E(x) = ( ) ( )

0

21

2

21

1

2exp*12

∞−

−−

−

dayday

day IV

xIV

IVπ

Therefore, the expected absolute 1-day return implied by the market is

E(absolute 1-day return) = dayIV −12π


January 10, 2006 19

Appendix II: 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.


January 10, 2006 20

Figure 26: Energy Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolOXY 32% 2% 39% WFT 32% 2% 35% WMB 33% -3% 39% BR 16% -21% 46%WFT 32% 1% 35% OXY 32% 1% 39% XTO 37% -2% 44% COP 26% -5% 38%BJS 32% 1% 34% HAL 35% 1% 43% COP 26% -1% 38% WMB 33% -4% 39%NBR 32% 1% 36% NBR 32% 1% 36% BHI 29% -1% 34% VLO 38% -4% 44%AHC 36% 1% 42% BJS 32% 1% 34% DVN 33% -1% 42% EP 31% -3% 31%

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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3-Month Put-Call Skew (20 Delta)

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

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Energy

OIH

XLE

XNG

OSX

IYE

XOI

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 2.5

XNG

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IYE

Energy

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OSX

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

12-Month - 3-Month Term Spread

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OSX

Energy

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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.



January 10, 2006 21

Figure 27: Materials Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolATI 40% 3% 37% ATI 40% 3% 37% EC 15% -7% 48% LPX 28% -5% 22%

NEM 35% 3% 34% ROH 23% 3% 24% X 39% -3% 39% PTV 22% -5% 38%AA 26% 1% 18% FCX 35% 3% 35% EMN 24% -2% 25% MON 30% -4% 30%

FCX 35% 1% 35% ECL 19% 2% 16% MON 30% -2% 30% TIN 25% -4% 24%HPC 31% 1% 31% NEM 35% 2% 34% LPX 28% -2% 22% X 39% -4% 39%



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


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


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






January 10, 2006 22

Figure 28: Industrials Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolAW 36% 3% 26% ITT 24% 3% 31% DOV 19% -1% 18% NAV 35% -4% 34%MAS 24% 2% 24% CTAS 21% 2% 19% ROK 24% -1% 19% R 29% -2% 30%LUV 29% 1% 25% LUV 29% 2% 25% APCC 33% -1% 29% PBI 16% -2% 15%TYC 25% 1% 21% AW 36% 2% 26% HON 21% -1% 21% HON 21% -2% 21%FDX 22% 1% 20% UTX 18% 1% 17% R 29% -1% 30% PCAR 24% -2% 20%



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


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Capital Goods

XAL

CYC

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






January 10, 2006 23

Figure 29: Consumer Discretionary Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolMYG 38% 5% 23% DG 26% 4% 22% DCN 55% -12% 96% DCN 55% -14% 96%DG 26% 2% 22% MAT 27% 2% 20% GM 74% -10% 55% BBY 31% -9% 43%

SHW 23% 2% 24% AN 22% 2% 16% VC 61% -9% 67% IPG 28% -8% 24%COH 32% 2% 31% IGT 26% 1% 21% OMX 32% -4% 40% SHLD 38% -7% 32%HLT 26% 2% 34% KRI 24% 1% 26% RBK 4% -4% 5% RBK 4% -7% 5%



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Media

Automobiles & Components

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


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






January 10, 2006 24

Figure 30: Consumer Staples Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolRAI 25% 2% 22% CL 17% 1% 12% ABS 31% -3% 34% CVS 26% -3% 30%TSN 28% 2% 28% RAI 25% 1% 22% WAG 20% -2% 21% ADM 27% -2% 27%KR 24% 1% 18% ACV 23% 1% 17% STZ 27% -1% 31% STZ 27% -2% 31%

WMT 19% 1% 17% CCE 22% 0% 17% CVS 26% -1% 30% MO 23% -2% 22%UST 24% 1% 24% SWY 27% 0% 24% PG 16% -1% 14% WMT 19% -2% 17%



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Food, Beverage &Tobacco

Food & StaplesRetailing

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Household &Personal Products

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


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-1%

0%

1%

1%

2%

2%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



0.00.20.40.60.81.01.21.4


XLP



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






January 10, 2006 25

Figure 31: Health Care Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolUNH 25% 1% 24% BOL 31% 6% 31% GDT 18% -6% 41% BSX 33% -11% 25%HUM 32% 1% 39% BCR 24% 3% 18% WYE 18% -3% 20% PFE 21% -10% 32%SGP 25% 1% 23% HSP 25% 3% 23% PFE 21% -2% 32% SGP 25% -6% 23%AGN 25% 1% 24% UNH 25% 3% 24% BDX 19% -2% 22% BMET 29% -5% 23%WLP 25% 1% 25% BAX 21% 2% 18% STJ 27% -2% 25% MEDI 35% -5% 26%



15%

20%

25%

30%

35%

40%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



0%

5%

10%

15%

20%

25%

30%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Pharmaceuticals & Biotechnology

IBB

Health Care Equipment & Services

PPH

XLV

IYH

BBH

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


-2.0 -1.5 -1.0 -0.5 0.0 0.5


XLV


PPH

IBB

IYH

BBH

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


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

2%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-1.0-0.50.00.51.01.52.0


BBH


IBB

IYH

PPH

XLV

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






January 10, 2006 26

Figure 32: Financials Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolFNM 31% 2% 27% JP 19% 4% 16% ET 32% -3% 29% ET 32% -6% 29%AIV 21% 2% 23% FNM 31% 4% 27% ASN 20% -3% 18% MCO 21% -4% 17%AOC 31% 2% 20% WM 24% 3% 21% SPG 23% -2% 21% FITB 23% -3% 21%UNM 27% 1% 23% UNM 27% 2% 23% SNV 20% -2% 17% VNO 19% -3% 18%FII 21% 1% 16% AOC 31% 2% 20% FRE 21% -2% 19% NFB 20% -3% 17%



10%

15%

20%

25%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



0%

5%

10%

15%

20%

25%

30%

35%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-1.5 -1.0 -0.5 0.0 0.5 1.0

IYRICFIYFXLFXBDReal EstateBanksBKXRKHDiversified FinancialsInsurance

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


-2.0 -1.0 0.0 1.0 2.0 3.0 4.0

Diversified Financials

IYFBanksInsurance

BKX

XLFReal Estate

XBD

IYRRKHICF

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


-1%

-1%

0%

1%

1%

2%

2%

3%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-2.5-2.0-1.5-1.0-0.50.00.51.0

RKH

ICF

Banks

Insurance

Diversified Financials

Real Estate

BKX

XLF

IYR

XBD

IYF

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






January 10, 2006 27

Figure 33: Information Technology Sector Volatility Snapshot (as of January 6, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolSUNW 46% 7% 33% SEBL 17% 5% 5% PMTC 38% -9% 31% PMTC 38% -11% 31%BMC 35% 6% 34% BMC 35% 4% 34% LU 36% -8% 34% MU 34% -9% 31%GTW 57% 5% 50% UIS 50% 4% 61% CIEN 57% -6% 53% ERTS 33% -5% 31%NVDA 43% 4% 34% GTW 57% 3% 50% JDSU 49% -4% 55% AV 38% -5% 30%HPQ 29% 1% 23% MOLX 27% 3% 20% ANDW 34% -3% 24% LU 36% -5% 34%



15%

20%

25%

30%

35%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



0%

5%

10%

15%

20%

25%

30%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



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

BDHIGMSemiconductors & Semiconductor EquipmentMSHSoftware & ServicesIGWSMHIGNIYWXLKXCISOXIGVSWHWMHTXXTechnology Hardware & EquipmentIAH

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


-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0

XCIIAHTXXSemiconductors & Semiconductor EquipmentSWHWMHSMHSoftware & ServicesIYWIGVTechnology Hardware & EquipmentMSHIGNIGWSOXIGMBDHXLK

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


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-2.0-1.00.01.02.03.04.0

WMHIAHXCIIGWSMHIYWIGVSemiconductors & Semiconductor EquipmentIGNTechnology Hardware & EquipmentSWHTXXSOXSoftware & ServicesIGMBDHMSHXLK

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






January 10, 2006 28

Figure 34: Telecommunication Services Sector Volatility Snapshot (as of January 6, 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 28% 2% 23% S 28% 3% 23% CZN 18% -1% 17% CTL 19% -2% 19%

BLS 18% 1% 15% Q 39% 2% 32% T 14% -1% 13% T 14% -1% 13%Q 39% 0% 32% VZ 19% 1% 15% CTL 19% -1% 19% CZN 18% -1% 17%VZ 19% 0% 15% BLS 18% 1% 15% AT 19% 0% 18% AT 19% 0% 18%AT 19% 0% 18% AT 19% 0% 18% VZ 19% 0% 15% BLS 18% 1% 15%



10%

15%

20%

25%

30%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-5%

0%

5%

10%

15%

20%

25%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-0.4 -0.3 -0.2 -0.1 0.0 0.1

IYZ

TelecommunicationServices

TTH

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


-1.0 -0.5 0.0 0.5 1.0 1.5


IYZ

TTH

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


-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-0.20.00.20.40.60.81.0


IYZ

TTH

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






January 10, 2006 29

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized VolED 18% 1% 15% ED 18% 2% 15% PCG 18% -3% 20% AES 26% -6% 28%

CNP 24% 1% 23% DYN 46% 2% 38% XEL 17% -2% 17% D 18% -4% 22%PGN 14% 1% 14% PPL 20% 1% 24% PNW 13% -2% 19% CEG 21% -4% 32%TXU 32% 1% 44% SO 16% 1% 14% EXC 24% -1% 25% PCG 18% -3% 20%KSE 16% 0% 17% NI 22% 1% 21% AYE 26% -1% 26% AEE 15% -3% 16%



10%

15%

20%

25%

30%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



0%

5%

10%

15%

20%

25%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



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

UTH

XLU

Utilities

UTY

IDU

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


-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

UTH

UTY

IDU

XLU

Utilities

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


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

2%

Jan-0

5

Feb-05

Mar-05

Apr-05

May-05

Jun-0

5Ju

l-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05



-1.5-1.0-0.50.00.51.01.52.0

XLU

Utilities

IDU

UTH

UTY

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






January 10, 2006 30

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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options strategy monthly - 2006 - low volatility in the 7th inning? housing market, credit markets...

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