Dual Strategy Equity Derivatives Fund January 2017

Prepared by George G. Namur, Ph.D.

January 2017

Dual Strategy

Equity Derivatives Fund

Confidentiality

Dual Strategy Equity Derivatives Fund2

This information is privileged and confidential. This

presentation includes descriptions of methodologies and

concepts derived through substantial research and

development efforts. No part of this presentation may be

reproduced by any means or transmitted without the written

permission of Dr George G. Namur.


The Fund in a Snapshot

Options Strategy

Quantitative Volatility Strategy

Dual Strategy Structure and Performance

Highlights of Private Placement Memorandum

Highlights of Prospective Organization

Bios

Contact Information

Appendices

Overview



The FCA-regulated London based fund will combine two complementary

strategies to generate high returns while limiting volatility

Long-Biased Options Strategy


Equity options primarily The strategy serves to de-risk the options strategy

• The two strategies are loosely correlated (correlation of 0.115)*

• The combination features a barbell strategy where more weight is allocated to

the lower risk strategy* Monthly returns from Jan 2012 to Dec 2016 were used in computations



A Complimentary Combination

Options Strategy Quantitative Volatility

Strategy

Up Markets

Theoretical Allocation

(20-80)

• Relatively higher allocation

• Higher returns (leverage effect)

• Asymmetric exposure

• Likely to outperform

• Serves to reduce fund’s volatility

• Performance not correlated with

market

Sideways or

Down Markets

Theoretical Allocation

(10-90)

• Opportunities for bearish positions

• Using spreads rather than outright

positions may be appropriate

• Relatively higher allocation

• Investment opportunities (arbitrage,

relative value, carry trade, etc.)

unaffected by market direction

• Likely to outperform

Options Strategy



Long biased, long options (long risk)

Primarily US equities

Discretionary with quantitative enhancements

Stereoscopic approach (top down and bottom up)

Combines global macro, fundamental, technical and sentiment

analysis

Favors growth over value or income on the long side

Shorts broken companies, not “expensive” stocks

Options Strategy


Outright

positions and

spreads

Strategy allows for

leverage without

margin (no margin

call risk)

Bearish positions

consist of long

put option

positions

In bull markets,

most positions are

long call option

positions

Primarily long

option positions

Long-biased

Options

Strategy

Options Strategy - Overview

The main focus of the

strategy. Including

ADRs. Both the

stocks and their

corresponding

options need to be

liquid

Options on FX futures

(different from

options on FX)


FX Futures

Provided

options are

liquid and trade

electronically

Provided index

is optionable

and options are

liquid

Five different types of securities underlie the options

portfolio

1

5

4

Including

leveraged ETFs on

occasion2

3


• Focuses on major central banks and impact on liquidity

• Consequences on developed and emerging markets

• Analyses the trends in interest rates and currencies

Global Macro Analysis

• Both top down and bottom up approach

• Used to identify sectors and industries in favor

• Not meant to pick low valuation stocks

Fundamental Analysis

• Necessary for initiating and adding to positions

• Catches shorter term moves

• Helps with position sizing

Technical Analysis

• A very useful contrarian tool

• Signals meaningful at extremes

• Identifies changes in trend

Sentiment Analysis

The investment approach consists of a combination of

four key analyses


Top down and bottom up

Top Down

Global Macro

Central banks’ stanceLevel and trend of interest rates and

yield curveTrend for US dollar

Assessment of the Cycle

Expansion vs. contraction

Early cycle vs. late cycle

Bottom Up

Favor Growth Over Value

Avoid high dividend yielding stocks

Avoid focus on cheap stocks and value traps

Factor In Risk Appetite

Requires a read on investor psychology

Invest in market leadership

Capture Secular Themes

Like industrial and energy renaissance;

biotechnology advances; baby

boomers retirement, millennials’ trends

Respect Technicals

Charts identify trends before news are out

Additionally, the approach is stereoscopic and spans

several factors and sub-factors


Portfolio of Long Options

No margin or margin calls

No diminishing

returns adding new

names

Ability to add names

without deploying more cash

Tremendous flexibility in

risk management

Asymmetric exposure thanks to positive gamma

A portfolio of long options is typically characterized by

five advantages

Options Strategy

Risk



Risks inherent to a long-biased equity hedge

fund

Risks associated with

using options rather than stocks

The two main risk areas for this type of options

strategies have been identified

• Diversification (large number of positions)

• Proper rolling of options

• Need at times to park money in short term paper

• Options are inherently less liquid than stocks (lower volume, wider spreads)

• Limiting rolling of less liquid options as a roll involves crossing bid/ask twice

• Avoiding illiquid options and options on illiquid stocks/ETFs

• Focus on longer dated options for calls (LEAPs when available)

• Not a problem for puts as those are normally held for smaller periods

• Limit portfolio concentration across both names and sectors

• Switch to spreads during trendless markets


Time Decay

A risk mitigation strategy significantly reduces the impact of three important risks associated with using options rather than stocks

Leverage

Liquidity

Options Strategy

Portfolio of Stocks vs. Options



Portfolio of Options

Through rolling of options, one can add more names while keeping same delta exposure to existing names, and without using new money

Portfolio of Stocks

Adding more names involves either selling or reducing some existing positions or using fresh cash

The impact of diversification is greater in portfolios of

options than in portfolios of stocks

The more names, the better. The benefit of adding more names does not diminish

Beyond a certain number, adding more stocks will have almost no beneficial effect


Compare two hypothetical portfolios worth $100,000 each

Portfolio 1 consists of 500 shares of stock A and stock B, both trading at $100

Portfolio 2 consists of 100 ATM call options on stocks A and B of a certain

maturity, both trading at $5

Assume news causes stock A to rise 50% (to $150) and stock B to fall 50% (to

$50)

The post-news value of Portfolio 1 remains the same

The post-news value of Portfolio 2 rises dramatically (value of the options on B

fall to almost zero, but value of options on A go up to say $55) to over $500,000

Example: Portfolio of Options vs. Portfolio of Stocks


• Global macro analysis

• Assessment of the cycle

• Identification of right sectors

• Go long names in sectors in favor

• Go long names that have exceptional stories regardless of sector or industry

• Go short broken companies rather than expensive stocks

• Determine right options (strike, maturity) as well as initial sizing based in part on conviction level

• Driven by overall portfolio risk levels (VaR, actual losses)

• Rolling of options and position sizing to manage concentration risk and leverage

• Scenario analysis

Strategic Allocation

Stock Picking

Risk Management

Risk management, stock picking and strategic

allocation are at the heart of the investment process


The portfolio’s edge is materialized through both a

research alpha and a manufactured alpha


The GVV cost framework

The quantitative volatility strategy is based on a cost framework that

breaks down an option to it’s basic components. The framework is

referred as the Gamma VannaVolga (GVV) model.

The GVV cost framework offers a new approach to analyzing the

value of derivatives contracts by directly linking the daily cost of

options to their realized value.

Daily cost of options

Realized value of options


The quantitative volatility strategy primarily identifies relative value and carry trades using a Gamma1 Vanna2

Volga3 (GVV) cost framework

Innovative and proprietary

volatility framework

Identify relative value and carry trades

Profit from volatility-arbitrage opportunities

Limit and manage risk

Enhance valuation of derivative contracts

1. The Gamma of an option is the second derivative of the option value with respect to the underlying price

2. The Vanna of an option is the sensitivity of the option delta with respect to change in volatility; or alternatively, the sensitivity of Vega with respect to

the underlying price

3. The Volga of an option is the second derivative of the option value with respect to the underlying’s implied volatility


The GVV cost framework - Example

For instance, the model quantifies the daily costs of owning a 3-

month as well as a 1-year crash risk insurance on the S&P 500 Index

If the market prices these daily costs at the same level, the model

would suggest owning the 3-month crash risk while simultaneously

selling the 1-year crash risk

The aggregated position would therefore be long crash risk for free

(in the event of a crash, the 3-month crash protection would yield

much higher profits than the 1-year crash protection)


The GVV cost framework follows a well-defined, 3-step

investment process

DefineInvestment

Universe

ScanOpportunities

ManagePortfolio

Regional focus (United States)Operate under the new volatility

frameworkMonitor positions closely

Major index volatility products (e.g. VIX options, VIX futures, etc.)

Analyze volatility, skew, convexity signals

Manage risk constantly and efficiently

Liquid productsIdentify compelling relative value

and carry trade opportunitiesUtilize models to identify entry and

exit points

Minimum slippage Analyze risk/reward profileHold positions on average between 3

and 6 months

GVV Cost Framework


The GVV cost framework breaks down the option implied

volatility into three cost components

Cost of an Option is the Sum of:

Volatility Cost Skew (Crash) Cost Convexity Cost

Portion of the implied volatility that is a measure

of risk aversion or risk anticipation and insures

against a crash of the underlying

Portion of the implied volatility that represents the uncertainty of the volatility, i.e. the volatility of volatility

Portion of the implied volatility that reflects the expected variation in the returns of the underlying


Arbitrage is a central component of the strategy and

includes both pure and volatility opportunities

Pure Arbitrage

Opportunities

Volatility Arbitrage Opportunities

Relative Value Carry Trades

• One set of derivative assets will converge to another asset at a time in the near future

• Focus on trading pricing anomalies within different underlying indices, and/or related products such as the VIX vs. S&P 500 options

• Held until either the implied volatile levels converge or until maturity

• Arbitrage opportunities of secondary Greeks (Gamma, Vanna, Volga) across various maturities

• Focus on carry trades that will earn carry while limiting market exposure


Detailed examples of actual trades undertaken using

the GVV cost framework can be found in Appendix B

1,000 of SPX condor

Carry Trade (2)6.7%

per month9 months

$4.4MCrash Protection

(3A)10.2% 2 days

$4.3MRelative Value Arbitrage (1A)

30% 2 months

$7.4MRelative Value Arbitrage (1B)

18.5% 8 days

$3.6MCrash Protection

(3B)6.6% 2 days

Initial Investment Return Time Frame


Risk Management Tools



Common risks associated with the quantitative volatility

strategy are constantly monitored and carefully managed

No directional view on the broad

market Liquidity Risk

Redemption Risk

Herd Risk“Greek”

Risk

Counter-party Risk

Market Risk

Investments in liquid instruments such

that borrow/liquidity risk is diminished

Minimum exposure to simultaneous exit

from common strategies

Constant monitoring and

management of all options-

related risk exposures

Minimized by imposing

redemption frequency and

sufficient redemption notice

period and by investing in

liquid instruments

- Engagement into listed trades,

when possible, in order to minimize

counter-party risk

- Benefits from the cross margining

provided by banks on given volatility

products to reduce costs where

possible

Dual Strategy Structure and

Performance


Dual Strategy Portfolio Allocation and Guidelines


Dynamic allocation between the two strategies at the manager’s

discretion

More weight is assigned to the Quantitative Volatility strategy

consistent with the barbell approach that allocates more funds to the

lower risk strategy

Allocation is a function of market conditions. Adverse markets would

make the options strategy relatively riskier, so risk parity would guide

the manager to reduce the allocation to this strategy to below the base

case

Dual Strategy Historical Performance


The performance statistics in the next several slides are obtained by

combining those from an options portfolio, run by the fund’s

prospective manager and those from a portfolio using the quantitative

volatility strategy

This quantitative volatility strategy is used on the equity derivatives

desk of a major bank in NY and run by one of the fund’s prospective

portfolio managers

To turn the desk’s P&L into returns, it was assumed that the desk in NY

is working with a constant $100 million monthly budget. The

quantitative strategy’s returns are therefore obtained by dividing the

actual monthly P&L numbers by a constant $100 million

Variations of Strategy Allocations with Key Metrics*


Combination 1(Low Allocation)

Combination 2(Base Allocation)

Combination 3(High Allocation)

Weight of Options Strategy 10% 15% 20%

Weight of Quantitative Volatility

Strategy90% 85% 80%

Best Month 10.04% 14.45% 18.86%

Worst Month -6.75% -7.14% -7.55%

CAGR 25.89% 29.65% 33.29%

Correlation with S&P 500 0.55 0.46 0.56

Standard Deviation 10.81% 14.46% 18.30%

Sharpe Ratio 2.20 1.88 1.68

Sortino Ratio 3.30 3.15 2.90

* Monthly returns from Jan 2012 to Dec 2016 were used in computations

Correlation* of the Dual Strategy with the S&P 500


Up Months Down Months

Combination 1 (10-90) 0.42 0.16

Combination 2 (15-85) 0.42 0.15

Combination 3 (20-80) 0.41 0.15

* Monthly returns from Jan 2012 to Dec 2016 were used in computations


Average returns, standard deviation and CAGR increase while the

Sharpe ratio decreases as more and more weight is allocated to the

options strategy

Index futures and futures options markets are deeper than single

stock options’; larger AUM likely to favor higher allocation to

quantitative volatility strategy

Correlation coefficient between the two strategies is 0.115

3-month Treasury bills used as a proxy for risk-free rate to compute

excess returns and Sharpe ratio

Dual Strategy Assumptions and Interpretations

Highlights of Private Placement

Memorandum


Fee Structure


• Assets under management will be subject to an annual management

fee of 2%

• The fee will be charged on the first business day of every quarter on

the net assets under management as of the last business day of the

previous quarter

• Profits will be subject to an annual performance fee of 20%.

• The fee will be charged on the first business day of every quarter on

the previous quarter profits in excess of the high-watermark, after

the management fees are deducted

Key Investment Terms


Minimum Initial Investment $300,000

Minimum Additional Investment $100,000

Lockup Period 2 Years

Lockup Type Soft

Lockup Redemption Penalty 15%

Redemption Date Quarterly

Redemption Notice 120 Days

Highlights of Prospective Organization



General Partners

CIO

Portfolio Management

Traders / Analysts

Investment Committee

Head of Marketing

CRO

Risk Management

COO

External Advisory

Board

A management committee oversees four core functions and is supported by an external advisory board

Service Providers


Investment Manager Company

UBS London

(Prime Broker)

PWC London

(Auditor)

HSBC London

(Banker)

Dechert London

(Law Firm)

Citco London

(Administrator)

FCA

(Regulator)


Investors

Limited Partners

Fund Entity

General Partners Investment Manager (London LLC)

Master Fund

(Cayman Islands LLP)

Feeder Fund

(Delaware LLP)

US Investors

Feeder Fund

(Cayman Islands Company)

Non-US Investors

Investment Advisor (Cayman Islands LLC)

Fund Structure

Transactions under Master-Feeder Structure


US Investors will invest in the US feeder fund, while non-US and

tax-exempt US investors will invest in the offshore feeder fund

The master fund will pool the investments from both feeder funds

and the investment manager company will manage the assets

directly from the master fund

Management fees will be collected by the investment manager

company and performance fees will be collected by the investment

advisor company

Net profit will be distributed to each feeder fund separately

Distribution of Fees and Capital Gains


U.S. Feeder Fund Offshore Feeder Fund

Master Fund

Investment Advisor

Company

General Partners

Capital GainsCapital Gains

Performance

Fees

Performance

Fees

Investment Manager

Company

Management

Fees

Management Fees

Net Expenses

The Master-Feeder structure significantly increases tax

efficiency for all parties


• Investments in the fund will not be treated as investments in a Passive Foreign Investment Company, which has significantly higher tax rates

• No double-taxation US Investors

• Avoid US tax regulations

• AnonymityNon-US Investors

• No US self- employment tax

• Non-US general partners will not be subject to US income tax if they pass the “presence test”

General Partners


Communication

Open channels of communication with investors

Detailed monthly report with administrator-provided NAV

Transparency

Critical factor for success

With General Partners as well as Limited Partners

Risk

We are in risk business, so prudent risk is embraced

First rule to making money is to not lose money

Trading to make money as opposed to trading to not lose money

Idea Generation

Ideas start with PM (with trader and analysts)

Major calls go through Investment Committee

External Advisory Board provides a fresh perspective

Four cornerstones govern the fund’s philosophy


Factors Affecting Choice of Locations

Credibility

Availability of talent

Cost of business, including taxation

Convenience of time zone relative to trading hours

1

2

3

4

Switzerland

Two potential fund locations were identified and

assessed based on specific business factors

Key Pros Key Cons

US

• Perfect time

zone

• Best talent

pool

• High corporate taxes

• Increasingly tough regulatory environment

UK

• Second best

location for

hedge fund

• Good talent

pool

• Trading

hours

• Uncertainty

over taxation

Fund Manager’s Bio



Professor of Finance at the Olayan School of Business at the American

University of Beirut. Teaches finance classes including “Hedge Fund

Strategies”, “Derivative Securities” , “Investment Management”, and

“Valuation Methods”

Teaches CFA Level I, II, and III classes related to Fixed Income

Portfolio Management, Derivatives, Alternative Investment, Risk

Management and Quantitative Methods as well as a FRM class on

Quantitative Analysis at the Institute for Financial Analysts (IFA)

Two decade experience trading various markets (currencies, equities,

fixed income and commodities). Stock picking skills based on

stereoscopic analysis

Dr. George G. Namur


In-depth expertise in derivatives and risk management

Affiliated with the Gerson Lehrman Group as an expert on

commodities, resources and financial services

Previously CEO and Managing Partner at Vermont Harding

Management Ltd, an alternative asset management firm based on

systematic foreign exchange trading

Dr. George G. Namur


He started Namur Capital Management in 2001, a wealth management

and advisory firm. He managed clients money as Separately Managed

Accounts. He focused on global macro trends and his trading was

driven by his macro views, including market risk appetite. He

supplemented his fundamental analysis with technical, cyclical as well

as sentiment analysis

Formerly General Manager at Shell Trading (previously Coral Energy)

in Houston, managing energy-related assets worth several billion

dollars. Introduced the concept of front office risk management.

Developed a portfolio of synthetic power assets allowing the firm to

become a major player in electricity trading

Dr. George G. Namur


Worked at the prestigious Capital Market Risk Advisors in New York

where he advised Wall Street firms, banks and asset managers on risk

management and derivative valuation. Experience at CMRA inculcated

a risk-based discipline in trading and asset management

Trained in derivatives at Chase Manhattan Bank, now JPMorgan Chase

in New York

MBA in Analytical Finance and Business Economics from the

University of Chicago Booth School of Business, and Ph.D. in Civil

Engineering from the University of Michigan, Ann Arbor

Fluent in Arabic and French

Dr. George G. Namur

Quantitative Volatility Portfolio

Manager’s Bio



Senior equity derivatives trader for a major bank in New York

Joined the bank in 2000 and has been managing the risk on the S&P

500 index options portfolio since 2004

Main responsibilities include market making, customer facilitation and

proprietary trading as well as overseeing risk on other derivative

products

Helped develop volatility marking methodologies that analyze relative

value opportunities in secondary risks

MS in Financial Engineering degree from Columbia University and

MBA in Finance from the City University of New York

Contact Information



For further enquiries, please contact

George G. Namur, Ph.D.

[email protected]

+1 (917) 421-9866

+961 (71) 100977

Contact Information

mailto:[email protected]

Appendix A

Historical Performance of Dual Strategy



Combination 1 – Monthly Returns

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD

2012 5.88% 5.19% 1.21% -1.92% 2.52% 2.11% 0.44% 3.96% 3.73% -3.34% 6.67% 4.40% 34.95%

2013 5.70% -1.98% 0.39% 4.87% 6.92% 0.66% 4.62% 1.01% 3.96% 4.73% 4.40% 1.14% 42.66%

2014 3.96% 6.85% -3.03% -1.92% 4.15% 1.37% 1.33% 2.26% -1.13% 2.28% 0.00% -1.61% 15.00%

2015 1.60% 2.61% 2.43% -0.26% 4.31% -0.30% 9.07% -6.75% 0.76% 10.04% 2.51% -0.27% 27.72%

2016 -2.26% -0.95% 1.42% 2.85% 3.30% -2.86% 5.01% 0.65% 4.20% -1.02% 0.05% 1.16% 11.82%


Combination 1 – Monthly Returns vs S&P 500

-10%

-5%

0%

5%

10%

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6

Combination 1 S&P 500


Combination 1 – VAMI Curve vs S&P 500

1,000

1,500

2,000

2,500

3,000

3,500

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6


VA

MI

Combination 2 - Monthly Returns



2012 6.90% 7.19% 2.02% -2.26% 2.52% 2.66% 0.78% 5.01% 4.29% -5.44% 8.23% 4.39% 41.81%

2013 5.61% -2.40% 0.07% 6.64% 11.42% 0.25% 5.52% 0.87% 5.83% 5.60% 5.39% 0.92% 55.45%

2014 4.09% 8.13% -4.83% -3.35% 5.28% 0.99% 1.27% 2.78% -2.09% 2.45% -0.19% -1.54% 12.94%

2015 1.79% 2.98% 2.62% -1.07% 5.20% -0.68% 11.50% -7.14% -0.66% 14.45% 3.09% -0.27% 34.58%

2016 -4.40% -2.44% 2.06% 3.10% 4.66% -3.80% 6.65% 0.50% 5.44% -2.08% -0.92% 0.91% 9.34%



-10%

-5%

0%

5%

10%

15%

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6



* VAMI: Value Added Monthly Index, or the value of $1,000 invested at beginning of time series.

Combination 2 – VAMI Curve vs S&P 500V

AM

I

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6



Combination 3 – Monthly Returns


2012 7.92% 9.19% 2.83% -2.60% 2.53% 3.22% 1.13% 6.05% 4.86% -7.55% 9.78% 4.39% 48.82%

2013 5.52% -2.83% -0.26% 8.41% 15.92% -0.17% 6.42% 0.73% 7.71% 6.48% 6.37% 0.69% 68.96%

2014 4.22% 9.41% -6.63% -4.78% 6.41% 0.61% 1.22% 3.30% -3.05% 2.63% -0.38% -1.47% 10.82%

2015 1.98% 3.35% 2.81% -1.89% 6.08% -1.05% 13.92% -7.53% -2.08% 18.86% 3.67% -0.28% 41.47%

2016 -6.54% -3.93% 2.70% 3.36% 6.02% -4.73% 8.29% 0.35% 6.68% -3.13% -1.89% 0.65% 6.75%



-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6



Combination 3 – VAMI Curve vs S&P 500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6


VA

MI

Appendix B

Examples of Actual Trades Using the GVV

Framework



Example 1A – Relative Value Volatility Arbitrage

DescriptionRelative value strategy on the VIX vs. the S&P 500 (SPX) Indices

(Category I Type Strategy) implemented on October 2, 2012

MotivationThe VIX December 2012 Gamma Breakeven (or Volatility) levels were higher by

12 volatility points compared to the Volga Breakeven (or Convexity) levels of the

January 2013 SPX options

Theoretical

Underlying

The recently popular VIX and VXX options along with the options on realized

variance can be broken down to their Gamma, Vanna and Volga components and

ultimately compared to plain vanilla SPX options. This ultimately results in

identifying the most of efficient product that creates gamma and thus relative

value opportunities amongst related volatility products


Example 1A – Relative Value Volatility Arbitrage

Trade

• Sell 10,000 VIX Dec 21 calls vs. 19 in the VIX December Futures for 2.2

• Sell 1,000 SPX Dec 1400/1500 strangles to buy 2,000 Jan 1225/1575

strangles with a reference price of 1440 in the SPX futures

• The trade was theta neutral

• Unwind Date: December 12, 2012

Results

• SPX strategy is worth 0.9 cents vs. 1440 yielding profit of USD 1.44 Million

• VIX calls are worth 0.1, yielding profit of USD 2.1 Million

• VIX Futures hedge was 500 Futures at 19 vs. 15.5 resulting in a loss of USD -

2.25 Million

• Net Profit: USD 1.29 Million (30 % return on a USD 4.3 Million initial

investment over a period of 2 months)


Example 1B – Relative Value Volatility Arbitrage

DescriptionRelative value strategy on the VIX vs. the S&P 500 (SPX) Indices (Category I Type

Strategy) implemented on November 12, 2012

MotivationThe VIX January 2013 Gamma Breakeven (or Volatility) levels were lower by 10

volatility points compared to the Volga Breakeven (or Convexity) levels of the

February 2013 SPX options


Example 1B – Relative Value Volatility Arbitrage

Trade

• Buy 1,500 SPX Feb 1275/1325/1450 put trees vs. 1374 in the SPX

December Futures for 39.7

• Buy 5,000 VIX Jan 20 calls vs. 20.4 in the VIX December Futures for 2.9

• Unwind Date: November 20, 2012

Results

• SPX Feb put tree is worth 45.5 vs. 1374 Dec Futures level resulting in a

profit of USD 870,000

• VIX Jan 20 calls vs. 18.65 is worth 1.97 resulting in a net profit of USD

500,000

• Net Profit: USD 1.37 Million (18.5% return on a USD 7.4 Million

investment over a period of 8 days).


Example 2 – Carry Trade

Description• This carry trade on the S&P 500 index (Category II) was followed from the

first part of 2012 until September 2012

Motivation• For the first part of 2012 the implied daily Vanna Breakeven (or Skew) for SPX

1-month options were 1% while the realized Vanna was around 20 bps


Example 2 – Carry Trade

Trade• During this period a rolling strategy of the 1 m 103/97/94/90 put condor

would focus on earning carry on a daily basis for flat gamma and Volga

Results

• This strategy was successfully followed until Sep 2012 when the skew levels

flattened to approximately 50 bps per day

• Overall this strategy would yield approximately USD 30,000 per 1,000 of SPX

condors (6.7% return per month consecutively for a period of 12 months)


Example 3A – Crash Protection Strategy

DescriptionThis crash protection strategy using VIX Index Options (Category I Type Strategy) was implemented on April 11, 2013

MotivationThe VIX May 2013 Gamma Breakeven (or Volatility) levels were lower by 15


June 2013 VIX options


Example 3A – Crash Protection Strategy

Trade

• Buy 10,000 VIX May 2013 14-15 Strangle vs. 14.5 reference

• Sell 10,000 VIX June 2013 15-16 Strangle vs. 15.5 reference

• Structure Fair Value: 1.025

• Unwind Date: April 15, 2013

Results

• As promised, the strategy performed very well in a sharp move down,

yielding almost 50% within a period of 2 days without any decay. The fair

value of the structure on the unwind date was 0.55

• Net Profit: USD 0.45 Million profit on a USD 4.4 Million investment

(10.2% return in 2 business days)


Example 3B – Crash Protection Strategy

DescriptionIn the same spirit, the previous strategy could have also been implemented by trading S&P 500 Index Options (April 11, 2013)

MotivationThe VIX May 2013 Gamma Breakeven (or Volatility) levels were lower by 15


June 2013 VIX options


Example 3B – Crash Protection Strategy

Trade

• Buy 2,000 SPX Dec 13 1475 Puts and Sell 1675 Calls vs. 1591 reference

• Sell 2000 SPX Dec 14 1400 Puts and Buy 1750 Calls vs. 1591 reference

• Structure Fair Value: 19.6 (Dec 14 Puts over)

• Unwind Date: April 15, 2013

Results

• The fair value of the structure on the unwind date was 18.5

• Net Profit: USD 240,000 on a USD 3.6 Million initial investment (6.6%

return in 2 business days)

Dual Strategy Equity Derivatives Fund January 2017

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