Enhanced Call Overwriting*
Systematically overwriting the S&P 500 with 1-month at-the-money calls, rebalanced on a monthly basis at expiration, outperformed the S&P 500 Index during our sample period (1996 – 2005). This “base case” overwriting strategy also generated superior risk-adjusted returns versus the index.
Overwriting portfolios with out-of-the-money calls tends to outperform at-the-money overwriting during market rallies, but provides less protection during market downturns. However, out-of-the money overwriting also results in relatively higher return variability and inferior risk-adjusted performance.
During the sample period, overwriting the S&P 500 with short-dated options, rebalanced more frequently, outperformed overwriting with longer-dated options, rebalanced less frequently. We discuss possible explanations for these performance differences.
We find that going long the market during periods of heightened short-term anxiety, inferred from the presence of relatively high S&P 500 1-month at-the-money implied volatility, has, on average, been a winning strategy. To a slightly lesser extent, having relatively less exposure to the market during periods of complacency – or relatively low implied market implied volatility – was also beneficial.
We create an “enhanced” overwriting strategy – whereby investors systematically overwrite the S&P 500 or Nasdaq 100 with disproportionately fewer (more) calls against the indices when risk expectations are relatively high (low).
Our enhanced overwriting portfolios handily outperformed the base case overwrite portfolios and the respective underlying indices, on an absolute and risk-adjusted basis. For example, the average annual return for the S&P 500 enhanced overwriting portfolio from 1997 – 2005 was 7.9%, versus 6.6% for the base case overwrite portfolio and 5.5% for the S&P 500 Index.
Overwriting with fewer calls when implied volatility is rich, and more calls when implied volatility is cheap, could improve the absolute and risk-adjusted performance of index-oriented overwriting portfolios.
This goes against the conventional tendency for investors to sell calls against their positions when implied volatility is high.
*Renicker, Ryan and Devapriya Mallick., “Enhanced Call Overwriting.”, Lehman,Brothers Global Equity Research Nov 17, 2005.
Identifying Rich and Cheap Implied VolatilityRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
DSD-INT 2018 Simulating sediment transport in irrigation systems using Delft3...Deltares
Presentation by Shaimaa Abd Al-Amear Theol, IHE Delft Institute for Water Education, The Netherlands, at the Delft3D - User Days (Day 1: Hydrology and hydrodynamics), during Delft Software Days - Edition 2018. Monday, 12 November 2018, Delft.
Convertible Bonds and Call Overwrites - 2007RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Identifying Rich and Cheap Implied VolatilityRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
DSD-INT 2018 Simulating sediment transport in irrigation systems using Delft3...Deltares
Presentation by Shaimaa Abd Al-Amear Theol, IHE Delft Institute for Water Education, The Netherlands, at the Delft3D - User Days (Day 1: Hydrology and hydrodynamics), during Delft Software Days - Edition 2018. Monday, 12 November 2018, Delft.
Convertible Bonds and Call Overwrites - 2007RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Stochastic Local Volatility Models: Theory and ImplementationVolatility
1) Hedging and volatility
2) Review of volatility models
3) Local volatility models with jumps and stochastic volatility
4) Calibration using Kolmogorov equations
5) PDE based methods in one dimension
5) PDE based methods in two dimensions
7) Illustrations
Short Variance Swap Strategies on the S&P 500 Index Profitable, Yet RiskyRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
The Lehman Brothers Volatility Screening ToolRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Realized and implied index skews, jumps, and the failure of the minimum-varia...Volatility
1) Empirical evidence for the log-normality of implied and realized volatilities of stock indices
2) Apply the beta stochastic volatility (SV) model for quantifying implied and realized index skews
3) Origin of the premium for risk-neutral skews and its impacts on profit-and-loss (P\&L) of delta-hedging strategies
4) Closed-form solution for the mean-reverting log-normal beta SV model
5) Optimal delta-hedging strategies to improve Sharpe ratios
6) Argue why log-normal beta SV model is better than its alternatives
Style-Oriented Option Investing - Value vs. Growth?RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable E...Volatility
1) Analyze the dependence between returns and volatility in conventional stochastic volatility (SV) models
2) Introduce the beta SV model by Karasinski-Sepp, "Beta Stochastic Volatility Model", Risk, October 2012
3) Illustrate intuitive and robust calibration of the beta SV model to historical and implied data
4) Mix local and stochastic volatility in the beta SV model to produce different volatility regimes and equity delta
Options on the VIX and Mean Reversion in Implied Volatility Skews RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Pricing Exotics using Change of NumeraireSwati Mital
The intention of this essay is to show how change of numeraire technique is used in pricing derivatives with complex payoffs. In the first instance, we apply the technique to pricing European Call Options and then use the same method to price an exotic Power Option.
Efficient Numerical PDE Methods to Solve Calibration and Pricing Problems in ...Volatility
1) Volatility modelling
2) Local stochastic volatility models: stochastic volatility, jumps, local volatility
3) Calibration of parametric local volatility models using partial differential equation (PDE) methods
4) Calibration of non-parametric local volatility volatility models with jumps and stochastic volatility using PDE methods
5) Numerical methods for PDEs
6) Illustrations using SPX and VIX data
An Approximate Distribution of Delta-Hedging Errors in a Jump-Diffusion Model...Volatility
1) Analyse the distribution of the profit&loss (P&L) of delta-hedging strategy for vanilla options in Black-Scholes-Merton (BSM) model and an extension of the Merton jump-diffusion (JDM) model assuming discrete trading and transaction costs
2) Examine the connection between the realized variance and the realized P&L
3) Find approximate solutions for the P&L volatility and the expected total transaction costs
4) Apply the mean-variance analysis to find the trade-off between the costs and P&L variance given hedger's risk tolerance
5) Consider hedging strategies to minimize the jump risk
Achieving Consistent Modeling Of VIX and Equities DerivativesVolatility
1) Discuss model complexity and calibration
2) Emphasize intuitive and robust calibration of sophisticated volatility models avoiding non-linear calibrations
3) Present local stochastic volatility models with jumps to achieve joint calibration to VIX options and (short-term) S&P500 options
4) Present two factor stochastic volatility model to fit both the short-term and long-term S&P500 option skews
Identifying Rich and Cheap Implied Volatility - Equity OptionsRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Stochastic Local Volatility Models: Theory and ImplementationVolatility
1) Hedging and volatility
2) Review of volatility models
3) Local volatility models with jumps and stochastic volatility
4) Calibration using Kolmogorov equations
5) PDE based methods in one dimension
5) PDE based methods in two dimensions
7) Illustrations
Short Variance Swap Strategies on the S&P 500 Index Profitable, Yet RiskyRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
The Lehman Brothers Volatility Screening ToolRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Realized and implied index skews, jumps, and the failure of the minimum-varia...Volatility
1) Empirical evidence for the log-normality of implied and realized volatilities of stock indices
2) Apply the beta stochastic volatility (SV) model for quantifying implied and realized index skews
3) Origin of the premium for risk-neutral skews and its impacts on profit-and-loss (P\&L) of delta-hedging strategies
4) Closed-form solution for the mean-reverting log-normal beta SV model
5) Optimal delta-hedging strategies to improve Sharpe ratios
6) Argue why log-normal beta SV model is better than its alternatives
Style-Oriented Option Investing - Value vs. Growth?RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable E...Volatility
1) Analyze the dependence between returns and volatility in conventional stochastic volatility (SV) models
2) Introduce the beta SV model by Karasinski-Sepp, "Beta Stochastic Volatility Model", Risk, October 2012
3) Illustrate intuitive and robust calibration of the beta SV model to historical and implied data
4) Mix local and stochastic volatility in the beta SV model to produce different volatility regimes and equity delta
Options on the VIX and Mean Reversion in Implied Volatility Skews RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Pricing Exotics using Change of NumeraireSwati Mital
The intention of this essay is to show how change of numeraire technique is used in pricing derivatives with complex payoffs. In the first instance, we apply the technique to pricing European Call Options and then use the same method to price an exotic Power Option.
Efficient Numerical PDE Methods to Solve Calibration and Pricing Problems in ...Volatility
1) Volatility modelling
2) Local stochastic volatility models: stochastic volatility, jumps, local volatility
3) Calibration of parametric local volatility models using partial differential equation (PDE) methods
4) Calibration of non-parametric local volatility volatility models with jumps and stochastic volatility using PDE methods
5) Numerical methods for PDEs
6) Illustrations using SPX and VIX data
An Approximate Distribution of Delta-Hedging Errors in a Jump-Diffusion Model...Volatility
1) Analyse the distribution of the profit&loss (P&L) of delta-hedging strategy for vanilla options in Black-Scholes-Merton (BSM) model and an extension of the Merton jump-diffusion (JDM) model assuming discrete trading and transaction costs
2) Examine the connection between the realized variance and the realized P&L
3) Find approximate solutions for the P&L volatility and the expected total transaction costs
4) Apply the mean-variance analysis to find the trade-off between the costs and P&L variance given hedger's risk tolerance
5) Consider hedging strategies to minimize the jump risk
Achieving Consistent Modeling Of VIX and Equities DerivativesVolatility
1) Discuss model complexity and calibration
2) Emphasize intuitive and robust calibration of sophisticated volatility models avoiding non-linear calibrations
3) Present local stochastic volatility models with jumps to achieve joint calibration to VIX options and (short-term) S&P500 options
4) Present two factor stochastic volatility model to fit both the short-term and long-term S&P500 option skews
Identifying Rich and Cheap Implied Volatility - Equity OptionsRYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Options Strategy Monthly - 2006 - Low Volatility in the 7th Inning? Housing M...RYAN RENICKER
Actionable trade ideas for stock market investors and traders seeking alpha by overlaying their portfolios with options, other derivatives, ETFs, and disciplined and applied Game Theory for hedge fund managers and other active fund managers worldwide. Ryan Renicker, CFA
Why Emerging Managers Now? - Infusion Global Partners WhitepaperAndrei Filippov
Traditional asset classes appear to offer uninspiring beta returns at present, and recent years’ hedge fund returns have disappointed both in magnitude and diversification benefits, likely reflecting capacity pressures associated with the concentration of AUM and inflows with larger funds. We argue that, by contrast, Emerging hedge funds offer a rich opportunity set with far fewer capacity issues where skilled managers with concrete competitive advantages in less efficient, smaller capitalization market segments can generate better, more sustainable and less correlated excess returns. Emerging managers do involve more investment and operational risk than larger peers; to that challenge we offer some suggestions on a thoughtful and rigorous approach to constructing an Emerging Managers allocation and balancing effective due diligence with scalability.
Hedge funds have been criticized for taking hefty fees without a performance to match. This presentation takes a look at the issue of hedge fund performance looking at both sides of the equation and evaluating how hedge funds fit into an investment portfolio.
Advisory to Financial Institutions on E-Mail Compromise Fraud SchemesRyan Renicker CFA
"The Financial Crimes Enforcement Network (FinCEN) is issuing this advisory to help financial institutions guard against a growing number of e-mail fraud schemes, in which criminals
misappropriate funds by deceiving financial institutions and their customers into conducting wire transfers.
This advisory also provides red flags—developed in consultation with the Federal Bureau of Investigation (FBI) and the U.S. Secret Service (USSS)—that financial institutions may use to identify and prevent such e-mail fraud schemes."
Source: FinCEN Advisory FIN-2016-A003, September 6, 2016
Attached for your reference are “Quick Tips” regarding methods one can use to minimize your becoming a victim of cyber crime while using social media.
You are encouraged to share these tips with your friends, family and co-workers.
Also included is this “smart card” for Twitter for increased security awareness.
UNCLASSIFIED - TLP: WHITE. TLP: WHITE information may be distributed without restriction, subject to copyright controls.
Source: FBI.
Attached for your reference are “Quick Tips” regarding methods one can use to minimize your becoming a victim of cyber crime while using social media.
You are encouraged to share these tips with your friends, family and co-workers.
Also included is this “smart card” for LinkedIn for increased security awareness.
UNCLASSIFIED - TLP: WHITE. TLP: WHITE information may be distributed without restriction, subject to copyright controls.
Source: FBI.
Attached for your reference are “Quick Tips” regarding methods one can use to minimize your becoming a victim of cyber crime while using social media.
You are encouraged to share these tips with your friends, family and co-workers.
UNCLASSIFIED - TLP: WHITE. TLP: WHITE information may be distributed without restriction, subject to copyright controls.
Source: FBI.
Attached for your reference are “Quick Tips” regarding methods one can use to minimize your becoming a victim of cyber crime while using social media.
You are encouraged to share these tips with your friends, family and co-workers.
Also included is this “smart card” for Google Plus for increased security awareness.
UNCLASSIFIED - TLP: WHITE. TLP: WHITE information may be distributed without restriction, subject to copyright controls.
Source: FBI.
Attached for your reference are “Quick Tips” regarding methods one can use to minimize your becoming a victim of cyber crime while using social media.
You are encouraged to share these tips with your friends, family and co-workers.
Also included is this “smart card” for Facebook for increased security awareness.
UNCLASSIFIED - TLP: WHITE. TLP: WHITE information may be distributed without restriction, subject to copyright controls.
Source: FBI.
FinCEN Statement on Providing Banking Services to Money Services BusinessesRyan Renicker CFA
"FinCEN Statement on Providing Banking Services to Money Services Businesses. The Financial Crimes Enforcement Network (“FinCEN”), as the agency primarily responsible for administering the Bank Secrecy Act, is issuing this Statement to
reiterate expectations regarding banking institutions’ obligations under the Bank Secrecy Act for money services businesses.
Money services businesses (“MSBs”), including money transmitters important to the global flow of remittances, are losing access to banking services, which may in part be a result
of concerns about regulatory scrutiny, the perceived risks presented by money services business accounts, and the costs and burdens associated with maintaining such accounts. "
National CFA Charterholder Compensation Survey 2015Ryan Renicker CFA
Some insights into the value of successfully completing (and retaining) the CFA Charter.
Source: CFA Societies Canada - 11 August 2015
https://www.cfasociety.org/saskatchewan/JobLine1/CFA%20Charterholder%20Compensation%20Survey%20-%20Summary%20-%20FINAL%20v2.pdf
Institutional lnvestor Magazine’s Alpha Hedge Fund Rankings - Top Ranked Ana...Ryan Renicker CFA
"Who is the best catering to hedge funds? To answer that question, Alpha turned to its sister publication, Institutional Investor, which for more than 34 years has surveyed money managers of all types...
Credit Market Imperfection and Sectoral Asymmetry of Chinese Business CycleRyan Renicker CFA
This paper analyzes the role of credit market imperfection and sectoral asymmetry as a
means through which shocks to the real economy are propagated and amplified. Drawing
on firm-level data to calibrate the model, our simulations capture two key stylized facts of
the Chinese economy: that credit constraints are more binding in nontradable sectors than
in tradable industries and that output volatility is much greater in China than in industrial
economies. We find that the driving force behind our simulation results is strongly related
to the on-uniform nature of credit market imperfections in China and their implications
for resource allocation and the way in which the economy reacts to shocks. Correctly
capturing these macro-financial interactions are essential to understand the dynamic behavior of the Chinese economy.
Prepared by Yuanyan Sophia Zhang (IMF)
Stock Pickers Guide, May 2002, CSFB Quantitative & Equity Derivatives Str...Ryan Renicker CFA
Source: CSFB Quantitative & Equity Derivatives Strategy, May 2002.
Survey CSFB fundamental research analysts on what they believe are the factors to watch when picking stocks.
Survey is segmented by sectors and industries.
Provide this analysis to complement our quantitative analysis.
We approach sectors and industries from both quantitative and qualitative perspectives and also list potential pitfalls to avoid.
Sectors highlighted in report include: Consumer Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information Technology, Materials, Telecom and Utilities.
Using Volatility Instruments As Extreme Downside Hedges-August 23, 2010Ryan Renicker CFA
“Long volatility” is thought to be an effective hedge against a long equity portfolio, especially during periods of extreme market volatility. This study examines using volatility futures and variance futures as extreme downside hedges, and compares their effectiveness against traditional “long volatility” hedging instruments such as out-of-the-money put options. Our results show that CBOE VIX and variance futures are more effective extreme downside hedges than out-of-the-money put options on the S&P 500 index, especially when reasonable actual and/or estimated costs of rolling contracts have taken into account. In particular, using 1-month rolling as well as 3-month rolling VIX futures presents a cost-effective choice as hedging instruments for extreme downside risk protection as well as for upside preservation.
Hedge Fund Predictability Under the Magnifying Glass:The Economic Value of Fo...Ryan Renicker CFA
The recent financial crisis has highlighted the need to search for suitable models forecasting hedge fund performance.
This paper develops and applies a framework in which to assess return predictability on a fund-by-fund basis.
Using a comprehensive sample of hedge funds during the 994-2008 period, we identify the fraction of funds in each style that are truly predictable, positively or negatively, by macro variables.
Out-of-sample, exploiting predictability can be di¢ cult as estimation risk and model uncertainty lead to imprecise fund forecast.
Moreover, in our multi-fund setting, investors face a trade-o¤ between unconditional and predictable performance, as strongly predictable funds may exhibit low unconditional mean.
Nevertheless, a strategy that combines forecasts across predictors circumvents all these challenges and delivers superior performance.
We highlight the statistical and economic drivers of this performance, especially in periods when predictor values strongly depart from their long run means.
Finally, we use one such period, the 2008 crisis, as a natural out-of-sample experiment to validate the robustness of our findings.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the what'sapp information for my personal pi vendor.
+12349014282
2. Elemental Economics - Mineral demand.pdfNeal Brewster
After this second you should be able to: Explain the main determinants of demand for any mineral product, and their relative importance; recognise and explain how demand for any product is likely to change with economic activity; recognise and explain the roles of technology and relative prices in influencing demand; be able to explain the differences between the rates of growth of demand for different products.
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the what'sapp contact of my personal pi vendor
+12349014282
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the what'sapp contact of my personal pi merchant to trade with
+12349014282
1. Elemental Economics - Introduction to mining.pdfNeal Brewster
After this first you should: Understand the nature of mining; have an awareness of the industry’s boundaries, corporate structure and size; appreciation the complex motivations and objectives of the industries’ various participants; know how mineral reserves are defined and estimated, and how they evolve over time.
The Rise of Generative AI in Finance: Reshaping the Industry with Synthetic DataChampak Jhagmag
In this presentation, we will explore the rise of generative AI in finance and its potential to reshape the industry. We will discuss how generative AI can be used to develop new products, combat fraud, and revolutionize risk management. Finally, we will address some of the ethical considerations and challenges associated with this powerful technology.
The WhatsPump Pseudonym Problem and the Hilarious Downfall of Artificial Enga...
Enhanced Call Overwriting (2005)
1. November 17, 2005
Enhanced Call Overwriting
Ryan Renicker, CFA
1.212.526.9425
Superior risk-adjusted returns for at-the-money overwriting vs. long only. Systematically
ryan.renicker@lehman.com overwriting the S&P 500 with 1-month at-the-money calls, rebalanced on a monthly basis at
expiration, outperformed the S&P 500 Index during our sample period (1996 – 2005). This
Devapriya Mallick
1.212.526.5429 “base case” overwriting strategy also generated superior risk-adjusted returns versus the
dmallik@lehman.com index.
Risk/return profile changes when overwriting with out-of-the-money calls. Overwriting
portfolios with out-of-the-money calls tends to outperform at-the-money overwriting during
market rallies, but provides less protection during market downturns. However, out-of-the-
money overwriting also results in relatively higher return variability and inferior risk-adjusted
performance.
Short-dated overwriting has outperformed long-dated overwriting. During the sample
period, overwriting the S&P 500 with short-dated options, rebalanced more frequently,
outperformed overwriting with longer-dated options, rebalanced less frequently. We discuss
possible explanations for these performance differences.
“Enhanced” Overwriting. We find that going long the market during periods of heightened
short-term anxiety, inferred from the presence of relatively high S&P 500 1-month at-the-money
implied volatility, has, on average, been a winning strategy. To a slightly lesser extent,
having relatively less exposure to the market during periods of complacency – or relatively
low implied market implied volatility – was also beneficial. We create an “enhanced”
overwriting strategy – whereby investors systematically overwrite the S&P 500 or Nasdaq
100 with disproportionately fewer (more) calls against the indices when risk expectations
are relatively high (low).
Our Enhanced Overwriting Strategy performed the best. Our enhanced overwriting
portfolios handily outperformed the base case overwrite portfolios and the respective
underlying indices, on an absolute and risk-adjusted basis. For example, the average annual
return for the S&P 500 enhanced overwriting portfolio from 1997 – 2005 was 7.9%, versus
6.6% for the base case overwrite portfolio and 5.5% for the S&P 500 Index.
Goes against conventional tendency to overwrite when volatility is rich. Overwriting with
fewer calls when implied volatility is rich, and more calls when implied volatility is cheap,
could improve the absolute and risk-adjusted performance of index-oriented overwriting
portfolios. This goes against the conventional tendency for investors to sell calls against their
positions when implied volatility is high.
Lehman Brothers does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of
interest that could affect the objectivity of this report.
Customers of Lehman Brothers in the United States can receive independent, third-party research on the company or companies covered in this report, at no cost to them,
where such research is available. Customers can access this independent research at www.lehmanlive.com or can call 1-800-2LEHMAN to request a copy of this research.
Investors should consider this report as only a single factor in making their investment decision.
PLEASE SEE ANALYST(S) CERTIFICATION AND IMPORTANT DISCLOSURES BEGINNING ON PAGE 12.
2. Equity Derivatives Strategy | Enhanced Call Overwriting
Call Overwriting and the CBOE S&P 500 BuyWrite Index (BXM)
Call overwriting is a trading strategy whereby investors sell call options against their long position in
the underlying. Specifically, an overwriting investor sells a stock’s upside potential beyond the strike
price in exchange for the initial premium received from the short call transaction.
Investors typically overwrite their portfolios when they anticipate range-bound stock prices, wish to
hedge against a short-term market retracement, reduce the total volatility of their portfolios or to
enhance yield. Please see Appendix I: Call Overwriting in a Nutshell for additional details on the
call overwriting strategy.
Overwriting has become increasingly popular among equity investors. According to the Chicago
Board Options Exchange (CBOE), more than $13 billion has been recently allocated by asset
1
managers to buy-write investment products . This popularity has led the CBOE, in cooperation with
Standard & Poor’s, to develop the CBOE S&P 500 BuyWrite Index (BXM), which measures the total
return of a “covered call” strategy applied to the S&P 500 Index (SPX). Specifically, the BXM consists
of a hypothetical portfolio consisting of a long position in the SPX and a short 1-month at-the-money
SPX call, rebalanced on a monthly basis at expiration. See Appendix II: Description of the BXM
Index for further details. From January 1996 to September 2005, the BXM had an average annual
return of 9.6%, versus 8.6% for the S&P 500 Total Return Index (SPTR). The BXM also had consistently
lower realized volatility than the SPTR.
Figure 1: BXM – SPTR Relative Performance (Quarterly) Figure 2: BXM vs. SPTR Cumulative Performance
12%
300
8%
BXM Outperf orms 250
ance
4%
erform
200
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B M - S TR P
150
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X
100 BXM (Scaled)
-8% S&P 500 TR Index (Scaled)
BXM Underperf orms
50
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96
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04
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6 6 7 7 8 8 9 9 0 0 1 1 2 2 3 3 4 4 5 5
-9 -9 r-9 -9 r-9 -9 r-9 -9 r-0 -0 r-0 -0 r-0 -0 r-0 -0 r-0 -0 r-0 -0
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Source: Lehman Brothers, Bloomberg Source: Lehman Brothers, Bloomberg
Figure 3: BXM vs. SPTR Rolling 90-Day Realized Volatility Figure 4: BXM vs. SPTR Daily Return Distribution
1,000
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BXM
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BXM Index
800
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2.
2.
3.
3.
4.
4.
5.
5.
-7
-7
-6
-6
-5
-5
-4
-4
-3
-3
-2
-2
-1
-1
-0
Ja
Ja
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Ja
Ja
Ja
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Daily Return
Source: Lehman Brothers, Bloomberg Source: Lehman Brothers, Bloomberg
1
Growing Interest in BuyWrite Strategies, CBOE, September 2005.
November 17, 2005 2
3. Equity Derivatives Strategy | Enhanced Call Overwriting
BXM Risk / Reward Characteristics
A comparison of the Sharpe ratios demonstrates that the BXM has had superior risk-adjusted
performance relative to a traditional long-only position in the SPTR from January 1996 to September
2005. Over this time period, the BXM had a Sharpe ratio of 0.55, versus 0.31 for the SPTR. Although
the BXM outperformed the SPTR on an absolute basis, a substantial contribution to the superior risk-
adjusted performance can be attributed to the BXM’s markedly lower volatility. During the study period,
the beta of the monthly total returns of the BXM with respect to the SPTR was about 0.62, and about
2
77% of the variability in BXM’s returns could be explained by the SPTR (R = 0.77).
However, since the BXM portfolio incorporates a short call position on the underlying index, its relative
performance versus the SPX is largely dependent on the market’s direction.
In directionless or bear markets, overwritten portfolios are more likely to outperform the SPTR, since the
initial cash flow received from the short call position cushions the loss of the long underlying position.
The BXM tends to lag SPTR in bull markets, as the overwritten portfolio’s profit potential is capped on the
upside.
This pattern is even more pronounced if the BXM portfolio returns are calculated strictly between
monthly expiration dates (Figure 5). In this case, the relative performance of the BXM versus the SPTR is
almost entirely dependent upon the market’s performance for the month leading up to expiration.
However, if the overwritten portfolio is rebalanced between expiration dates, the relative performance
of the BXM versus the SPTR becomes dependent upon both the direction of the market (“delta effect”)
as well as the change in the implied volatility for the short call option (“vega effect”) (Figure 6).
Figure 5: BXM vs. SPTR Relative Return Comparison Figure 6: BXM vs. SPTR Relative Return Comparison
(Rebalanced at expiration.) (Rebalanced between expirations.)
12% 12%
pread
& 0 o l e rn p e d
8% 8%
B M- S P5 0T ta R tu S r a
B M- S P500 Total R turn S
4% 4%
e
0% 0%
-20% -16% -12% -8% -4% 0% 4% 8% 12% 16% 20% -20% -16% -12% -8% -4% 0% 4% 8% 12% 16% 20%
-4%
-4%
&
-8%
X
-8%
X
-12%
-12%
S&P 500 Total Re turn
S&P 500 Total Re turn
Source: Lehman Brothers Source: Lehman Brothers
For example, there are instances when the BXM underperforms the SPTR during market downturns
(Figure 6, bottom left quadrant, circles). In these cases, the increase in the value of the written call
option attributed to rising implied volatility (“vega”) overwhelmed the drop in its value due to the
market’s decline. Thus, investors short the calls would incur a loss at rebalance since they would have
to buy back the calls at a higher price than what they had originally sold them for when the trade was
initiated. On these 6 occasions, the SPTR was down an average of 3.7%, and the BXM
underperformed the SPTR about 1.1%, on average. On the other hand, the BXM occasionally
outperforms the SPTR by more than anticipated despite strong market performance (Figure 6, top right
quadrant). In these cases, the initial premium received and the decrease in the value of the call option
sold attributed to declining implied volatility was greater than the opportunity cost incurred when the
market rallied beyond the strike price.
November 17, 2005 3
4. Equity Derivatives Strategy | Enhanced Call Overwriting
Overwriting Scenarios
Does it then follow that overwriting 1-month at-the-money calls is the optimal covered call strategy?
Why not overwrite a position by shorting out-of-the-money calls, or sell longer-dated calls rebalanced
less frequently? In this section, we explore some of these overwriting alternatives and examine their
risk/reward characteristics.
Base Case
We analyze the absolute performance and risk/reward profile of systematically overwriting the SPX
with 1-month at-the-money calls, rebalanced at each sequential monthly option expiration date. The
2
study incorporates data from February 1996 to September 2005 .
The base case overwriting strategy outperformed the SPX in 63% of the 115 months included in our
study and outperformed the SPX by about 0.5% per year (Figure 9). In addition, the overwrite portfolio
tended to outperform (underperform) the market during market declines (rallies).
Figure 7: Base Case Overwrite vs. S&P 500 Index Performance Figure 8: Base Case Relative Performance vs. S&P 500 Index
250 15%
10%
Cumulative Performance
Overwrite - S&P 500 Return
200
5%
150
0%
100
-5%
50 Overw rite Portfolio (Scaled) -10%
S&P 500 (Scaled)
-15%
-
0%
5%
0%
%
%
%
%
0%
5%
10
15
20
-5
-2
-1
-1
96
97
98
99
00
01
02
03
04
05
b-
b-
b-
b-
b-
b-
b-
b-
b-
b-
S&P 500 Return
Fe
Fe
Fe
Fe
Fe
Fe
Fe
Fe
Fe
Fe
Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics
In addition, the overwrite portfolio fared better than the market on a risk-adjusted basis. Specifically,
the overwriting strategy yielded a Sharpe ratio of 0.30 versus 0.16 for the SPX. During the study
period, the beta of the monthly total returns of the base case overwrite portfolio with respect to the SPX
was about 0.56, and about 77% of the variability in the overwritten portfolio’s returns could be
2
explained by SPX returns (R = 0.77).
Figure 9: Base Case vs. S&P 500 Risk / Reward Characteristics
Base Case Base Case -
SPX
Overwrite SPX
Average Annual Return 7.5% 7.0% 0.5%
Annualized Excess Return 3.3% 2.8% 0.5%
Standard Deviation 11.1% 17.3% -6.2%
Sharpe Ratio 0.30 0.16
# Months Outperformed 72 43 29
% Months Outperformed 63% 37% 25%
Note: Annualized excess return is relative to 1-month LIBOR.
Source: Lehman Brothers, OptionMetrics
2
For simplicity, we exclude dividends and transaction costs in the return calculations of both the overwrite portfolio and
the S&P 500 Index and assume that all trades are executed at the close on expiration.
November 17, 2005 4
5. Equity Derivatives Strategy | Enhanced Call Overwriting
Altering “Moneyness” of Calls
In this section, we analyze the tradeoffs associated with overwriting the SPX with 2%, 5% and 8% out-
of-the-money calls, rebalanced at each monthly expiration date from January 1996 to September
2005.
Since a call option’s delta decreases as the strike price increases, and the underlying portfolio has a
delta of 1.0, the overwritten portfolio’s net delta approaches 1.0 as deeper out-of-the-money (OTM)
calls are sold against the underlying portfolio. This means that overwriting strategies incorporating out-
of-the-money calls tend to outperform at-the-money overwriting during market rallies, but provide less
protection during market downturns (Figure 10). This also results in higher return variability and lower
risk-adjusted performance (Figure 11).
Figure 10: Average Return During Market Rallies, Downturns Figure 11: Altering “Moneyness”: Sharpe Ratio Comparison
10% M arket Rallies
0.35
M arket Downt urns
8% 0 .3 0
0.30
6%
Average Monthly Return
0 .2 4 0 .2 4
4% 0.25
Sharpe Ratio
2% 0 .19
0.20
0 .16
0%
0.15
-2%
0.10
-4%
-6% 0.05
-8%
-
-10% Base Case 2%OTM 5%OTM 8%OTM S&P 500 Index
Base Case 2%OTM 5%OTM 8%OTM S&P 500 Index (ATM )
(ATM )
Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics
During our sample period, systematically overwriting the SPX with 5% OTM calls generated the highest
average annual return across the “moneyness” scenarios (Figure 12). In the environment we have
considered, 5% OTM overwriting allowed investors to participate in monthly market rallies during the
bull markets of 1996 – 2000 and 2003 – 2005, while retaining a slight downside hedge during the
bear market of late 2000 – early 2003. However, the volatility of portfolios overwritten with out-of-the
money calls was relatively high and the risk-adjusted returns of out-of-the-money overwriting became
less attractive as further out-of-the-money calls were sold against the underlying portfolio (Figure 11).
Figure 12: Base Case, OTM Overwriting vs. S&P 500 Risk / Reward Characteristics (1/96 – 9/05)
Base Case S&P 500
2% OTM 5% OTM 8% OTM
(ATM) Index
Average Annual Return 7.5% 7.2% 8.0% 7.5% 7.0%
Annualized Excess Return 3.3% 3.0% 3.8% 3.2% 2.8%
Standard Deviation 11.1% 12.6% 15.5% 16.7% 17.3%
Sharpe Ratio 0.30 0.24 0.24 0.19 0.16
# Months Overwrite Outperforms S&P 500 72 77 101 107 NA
% Months Overwrite Outperforms S&P 500 63% 67% 88% 93% NA
Source: Lehman Brothers, OptionMetrics
November 17, 2005 5
6. Equity Derivatives Strategy | Enhanced Call Overwriting
Altering Expiration Dates
We compare the risk/reward attributes of overwriting the SPX with at-the-money calls expiring in 1, 3
and 6 months, rebalanced at expiration every 1, 3 and 6 months, respectively. The short-dated
overwrite portfolios significantly outperformed the 6 month covered call strategy during this time period
(Figure 13). Below, we explore possible explanations for these performance differences.
“Trending Market” Bias. Portfolios overwritten with longer dated options, rebalanced less frequently,
may outperform shorter-dated overwriting strategies if markets are “choppy”. For example, assume
investor A overwrites the SPX with 1-month at-the-money calls, rebalanced once, for a combined two
month holding period. Investor B overwrites the SPX with 2-month at-the-money calls, and closes out this
position when the two-month options expire. If the market has a significant rally between the initial
trade date and 1 month from initiation, A realizes a loss and has to sell new, higher-strike calls for the
second month of the trade. If the market then reverts back to the level where it was trading at the
nd
beginning of the initial trade as of the end of the 2 month, the new calls expire worthless and A
retains the entire premium on the calls sold at the roll date. Thus, investor A’s total net profit equals the
sum of the premiums received on each of the two short call transactions, less the loss incurred due to
the market rally during the first month of the trade. Investor B, however, retains the entire premium
received from selling the 2-month calls, and does not incur the interim loss one month into the trade.
During our sample period, the loss due to rebalancing more frequently did not overwhelm the other
factors that contributed to the outperformance of the shorter-dated overwriting.
“Out-of-the-Money Rebalance” Bias. At each rebalance date, the new “at-the-money” call written
against the SPX is selected by choosing the listed option having the closest strike price above the
closing price of the SPX as of the close of trading on that day; for our base case portfolio, the calls
shorted were, on average, about 0.41% out-of-the-money (Figure 14). Since this option is typically out-
of-the-money at the trade initiation date, the value of the overwritten portfolio partially participates in a
rally of the underlying during the trade’s holding period, unlike a pure “at-the-money” overwrite
position. Thus, if the market has a series of monthly rallies between each roll date, the cumulative
impact of writing slightly out-of-the-money versus at-the-money calls is compounded.
Figure 13: One, Three, Six Month Overwrites vs. SPX Figure 14: % OTM for Each Monthly Rebalance (Base Case)
250
2.0%
u u tiv e fo m n e
C m la eP r r a c
200 1.5%
1.0%
% T o W nC ll
itte a
150
0.5%
100
OM n r
0.0%
1 M ont h
50 3 M o nt h -0.5%
6 M o nt h Ave r age % OTM = 0.41%
S&P 50 0 (1 M ont h) -1.0%
-
-1.5%
S 6
M 6
S 7
M 7
S 8
M 98
S 9
M 9
S 0
M 0
S 1
M 1
S 2
M 02
S 3
M 3
S 4
M 4
S 5
5
-9
-9
-9
-9
-9
-9
-9
-0
-0
-0
-0
-0
-0
-0
-0
-0
-0
-0
-
-
ep
ep
ep
ep
ep
ep
ep
ep
ep
ep
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
M
-2.0%
Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics
Rate of Time Decay. As an at-the-money option approaches expiration, the rate of time decay (theta)
tends to increase at an increasing rate, particularly during the last week or two heading into
expiration, other factors held constant. Thus, a relatively large proportion of the total time decay for an
at-the-money option occurs close to its expiration (a 6-month option retains a relatively high proportion
of its original “time value” even one month prior to expiration). Investors who sell options benefit if the
value of the option sold decreases more rapidly with the passage of time. Thus, the expected total
premium from selling six consecutive 1-month at-the-money calls (which have yet to undergo rapid time
decay) can be higher than that expected from selling one 6-month at-the-money call. This could have
contributed to the higher return for the short-dated call overwriting strategy versus overwriting with
longer-dated options.
November 17, 2005 6
7. Equity Derivatives Strategy | Enhanced Call Overwriting
Enhanced Overwriting
Since covered call strategies have a tendency to underperform traditional long-only strategies during
market rallies and outperform during market downturns, and since a relatively high proportion of
overwriting returns can be explained by movements in the underlying portfolio overwritten, overwriting
strategies that are dynamically rebalanced ahead of relatively large market rallies or downturns can
naturally enhance the returns generated. Generally speaking, a stock’s near-term implied volatility tends
to rise during market retracements and decline as the market rallies. This suggests option market
participants alter their short-term forecast of a stock’s future return uncertainty (risk) in response to recent
observations in the underlying stock’s movement.
3
During our sample period (1/97 to 9/05), the SPX was up in 55 months, and declined in 49
months. In the month following up months, the market declined 53% of the time. In the month following
down months, the market rallied in 59% of the months. Since risk expectations (measured by at-the-
money implied volatility) generally rose following these market downturns, the market had a tendency
to rally after spikes in implied volatility. In other words, it is likely that investors were already pricing in
their worst fears for the market after the market had declined, and tended to be rewarded for going
long the market when market fear was excessive. To a lesser extent, investors generally experienced
lower returns investing in the market when risk expectations were relatively low (complacency).
To illustrate this, we calculate the number of standard deviations (z score) 1-month at-the-money implied
volatility stood above or below the average 1-month at-the-money implied volatility for each index
during the year prior to each monthly rebalance date. Next, we calculate the return for each respective
index for each following month. As Figure 15 demonstrates, the SPX tended to rally during the month
following rebalance dates in which the market had relatively high implied volatility levels and, to a
lesser extent, decline the month following rebalance dates in which the market had relatively low
implied volatility levels. Specifically, when the z score was greater than 1 at the beginning of the
monthly rebalance, the SPX was up 80% of the time during the next month; when the z score was less
than -1, the SPX declined 61% of the time during the following month. Although less pronounced, this
pattern was also evident for the NDX, which was up 58% of the time during the month following
periods when the z score was greater than 1. When the z score was less than -1, the NDX declined
56% of the time during the subsequent month (Figure 16).
Based on these results, we conclude going long during periods of complacency has, on average, not
been a winning strategy during this time period. This phenomenon also tended to occur for the
Nasdaq 100 Index (NDX), although to a lesser extent.
Figure 15: Implied Vol. Z-Score vs. Next Month SPX Returns Figure 16: Implied Vol. Z-Score vs. Next Month NDX Returns
20% 30%
Nasdaq 100 Next Month Return
15%
20%
S&P 500 Next Month Return
10%
10%
5%
0% 0%
-5%
-10%
-10%
-20%
-15%
Low Risk Expectations High Risk Expectations Low Risk Expectations High Risk Expectations
-20% -30%
-5 -4 -3 -2 -1 0 1 2 3 4 5 -5 -4 -3 -2 -1 0 1 2 3 4 5
Im plied Vol. Z-Score Im plied Vol. Z-Score
Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics
November 17, 2005 7
8. Equity Derivatives Strategy | Enhanced Call Overwriting
Performance of Enhanced Overwriting for the S&P 500 and Nasdaq 100
Since the underlying market tended to rally following periods of heightened risk aversion and decline
following periods of low implied volatility, we test whether writing disproportionately fewer (more)
calls against the indices when risk expectations were high (low) would have generated superior
3
absolute and risk-adjusted returns versus the base case overwriting portfolio . We find that this
“enhanced overwriting strategy” handily outperformed the base case and the underlying indices, on an
absolute and risk-adjusted basis (Figure 17, Figure 18, Figure 19).
Figure 17: SPX Enhanced, Base Case Overwrite Portfolios vs. SPX Figure 18: NDX Enhanced, Base Case Overwrite Portfolios vs. NDX
250 500
Te ch "Bubble " Dynamic Overw rite
450 NDX
NDX Base Case Overw rite
200
400
um la e e rm n e
um la e e rm n e
C u tiv P rfo a c
C u tiv P rfo a c
350
150
300
100
250
200
50
Dynamic Overw rite (Scaled)
150
S&P 500 (Scaled)
Base Case Overw rite (Scaled)
- 100
7
8
9
0
1
2
3
4
5
7
8
9
0
1
2
3
4
5
7
8
9
0
1
2
3
4
5
7
8
9
0
1
2
3
4
5
l-9
l-9
l-9
l-0
l-0
l-0
l-0
l-0
l-0
l-9
l-9
l-9
l-0
l-0
l-0
l-0
l-0
l-0
9
9
9
0
0
0
0
0
0
9
9
9
0
0
0
0
0
0
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
n-
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ju
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Ja
Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics
In addition, the SPX enhanced overwriting strategy outperformed the base case SPX overwriting
strategy 36% of the time, performed in line 46% of the time and underperformed the base case only
18% of the time. The NDX enhanced overwriting strategy outperformed the base case NDX strategy
38% of the time, performed in line 37% of the time and underperformed the base case only 25% of the
time. These results imply that the conventional tendency for investors to sell calls against indices having
rich implied volatilities – in order to maximize the premium received – might not have been the optimal
strategy during the past 9 years, since the indices tended to rally following periods of heightened
implied volatility. Rather, it would have led to higher opportunity costs since overwriting investors – by
definition – would have sold away at least some of the underlying portfolio’s upside potential.
Figure 19: SPX, NDX Enhanced, Base Case Overwrite, Underlying Index Risk / Reward Comparison
SPX SPX Base NDX NDX Base
Enhanced Case SPX Enhanced Case NDX
Overwrite Overwrite Overwrite Overwrite
Average Annual Return 7.9% 6.6% 5.5% 9.8% 8.8% 7.1%
Annualized Excess Return 3.8% 2.5% 1.5% 5.7% 4.7% 3.0%
Standard Deviation 11.7% 11.5% 18.0% 19.8% 19.4% 32.5%
Sharpe Ratio 0.33 0.22 0.08 0.29 0.24 0.09
# Months Outperformed Index 67 67 NA 65 65 NA
Source: Lehman Brothers, OptionMetrics
3
In our enhanced overwrite portfolios, we write 0.75 calls against each respective underlying index at each rebalance
date if the 1-month at-the-money implied volatility of the index at this rebalance date is more than 1 standard deviation
above the average of where the 1-month implied volatility has traded on a daily basis during the prior year (z-score of
implied volatility > 1.0). We write 1.25 calls against the index at each rebalance date if the 1-month at-the-money
implied volatility of the index at this date is more than 1 standard deviation below the average of where the 1-month
implied volatility has traded on a daily basis during the prior year (z-score < -1.0). We write 1.0 calls against the index if
the 1-month implied volatility is within + or – 1 standard deviation of where it has traded during the 1 year prior to the
rebalance date. Since z-score calculations require 1 year of historical vol data, and our volatility database includes data
from 1996 – present, our sample period for the enhanced overwriting strategies begins January 1997.
November 17, 2005 8