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3


Shaun Wurzbach

4

Moderator

Vice President, Global Head of Financial Advisor Channel

S&P Dow Jones Indices

Shaun Wurzbach, vice president at S&P Dow Jones Indices, is responsible for financial advisor channel management globally. The financial advisor channel focuses on increasing financial advisors’ awareness of and preference for index-based solutions.

Shaun joined S&P Indices in 2007 as head of the program management office, where his goal was to improve operational efficiency, new index design and costing procedures, and link effective change management to S&P Indices’ index calculation engines. Shaun was then asked to lead the New York and London-based index calculation teams in 2008, culminating in a reorganizational effort that resulted in a combined, international index analysis and production group.

Prior to joining S&P Indices, Shaun was a U.S. Army officer with over 20 years of active duty service including combat operations as a reconnaissance platoon leader in Desert Shield and Desert Storm.

Shaun has a master’s degree from the University of Chicago Booth School of Business, and a bachelor’s degree from The United States Military Academy at West Point.


Berlinda Liu

5

Director, Index Research & Design


Berlinda Liu is director, Index Research and Design, at S&P Dow Jones Indices. Berlinda is responsible for quantitative index research and design covering volatility, commodity, and other derivative-based indices and strategies.

Berlinda joined Standard & Poor's in December 2007. Prior to S&P, she was an equity derivatives strategist at both Bear Stearns, London, and Credit Suisse, New York, where she joined as a business analyst.

Berlinda is a CFA charter holder. She holds a bachelor’s degree in international business management from Wuhan University of China, a master’s degree in information system management, and a master’s degree in computational finance from Carnegie Mellon University.


Natalia Bandera

6

Director

Barclays

Natalia Bandera is a Director in Equity and Funds Structured Markets team at Barclays. Based in New York, Ms. Bandera is responsible for structuring and developing new innovative equity product ideas, strategies and indices for institutional, private banking and investment advisor clients.

Ms. Bandera joined Barclays Capital in 2008 from Lehman Brothers where she structured and priced equity and cross-asset hybrid structured products for the firm’s institutional and private banking clients. She also spent 4 years at TD Securities trading interest rate derivatives.

Ms. Bandera holds a Master of Science in Financial Engineering from Columbia University (2007) and a Bachelor of Commerce from the University of Toronto (2002).


Paris Smith

7

Principal/Senior Trader

Wolverine Trading, LLC

Paris Smith is a Principal at Wolverine Trading and Co-Head of the Liquidity Desk. With an Index arbitrage background, he develops and implements new trading strategies for ETP's and futures in addition to being responsible for issuer relationships. The Wolverine Liquidity Desk makes markets in futures and ETP's in a wide range of products including currency, commodities, VIX, fixed income and domestic and international equities. Paris is a frequent speaker at industry conferences and roundtables focused on ETF pricing, trading, risk management and overall market structure. He has a vast knowledge of the overall ETF ecosystem from the issuers, investors and traders perspective. After having spent over a decade as an ETP market maker with Wolverine, he recently added “father with little sleep” to his resume.

Paris hold a Series 7 license and a BS Degree in Finance from the University of Illinois, Chicago.


J.J. Feldman

8

Portfolio Manager

Miracle Mile Advisors

J.J. has over a decade of experience in portfolio management, securities analysis, and corporate finance in the entertainment industry and currently serves on the firm’s Investment Committee and Risk Management Committee.

Prior to joining Miracle Mile Advisors, J.J. worked at a Los Angeles based investment advisory firm where he designed and implemented custom portfolio solutions for high net worth individuals, 401k plans, and institutions. J.J.’s responsibilities included portfolio construction, security analysis and selection, asset allocation, and short-term tactical trading. He also oversaw risk management and compliance functions.

Previously, J.J. served as a finance manager for a large talent agency in Los Angeles. He began his career at KPMG advising middle market companies on merger and acquisitions and valuations.

J.J. graduated Cum Laude and with Honors from the University of Miami with a degree in Finance.


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Indexology: Join the Conversation at

www.indexologyblog.com Identifying the Differences Between

VIX Spot and Futures

For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices. 10

TOOLS TO HEDGE EQUITY MARKET RISK

EVOLUTION OF EQUITY VOLATILITY AS AN ASSET CLASS

MAIN USES AND MISUSES OF VIX ETPS

01

02

03

04 TOOLS TO REDUCE HEDGING COST OF VIX ETPS

AGENDA


DECOMPOSE AND MANAGE EQUITY RISK

11

Source: S&P Dow Jones Indices. Chart is for illustrative purpose only.

Equity Risk

Company Specific Risk

Equity Market Risk

= +

Diversification

Among Stocks

Diversification

Across

Asset Classes

Equity Index

Put Options

VIX Derivatives:

VIX Futures

VIX Options

VIX ETPs


EVOLUTION OF EQUITY VOLATILITY AS AN ASSET CLASS

12

Source: S&P Dow Jones Indices. Charts are provided for illustrative purposes.

Instruments for trading volatility have evolved since early 1970’s.

2008: S&P

500® VIX

FuturesTM

Indices

Opens volatility

futures trading

to all investors

2000s: VIX®

Index,

VIX Futures

and Options

Constant vega

exposure

Listed contracts

Need for

managing roll

1990s:

Variance

Swaps

Constant

gamma

Linearly

changing vega

Convex

exposure

OTC only

1970s/1980s:

Listed Options

Operationally

difficult

Non-linear

decay

2009+:

Volatility

Strategies

Smart Beta

Alternative Beta

Hedging


THE VIX® INDEX

13

Source: Barclays Research. Data is current through December 31, 2013. Charts are provided for illustrative purposes. Past performance is no guarantee of future results. This chart may reflect hypothetical

historical performance. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance.

The CBOE Volatility Index® (VIX®) measures the market’s expectation of 30-day S&P

500 ® volatility based on prices of near term S&P 500 put and call options.

• Negatively correlated with the S&P 500.

• Correlation is convex: greater reaction to large decreases in the equity market than to

market increases.

0

10

20

30

40

50

60

70

1,000

1,500

2,000

2,500

3,000

3,500

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

VIX

Index

S&

P 5

00

TR I

ndex

S&P 500 TR IndexVIX Index

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

-10% -5% 0% 5% 10% 15%

Weekly Returns Comparison 1/5/2001 - 12/31/2013

S&P 500 TR Index

VIX

Index


BUT CONVEXITY FOR VIX FUTURES IS NOT FREE

14

VIX futures can lose money even if VIX does not change

• VIX futures are typically higher than VIX during stable or rallying markets and lose

value as they approach maturity.

• This “roll cost” is equivalent to the upfront premium for put options.

• Short-term VIX futures have more convexity and so higher cost (10% - 15% / month)

relative to mid-term VIX futures (2% - 5%) during rallying markets.

Upward Sloping Term Structure During Rallying Markets

Source: : Barclays Research. Charts are provided for illustrative purposes.


LEADING TO SIGNIFICANT DRAG OVER TIME

15

Source: S&P Dow Jones Indices. Data is current through December 31, 2013. Charts are provided for illustrative purposes. Past performance is no guarantee of future results. This chart may reflect

hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance.


STRATEGIES TO REDUCE HEDGING COST

16

Hedging Approach Risk

Alpha Overlay Alpha may not work during downturns

Selling Other Options Negative P&L in some scenarios

Just-in-Time Hedging Miss the next

risk flare

Cross-Asset Hedging Basis risk

Dynamic Asset Allocation Gap risk


VEQTOR - VOLATILITY EQUITY ALLOCATOR

17

VEQTOR Allocation Matrix

Implied Volatility

Trend

Realized Volatility

Equity

S&P 500

Cash

O/N LIBOR

Volatility

VIX Futures

No Yes

+ STOP LOSS


BROAD EQUITY MARKET EXPOSURE WITH VOLATILITY HEDGE

18

Allocate the majority of the notional value to the S&P 500® Total Return Index.

Provide a “volatility hedge” by dynamically allocating part of the notional value to the S&P 500® VIX Short-Term Futures Total Return Index.

Includes a “stop loss” mechanism that shifts the entire notional investment to an interest-bearing cash investment if the performance of the index over the previous 5 business days falls by 2% or more.

Equity

S&P 500

Volatility

VIX Futures

Target Equity / Volatility Index Allocation

Realized Volatility

Environment

Implied Volatility

Downtrend

No Implied Volatility Trend

Implied Volatility Uptrend

< 10% 97.5% / 2.5% 97.5% / 2.5% 90% / 10%

10% to 20% 97.5% / 2.5% 90% / 10% 85% / 15%

20% to 35% 90% / 10% 85% / 15% 75% / 25%

35% to 45% 85% / 15% 75% / 25% 60% / 40%

≥ 45% 75% / 25% 60% / 40% 60% / 40%

STOP LOSS

Under certain circumstances, the entire allocation of equity and volatility may be shifted into a notional cash investment

Cash

O/N LIBOR


STOP LOSS FEATURE

19

• On each business day, the performance of the S&P 500 Dynamic VEQTOR Index Excess Return over the previous 5 business days is evaluated.

• If the 5-day performance is less than or equal to a fall of 2.0%, the Index will allocate 100% of its notional value to cash position.

Weights of both equity and volatility components are zero

• Once the 5-day performance is greater than -2.0%, the index will allocate back to equity and volatility components in accordance with the previous steps described.

• Average duration = 2.39 days


ALLOCATION HISTORY

20

Source: S&P Dow Jones Indices. Data is current through December 31, 2013. Charts are provided for illustrative purposes.

Past performance is no guarantee of future results. This chart may reflect hypothetical historical performance. Please see the

Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-

tested performance.

• VEQTOR dynamic allocates to equity: Median = 90%

Mean = 83%

• In a stressed market environment, allocation to equity may decline to as low as 60%. September – December 2008

August 2011


DOWNSIDE HEDGING PROPERTY

21

Source: S&P Dow Jones Indices. Data is current through December

31, 2013. Charts are provided for illustrative purposes. Past

performance is no guarantee of future results. This chart may reflect

hypothetical historical performance. Please see the Performance

Disclosure at the end of this document for more information regarding

the inherent limitations associated with back-tested performance.

Compared to S&P 500, VEQTOR shows lower maximum drawdown and faster recovery.

S&P 500

Total Return

S&P 500 Dynamic

VEQTOR Index Total

Return

Maximum Draw Down -55.25% -17.90%

Index Value 1095.04 112477.80

Date 9-Mar-09 15-Sep-08

Previos Peak Value 2447.03 137005.24

Date 9-Oct-07 9-Oct-07

Recovery Value 2449.08 140308.26

Date 2-Apr-12 16-Oct-08

#Days to Recover 1120 31

Go Live

Date

10/9/2007

3/9/2009

9/15/2008

10/16/2008 10/7/2007 4/2/2012


PERFORMANCE IN DIFFERENT MARKET ENVIRONMENTS

22

Source: S&P Dow Jones Indices. Data is current through December 31, 2013. Charts are provided for illustrative purposes. Past performance is no guarantee of future results. This chart may reflect

hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance.

• VEQTOR outperforms in bear market.

• VEQTOR participates in the growth but underperforms in strong bull or choppy market.

Period Market Environment

S&P 500 Total

Return

S&P 500 Dynamic

VEQTOR Index

Total Return

S&P 500 Low

Volatility Index Total

Return

12/1/2009 - 6/30/2010 Uncertainty after credit risk -4.85% -3.09% 0.42%

7/1/2010 - 7/31/2011 Bull Market 28.03% 6.50% 23.46%

8/1/2011 - 9/30/2011 Euro crisis, US downgrade -12.08% 13.34% -2.02%


PERFORMANCE HISTORY

23

Source: S&P Dow Jones Indices. Data is current through January 31, 2014. Charts are provided for

illustrative purposes. Past performance is no guarantee of future results. This chart may reflect

hypothetical historical performance. Please see the Performance Disclosure at the end of this document

for more information regarding the inherent limitations associated with back-tested performance.

Go Live

Date

S&P 500

Total Return

S&P 500 Dynamic

VEQTOR Index

Total Return

S&P 500 Low

Volatility Index

Total Return

Annual Return

2006 15.79% 14.15% 19.69%

2007 5.49% 17.20% 0.58%

2008 -37.00% 21.29% -21.41%

2009 26.46% 23.39% 19.22%

2010 15.06% 1.65% 13.36%

2011 2.11% 17.41% 14.78%

2012 16.00% 3.54% 10.30%

2013 32.39% 14.31% 23.59%

Jan 2014 -3.46% -3.21% -2.49%

Annualized Volatility

2006 10.03% 7.50% 7.77%

2007 16.03% 9.55% 14.29%

2008 41.01% 16.16% 29.96%

2009 27.25% 14.32% 17.13%

2010 18.07% 10.28% 12.75%

2011 23.37% 10.78% 16.50%

2012 12.74% 8.15% 8.40%

2013 11.07% 7.68% 10.20%

Jan 2014 12.54% 9.48% 10.83%

Maximum Draw Down

-55.25% -17.90% -40.40%

Correlation with S&P 500 using Daily Returns

100% 62.88% 93.34%

Correlation with S&P 500 using Monthly Returns

100% 12.07% 87.62%


CONCLUSIONS

24

• Market risk may lead to long-term performance drag. After a large loss, gains needed

to break even may take years to achieve. During times of heightened volatility, there

is greater demand for broad equity market exposure with built-in control over market

volatility.

• The S&P 500 VIX Short-Term Futures Index has demonstrated high negative

correlation with the equity market.

• The S&P 500 Dynamic VEQTOR Index hedges equity portfolio market risk using

derivative overlay techniques. It dynamically allocates among the equity, volatility

and cash, according to the realized volatility of the equity market and the implied

volatility of the S&P 500 options.

• The S&P 500 Dynamic VEQTOR Index has outperformed the equity market in a bear

market.


PERFORMANCE DISCLOSURE

25

The S&P 500 VIX Short-Term Futures Index was launched on January 22, 2009, at market close. All information presented prior to the Launch Date is back-tested. Back-tested performance is not actual performance, but is

hypothetical. The back-test calculations are based on the same methodology that was in effect on the Launch Date. Complete index methodology details are available at www.spdji.com.

The S&P 500 Dynamic VEQTOR Index was launched on November 18, 2009, at market close. All information presented prior to the Launch Date is back-tested. Back-tested performance is not actual performance, but is

hypothetical. The back-test calculations are based on the same methodology that was in effect on the Launch Date. Complete index methodology details are available at www.spdji.com.

The S&P 500 Low Volatility Index was launched on April 4, 2009, at market close. All information presented prior to the Launch Date is back-tested. Back-tested performance is not actual performance, but is hypothetical.

The back-test calculations are based on the same methodology that was in effect on the Launch Date. Complete index methodology details are available at www.spdji.com.

S&P Dow Jones Indices defines various dates to assist our clients in providing transparency on their products. The First Value Date is the first day for which there is a calculated value (either live or back-tested) for a given

index. The Base Date is the date at which the Index is set at a fixed value for calculation purposes. The Launch Date designates the date upon which the values of an index are first considered live; index values provided for

any date or time period prior to the index’s Launch Date are considered back-tested. S&P Dow Jones Indices defines the Launch Date as the date by which the values of an index are known to have been released to the

public, for example via the company’s public Web site or its data-feed to external parties. For Dow Jones-branded indices introduced prior to July 31, 2013, the Launch Date (which prior to July 31, 2013, was termed “Date of

Introduction”) is set at a date upon which no further changes were permitted to be made to the index methodology, but that may have been prior to the Index’s public release date.

Past performance is not an indication of future results. Prospective application of the methodology used to construct the indices may not result in performance commensurate with the back-test returns shown. The back-test period does not necessarily correspond to the entire available history of the index. Please refer to the methodology paper for the index, available at www.spdji.com for more details about the index, including the manner in which it is rebalanced, the timing of such rebalancing, criteria for additions and deletions, as well as all index calculations. It is not possible to invest directly in an Index.

Also, another limitation of hypothetical information is that generally the index is prepared with the benefit of hindsight. Back-tested data reflect the application of the index methodology and selection of index constituents in hindsight. No hypothetical record can completely account for the impact of financial risk in actual trading. For example, there are numerous factors related to the equities (or fixed income, or commodities) markets in general which cannot be, and have not been accounted for in the preparation of the index information set forth, all of which can affect actual performance.

The index returns shown do not represent the results of actual trading of investor assets. S&P Dow Jones Indices maintains the indices and calculates the index levels and performance shown or discussed, but does not manage actual assets. Index returns do not reflect payment of any sales charges or fees an investor would pay to purchase the securities they represent. The imposition of these fees and charges would cause actual and back-tested performance to be lower than the performance shown. In a simple example, if an index returned 10% on a US $100,000 investment for a 12-month period (or US$ 10,000) and an actual asset-based fee of 1.5% were imposed at the end of the period on the investment plus accrued interest (or US$ 1,650), the net return would be 8.35% (or US$ 8,350) for the year. Over 3 years, an annual 1.5% fee taken at year end with an assumed 10% return per year would result in a cumulative gross return of 33.10%, a total fee of US$ 5,375, and a cumulative net return of 27.2% (or US$ 27,200).


GENERAL DISCLAIMER

26

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Thank you for joining us…

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Contact Us Want More?

Natalia Bandera

Barclays

[email protected];

Please click on the widget at the bottom of your screen to

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Featured Content:

• Indexology: Join the Conversation at www.indexologyblog.com.

• Identifying the Differences Between VIX Spot and Futures

Please click on the resources widget for more content and to

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J.J. Feldman

Miracle Mile Advisors

[email protected];

Berlinda Liu


[email protected]

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Wolverine Trading, LLC

[email protected];

Shaun Wurzbach


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The VIX Factor: Are you Ready to Take Volatility by the ... · both Bear Stearns, London, and...

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