MANAGING POSITIONS PRE- AND POST-TRADE

For educational purposes only. For professional and institutional clients only

Introduction

GABRIEL MANCEAU

Barclays, Volatility Trader

ANTOINE DELGA

Bloomberg, Equity Derivatives Application Specialist

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3

3

Managing Positions Pre- and Post-Trade

Option Valuation & Risk Measures

Vega Liquidity

Volatility Trading Map

Volatility Analysis and Trade Decisions

Rich VS Cheap Volatility

Volatility Trading Map

Trading risks and challenges

Convexity Trading

Are Underlyings really lognormal?Tail Risk

The Greeks

Pre-Trade

Post Trade

Source: Barclays, Bloomberg

You should not rely on historical or hypothetical historical information. Such historical and hypothetical historical information is not indicative of future performance

Option Valuation & Risk: Measures – the “Greeks”

Greeks Definition

Vega Liquidity

Delta and Realized Volatility

5

Introduction : What is Implied volatility?

Volatility surface for a given underlying by translating all options prices for all strikes

and maturities

Explain the price of options can calculate sensitivities to model inputs: no Model

No Greeks!

Simplify options prices to one variable common language for comparing any

option price

Exotics products: options prices Model taking inputs, trades specific portfolio

price

Any input variable can lead to a Greek

Log Normal

Diffusion

Black and Scholes

Implied vol

Forward

Strike

Maturity

Option price

6

Greeks

Spot:

Direct Greeks

Change in price in £ for 1% move in spot -> P&L = spot return in % * delta

Change in delta for 1% move in spot -> P&L = 50 * gamma * (spot return in %

)^2 Rates (rho), repos (repo rho), dividends (dividend risk): forward

determination

Change in price in £ for 1 point change in implied vol (additive) -> P&L = vega *

vol move

Change in price in £ for 1 day move forward . “Break-even”^2 = theta / (50 *

gamma)

Absolute change in delta in £ for a 1 point additive move on the vol -> the skew

greek

From model inputs (exotics): model parameters sensitivities

Delta

Gamma

Vega

Volatility:

Time:

Theta

Cross Greeks

Vanna

Model Greeks

Greeks profile for a call on euro stoxx : SX5E Dec 13 C2900 Index

We can observe the profile of each greek of this call for a price variation

at different dates:

OVME <GO> Options Risk Measures – “the Greeks”

8

Buying 4500 Straddle 2900 Sept 14 `

MARS <GO>

Example of a volatility strategy

Our portfolio is worth 19.5 M and we have 5.17% in vega, equivalent to EUR 1 Million

The Impact of a Volatility change as of now is linear

How to trade vega?

9

Buying 4500 Straddle 2900 Sept 14 `

MARS<GO> Example of a volatility strategy

The Impact of a Volatility change as is still linear but the drift weakens as time goes by

How to trade vega?

10

Spot ladder on Greeks

On a portfolio with various volatility strategies and assets

Greeks risk management for Volatility Portfolios

Spot

Shift P&L Change Delta Gamma Vega Rho Theta

-20.00% (16,993,217) 51,735,140 8,220,296 1,363,975 (43,394) (143,455)

-10.00% (6,634,572) 99,412,791 (4,436,269) 840,225 (22,293) (64,047)

-8.00% (4,559,178) 88,357,043 (7,301,477) 626,217 (18,617) (49,533)

-6.00% (2,824,505) 72,330,260 (8,879,257) 397,743 (15,175) (42,842)

-4.00% (1,487,144) 54,627,604 (8,937,058) 174,869 (11,956) (41,856)

-2.00% (540,331) 37,043,078 (9,475,732) (50,208) (8,938) (41,882)

-1.00% (213,307) 27,058,775 (11,171,663) (164,252) (7,502) (34,281)

0.00% - 14,927,662 (13,670,430) (280,638) (6,113) (23,666)

-1.00% (213,307) 27,058,775 (11,171,663) (164,252) (7,502) (34,281)

0.00% - 14,927,662 (13,670,430) (280,638) (6,113) (23,666)

1.00% 75,999 (125,065) (16,566,740) (394,063) (4,769) (10,243)

2.00% (11,276) (18,026,021) (19,056,298) (496,058) (3,462) 1,341

4.00% (724,290) (54,589,272) (16,342,166) (634,769) (898) 1,874

6.00% (1,998,915) (74,935,960) (2,642,287) (665,131) 1,733 (24,620)

4.00% (724,290) (54,589,272) (16,342,166) (634,769) (898) 1,874

6.00% (1,998,915) (74,935,960) (2,642,287) (665,131) 1,733 (24,620)

8.00% (3,365,551) (66,104,799) 13,790,487 (612,421) 4,562 (67,496)

10.00% (4,284,055) (29,904,304) 26,914,564 (517,109) 7,658 (98,198)

15.00% (1,987,823) 161,786,414 64,092,775 (23,615) 16,811 (166,326)

Greeks

11

A more advanced Vega risk management

Greeks risk management for portfolios

Maturity TOTAL

Strike Strike% July August September December March All Maturity

0 0.0% 4 -28 346 -204 552 669

143 5.0% -25 163 -1,958 1,096 -2,880 -3,604

285 10.0% 41 -257 3,042 -1,686 4,128 5,267

713 25.0% -87 480 -5,958 -488 -27,121 -33,174

1140 40.0% 486 -1,709 14,739 1,104 9,724 24,343

1425 50.0% -965 10,680 -78,125 46,225 -12,843 -35,028

1995 70.0% 521 -31,621 30,812 153,319 41,128 194,160

2280 79.9% 8,321 -76,021 -30,169 101,920 14,353 18,403

2565 89.9% 13,711 -328,278 523,492 243,061 76,810 528,797

2708 94.9% 28,491 -213,970 151,962 53,749 12,475 32,708

2779 97.4% 26,149 -116,902 -113,579 10,276 -16,000 -210,055

2850 99.9% 87,993 -5,323 -69,799 38,532 1,356 52,759

2921 102.4% -29,748 -117,137 -405,265 2,652 -12,977 -562,475

2993 104.9% 5,055 97,600 -322,165 -23,136 -45,489 -288,135

3135 109.9% 5,587 -34,307 -40,942 49,303 -12,590 -32,949

3420 119.9% -354 5,983 -10,848 30,307 6,993 32,081

3705 129.9% 105 -1,119 5,123 9,687 4,479 18,275

3990 139.9% -30 306 -1,507 -1,851 -1,147 -4,228

4275 149.9% 8 -79 385 432 -156 590

4560 159.9% -2 15 -74 -82 -37 -180

5700 199.9% 0 -0 2 2 0 4

11400 399.7% -0 0 -0 -0 -0 -0

42750 1498.9% 0 -0 0 0 0 0

Total 145,261 -811,523 -350,487 714,219 40,758 -261,771

WeightedTotal 506,682 -2,214,875 -763,084 1,052,746 48,385 -1,370,147

Vega Liquidity in the world

America EMEA Asia

SPX/VIX > 150M

SX5E/V2X

APPL, Russel, Nasdaq, FTSE, DAX

Facebook, Google,

EEM, PCLN,TSLA

NKY

NFLX, MSFT,AMZN, C,

LNKD, EFA, XLF, JPM

FSTMIB, SMI, CAC,

SX7E, AEX, TOP40

TPX, KOSPI, AXJO,

HSCE, HSI

12

50M

10M

5M

1M

500k

Avg daily

Vega traded

in M

13

SPX/VIX liquity

Delta and Realised Volatility

options “naked-delta” and options delta-hedged is a completely different trading

delta hedged trading: price based on the implied volatility (market sentiment), PL on the realised

volatility:

Realised volatility (RV) = a measure of the past fluctuations of the spot

Realised volatility is linked to the Black Scholes theory: it is the best-guess for the volatility that

should have been used in the option pricing formula.

14

3 Month realised vol for SPX, SX5E, FTSE, NKY

GV <GO>

Pre-Trade : Volatility Analysis and Trading Decisions

Rich VS Cheap Volatility

Volatility Trading Map

Determining “Rich” vs “Cheap” implied volatility

1. Use a data analysis tool to calculate the realised volatility for multiple time periods

2. Compare realized volatility to the current implied volatility in the options

3. Repeat this procedure for similar underlying assets, then compare spreads of

implied volatility to realized volatility

4. Consider any asset-specific catalysts (earnings, pending announcements or

macroeconomic factors) that may justify the presence of a particular spread

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17

Comparing the implied with realized volatility for the main global indices

VCA <GO>

Volatility Analysis

Volatility is quite expensive around the globe

Pre-Trade: Rich vs. Cheap Analysis

18

On the Euro Stoxx ATM 3 month VS 3month histo, a recent turn to rich

after some hieratic cycles

GV <GO>

Volatility Analysis

Pre-Trade: Rich vs. Cheap Analysis

19

GV <GO>

Historical evolution of the SX5E 1 year historical volatility

Pre-Trade : finding the good entry point

Looking at the realized volatility over the last year. we are at the minimum!

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GV <GO>

Historical evolution of the sx5e atm 1 year implied volatility

Pre-Trade : finding the good entry point

Looking at the volatility over the last year. we are within the 9th percentile of the

lowest volatility.

This is a very low entry point

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GV <GO>

Historical evolution of the 1 year volatility richness

Pre-Trade : finding the good entry point

… then we are now in a rich volatility regime

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GV <GO>

Historical evolution of the 1 year volatility richness

Pre-Trade : finding the good entry point

… then we are now in a rich volatility regime

23

SHOC <GO>

Using historical analysis to determine best and worst entry for a long volatility

trade: SX5E Straddle

Pre-Trade : finding the good entry point

Looking at the best entry point study, we can see that the current vol is 18.88

The lowest implied volatility was at 17.86 (-1.02 compared to now)

The average implied volatility was at 20.24 (+1.36 compared to now)

The highest implied volatility was at 23.50 (+4.68 compared to now)

Creating the relevant shocs

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MARS <GO>

Using historical analysis to determine best and worst cases

Pre-Trade : finding the good entry point

We can see how expensive or cheap becomes the Straddle according to the

different Implied Volatility levels

Pre-Trade : Comparing Historical Volatility for Similar Underlying Assets

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GV <GO>

Comparing Realized and Implied vol for the SX5e – SPX spread

Case Study: Spread VSTOXX - VIX

26

Analysing the Spread

The spread has been very rarely negative

Case Study: Spread VSTOXX - VIX

The Term Structure is pretty much constant around 3

27

Looking at the term structure this days

Case Study: Spread VSTOXX - VIX

However in the past like in end of 2011, there was better spreads opportunity…

… Like a back-end future spread around 2.5

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Analysing the Spread

Pre-Trade : Comparing Historical Volatility for Similar Underlying Assets

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GV <GO>

Analyzing richness of SX7E

From a 13 low to highs spiking at 80: A clear Vol regime change on the EUR

Banks Index

30

GV <GO>

Historical evolution of the sx7e atm 1 year implied volatility

Pre-Trade : Impact of an Event on Volatility

Focusing on the Euro Stoxx EUR Bank index (SX7E Index), we observe a huge

turn on volatility richness due to the concern on banks solvency raise in the

context of the European Sovereign Crisis

Case Study: Variance Swap

Payout = 𝑉𝑒𝑔𝑎 𝑡𝑟𝑎𝑑𝑒𝑑

2∗𝑆𝑡𝑟𝑖𝑘𝑒× (𝑅𝑒𝑎𝑙𝑖𝑧𝑒𝑑 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦2−𝐾2))

quadratic payout (K is the fair volatility)

Constant gamma over life-time of the trade

Theoretically replicable by a strip of options

Delta resets everyday

More expensive than straddle price for downside protection

position: long 1M vega sep14 SX5E @ 22

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What is a Variance Swap?

32

OVME <GO>

Buying a 12/30/11 SX7E Var Swap on 01/01/11:

Locking a 35.30 Volatility on June,2…

Case Study: Variance Swap

Example of a SX7E Var Swap

33

OVME <GO>

Example of a SX7E Var Swap

Buying a 09/10/13 SX7E Var Swap on 06/02/13:

We can see the money earned between end of August and November

Case Study: Variance Swap

Pre-Trade : Vol of Vol Trading

Forward variance: pays the square of realised volatility between 2 dates in the future

Fair strike depends on the expectation of the future realised volatility

No exposure to realised volatility until first date reach

P&L only depend on implied vol until first date is reach

Quadratic vega exposure (vega doubles when vol doubles)

Vix / V2X future:

Vix/V2X futures expires on the fair strike of a 30 days variance swap

P&L linear in volatility

Vega exposure constant

cheaper than forward variance

Both Implied volatility exposure, no gamma, but roll down/up theta

Both No daily delta-hedge

On Vix/V2x future expiry: VIX index = Future expiring = Fair strike of 30 day variance

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Volatility Assets Comparative

-20,000,000

-10,000,000

-

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

70,000,000

80,000,000

- 10 20 30 40 50 60

V2X Future

Fwd Var

Strategy p&l

Short 1M vega V2X nov13 future @ 19.8, Long Nov/Dec forward var @20.4 -> flat risk ?

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Max down P&L: 600 000 at 20.4 expiry level

Breakeven: under 15.5 and over 25.5

Example of P&L for a 40 Vol: 8.8 M

V2X Expiry Level

Pre-Trade : Vol of Vol Trading

Case Study: Forward Var / volatility futures

Pre-Trade: Skew Trading

36

GV <GO>

Looking at the 1 month skew

Skew Historical Analysis

Case Study : Risk Reversal

37

OVME <GO>

Based on negative correlation between spot and implied volatility

Trading spot/vol dynamic

Long Put Short Call dynamically delta hedged

5K dec13 2500/3200 rr, ref future = 2880

120M notional, 110K gamma, 22k vega, 16%

delta

-2.8M vanna but -5K theta

Scenario

Vol Up 1%, Spot Down 1% : New Delta -2.8 M

Buy 2.8 M Delta

Market back to flat: P&L = 23K on the day

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How did this trade work in the last 2 years

-4%

-3%

-2%

-1%

0%

1%

2%

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

1mth fixed strike vol move

spot move

2012

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

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

1mth fixed strike vol move

spot move

201

Trading Risk : Skew Trade

Looking at the skew differently : Sorting returns by Vol and Spot Trend

In 2013

spot move vol move 2012 2013

+ - 62% 67%

- + 54% 65%

In 2012

Post-Trade:Trading risks and challenges

Pin risk

Convexity Trading

Tail Risk Exposure

Are Underlyings really lognormal?

40 40

Summary of Volatility Assets exposure

You should not rely on historical or hypothetical historical information. Such historical and hypothetical historical information is not indicative of future performance

Source: Barclays.

Property Options Variance Swaps Forward Variance Swaps VIX Futures

Need to Delta-Hedge

Gamma Exposure

Theta Exposure

Exposure to Realised

Volatility

Exposure to Implied

Volatility

Exposure to Interest

Rates / Dividends

Listed/OTC Listed OTC / Listed OTC / Listed Listed

Convexity OTM/ ATM

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Spot Deviation to the Strike (in %)

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Short an option expiring in 15 days with implied volatility around 18, delta hedged

Theory limit with continuous : Pin Risk

Two equivalents spot realisation: One has a 4% move at the beginning of the

period, the other at the end: Which one do you prefer?...

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Spot Deviation to the Strike (in %)

Applying this reasoning to a 9000 SX5E 2900 Sept 14 Long Call, one day

before expiry, 1% below the strike with a vol around 18 and 12% Delta

Option Out of The Money still holding 150K premium to lose

Max loss 460K if spot expires at 2900, break-even [-0.5%,+1.2%]

Convexity Trading

Selling volatility performed well since 2008.

42

Barclays short 1Month SPX variance swap systematic strategy

43

S&P 20 years daily return probability comparison

Historical distribution versus distribution implied by options prices and implied vol

model

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

-10.0

%

-9.0

%

-8.0

%

-7.0

%

-6.0

%

-5.0

%

-4.0

%

-3.0

%

-2.0

%

-1.0

%

0.0

%

1.0

%

2.0

%

3.0

%

4.0

%

5.0

%

6.0

%

7.0

%

8.0

%

9.0

%

10

.0%

11

.0%

12

.0%

Historical Proba

Predicted proba

Daily return

Probability

Convexity Trading

44

-

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

500.00

-7.0% -2.0% 3.0%

Ratio

Ratio

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

-7.0% -2.0% 3.0%

Ratio

Ratio

(1,000,000,000,000.00)

1,000,000,000,000.00

3,000,000,000,000.00

5,000,000,000,000.00

7,000,000,000,000.00

9,000,000,000,000.00

-15.0% -10.0% -5.0% 0.0% 5.0% 10.0% 15.0%

Ratio

Ratio

Tail risks are underestimated more than billion time…

Convexity Trading

Post-trade : Tail Risk with Naked Put

Let’s remind us the profile of a Naked Short Put

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For a sole variation of the spot, shorting 40k dec 1900 Put gives us a EUR 400k gain.

On the other side of the coin, we have an infinite potential loss, for example, a

downside shift of -40% on the Euro Stoxx would cost us almost 60 m…

… Sizeable losses when stress testing the strategy for the worst market conditions in

recent years

Post-trade : Tail Risk with Short Call

Strategy: sell 10M notional Nokia 1M atm call at 40% vol, 50% delta, 10K vega, 400K

premium

Expected P&L, 1.2M a year

Stock up 50% 03/09 opening auction 2.5M loss

Nokia realized volatility before the spike :

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Theory Limit and trading behaviors

How log normal an underlying is?

beware of the underlying you are selling/buying

Spot jumps while theory assumes continuous diffusion

Beware of break-even thinking: portfolio with 5K theta, 1M gamma

break-even = 5𝐾

1𝑀∗50 = 1% (equivalent to 16% volatility)

day1: +1%, day2: -1% , day3 :+1%, day4 :+1%, day5: -1% : 1% avg move, 16%

realised volatility

day1: 0%, day 2 : 0% , day3 : 0%, day4 : 0%, day5 :+5% : 1% avg move, 35%

realised volatility

Convexity rule: realised volatility >= volatility calculated from the average of the move

Selling tail risk: high probability of a positive P&L, what about the expectation ?

Trader’s dilemma: 2 losing strategies. Which one you may finally follow ?

Strategy 1: +15K 4 days / 5, -80K 1 day / 5-> P&L expectation: -1M a year

Strategy 2: +10M 9 years /10, -100M 1 year /10 -> P&L expectation: -1M a year

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Thanks for your attention!

Any questions?

GABRIEL MANCEAU

Barclays, Volatility Trader

ANTOINE DELGA

Bloomberg, Equity Derivatives Application Specialist

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may be taxed differently to a direct investment in the components of the index.

SPONSOR ACTION THE INDEX SPONSOR MAY CHANGE THE INDEX. It may adjust the composition or calculation methodology and may

suspend or cancel the index. This will affect the performance of the Product.

INDEX SUBSTITUTION THE INDEX MAY BE SUBSTITUTED IN CERTAIN CIRCUMSTANCES. Such action may negatively affect the value and

performance of the Product.

Risk Factors

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