Transcript CatleyLakeman_JPM_21Sep11

Rise
in asset class correlation
Agenda
Page
Ruy M. Ribeiro
[email protected]
Ruy Ribeiro is an Executive Director for J.P. Morgan in London, where he is
responsible for Rule-based Investment Strategies across all asset classes. Prior
to that, Mr. Ribeiro was head of Strategies Research and Development at
HSBC. Before that, he was J. P. Morgan’s Global Commodity Strategist, being
responsible for cross-commodity strategy, and was previously a Global Market
Strategist, conducting research on asset allocation, developing cross-mart
trading strategies, analyzing and developing rule-based strategies and being
also responsible for research on hedge funds. During his time in Commodities,
he was responsible for the flagship publication Global Commodity Strategy and
Outlook. Mr. Ribeiro also contributed to four other J.P. Morgan’s flagship
publications: The J.P. Morgan View, Global Markets Outlook and Strategy,
Alternative Investments Outlook and Strategy and Investment Strategies. Mr.
Ribeiro previously taught advanced portfolio management at the Wharton
School of Business, as well as finance and economics at the University of
Chicago and Pontifical Catholic University in Rio de Janeiro. Mr. Ribeiro
received a Ph.D. in Finance from The University of Chicago, where he also
obtained an MBA in finance. He holds a BA and MA in economics from the
Pontifical Catholic University in Rio de Janeiro.
September 2011
This presentation was prepared exclusively for the benefit and internal use of the J.P. Morgan client to whom it is directly addressed and delivered (including
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of which are accordingly subject to change. J.P. Morgan’s opinions and estimates constitute J.P. Morgan’s judgment and should be regarded as indicative,
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RISE IN ASSET CLASS CORRELATION
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or to provide any other services.
Rise in asset class correlation: causes
 Globalization: Emerging Market Equities and Developed Market Equity convergence
 Macro Volatility: End of the Great Moderation
 Uncertainty: Common factors in returns become more important when there is more uncertainty
about common fundamentals, changes in broad market outlook, herd mentality
 Positions: position squaring creates correlation
 Market Structure: Investor increasingly trade portfolios instead of securities
 Efficiency: As markets show signs of stress, they are not confirming to efficient markets
hypothesis.
 Rise vs Change: 10 years is a short horizon for asset class correlations
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Cross-Asset Correlation Levels have Increased
Equity Indices are Globalized
Asset Class
Correlation Between
1990-1995*
Past 5Y
Change
Equity
DM Country Indices
31%
47%
17%
Equity
EM Country Indices
22%
45%
23%
Equity
DM and EM Indices
38%
74%
36%
Equity
Economic Sectors
57%
69%
12%
Equity
Individual Stocks
25%
41%
16%
Credit
High Yield and Equities
46%
64%
19%
Credit
High Yield and VIX
37%
60%
24%
Foreign Exch.
DM Currencies and Equities
-1%
28%
29%
Foreign Exch.
EM Currencies and Equities
Interest Rates
10Y Rate and Equities
Commodity
All Commodities
Commodity
Commodities and Equities
Average
6%
42%
36%
-38%
29%
67%
5%
25%
21%
-5%
12%
17%
19%
45%
26%
*For Credit 2002-2005. All Currencies vs. USD
Source: J.P.Morgan Equity Derivatives Strategy
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Correlation between Equity Indices
Historical 120 day correlation between Put Underlyings
1.2
SX5E-SPX
SX5E-NKY
SPX-NKY
1.0
0.8
0.6
Correlation close to 1
during market crash in
2008
0.4
0.2
0.0
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(0.2)
(0.4)
Jun-05
Dec-05
Jun-06
Dec-06
Jun-07
Dec-07
Jun-08
Dec-08
Jun-09
Dec-09
Jun-10
Dec-10
Source: Bloomberg & J.P. Morgan. Past correlation is not a guide to future values. Data: Jan 2005 to Jan 2011. Please refer to the back-testing disclaimer at the beginning of this presentation.
 Although the correlation between put underlyings is high most of the time, it is almost equal to 1 during the period of market
crash in 2008.
 In case of a crash, if the correlation is high the put payoff on the best off index is almost same as that of the basket.
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Correlation Opportunities - Best of Puts
Switch Put
 At maturity
 At maturity if no index has fallen by 30% at any point, client is long a 95% Vanilla Put on equally weighted basket
otherwise they are long a Best of 95% Strike Put (pays on the index which has fallen the least)
 Why invest in a Switch Put?
 If markets fall a small amount from current levels client is long a Vanilla Put ; in cases where markets correct significantly
they are long a Best of Put
 Cheaper downside hedge to Vanilla Put
Indicative Pricing
Key features
 Indicative terms:
 Significant discount to the Vanilla Put
 EUR
 30% discount
 1 year
 If market drops by more than 30%:
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 SPX , SX5E, NKY
 The client is long a Put on the index that has fallen the
least
 Indicative pricing
Strike
 If market drops by less than 30%:
95% offer
Basket Vanilla Premium
6.45%
Best of Put Premium
3.90%
Switch Put Premium
4.45%
 The Switch Put provides the same protection as a
Vanilla Put on the basket
 Client has paid a significantly lower premium or this
protection
.
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Best-Of Leveraged Certificate
Simple and cost effective portfolio hedge
 Financial Terms:
 5-year maturity;
 Linked to a basket of 4 indices: FTSE 100, DJ Eurostoxx 50, S&P 500, Nikkei 225;
 Key rationale:
 When equities sell-off, all indices tend to be strongly correlated (i.e. falling together) and historically the
performance of the best index is closely aligned to the basket average. Hence a hedge where the payoff is linked to
the best performing index is both efficient and cost effective;
 Additionally, as the Best-Of Leveraged Certificate is geared on the downside, investors are positioned to gain from
a downturn.
Indicative pricing
 5y maturity
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 Currency: qGBP
 Payoff profile illustration:
Payoff in %
Index
Value
Payoff
105
95
 Payout at maturity:
101
99
Lev * Best-of Put spread 75/95 – ATM Best-of Call
100
100
95
100
94
107
85
170
75
240
70
240
 Basket of 4 indices: FTSE 100, DJ Eurostoxx 50,
S&P 500 and Nikkei 225
 Leverage = 7
5
7x
Best Index Performance in %
75%
95%
-1 x
Emerging Market Equities vs Developed Market Equities
 The Current Developed Market Crisis is a Global Crisis
 EM Crises were not Global Crises in this regard
 Relative importance of EM in the global economy and global financial markets has increased
 Over the last 21 years, the overweight of emerging market countries in MSCI ACWI has grown from 6.8% to
15.8 %
1-year Correlation between MSCI EM and MSCI
World
0.9
0.8
0.7
0.6
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0.5
0.4
0.3
0.2
0.1
0
-0.1
Jan-89
Jan-91
Jan-93
Jan-95
Jan-97
Jan-99
Jan-01
Source: J.P. Morgan, J.P. Morgan Equity Derivatives Strategy, Bloomberg. MSCI Barra.
Jan-03
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Jan-05
Jan-07
Jan-09
Jan-11
Rise in asset class correlation: Equities vs Bonds
 Correlation depends on the state of the business cycle, inflation, etc
 Equities depend on expectations on future growth and future discount rates
 Bonds depend on expectations on future discount rates
1-year Correlation between S&P 500 and 10-yr US
Bonds
0.8
0.6
0.4
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0.2
0
Jan-89
-0.2
Jan-91
Jan-93
Jan-95
Jan-97
Jan-99
Jan-01
Jan-03
-0.4
-0.6
-0.8
Source: J.P. Morgan, J.P. Morgan Equity Derivatives Strategy, Bloomberg.
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Jan-05
Jan-07
Jan-09
Jan-11
S&P Put Option contingent on 10Y Swap rate
Rationale
Correlation of 10Y Treasury Yield and Equities
 Rates are recently exhibiting positive correlation to equities, due
to risk flows between equities and US treasury bonds (see
graph)
 Investors may need to hedge against different scenarios :
 a tail event resulting in a sell-off of both bonds and equities
 a severe stagflation in which rate would go up and market
decline
 Product provides a cost effective hedge for investors in those
reversing rates/equity correlation scenarios
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Indicative Terms
Maturity
5 years
Currency
USD
Underlyings
S&P500, 10Y USD swap rate
Payout at maturity
90% Put Option on S&P, conditional on 10Y swap rate >
4.75%
Indicative offer
5.33%
Source: J.P. Morgan, J.P. Morgan Equity Derivatives Strategy, Bloomberg.
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Rise in asset class correlation: Equities and Commodities
 Commodity Shocks: Demand vs Supply
 It is all about demand now…
 More efficient markets now? Prices anticipating future demand
1-year Correlation between S&P 500 and S&P GSCI
0.8
0.6
0.4
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0.2
0
Dec-71 Dec-74 Dec-77
-0.2
Dec-80 Dec-83 Dec-86 Dec-89
Dec-92 Dec-95 Dec-98 Dec-01 Dec-04
-0.4
-0.6
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Dec-07 Dec-10
Rise in asset class correlation: Equities and Commodities (continued)
Shift in commodity/equity correlation after 2008
 Prior to the financial crisis in 2008, commodities were essentially uncorrelated to equities and bonds, and were relatively
weakly correlated among themselves
 After 2008, the biggest reversion was experienced by Oil/Equity correlation which spiked from roughly -10% to current levels
of over 60%. Industrial metals and soft commodities experienced similar correlation shifts.
 Gold/Equity correlations initially dropped as investors sold equities and rushed into the perceived relative security of gold.
However, correlation quickly turned positive fueled by speculative demand and strong negative correlation of USD to equities.
S&P 500 1Year 95% Put contingent on Spot Gold above
105%
Commodity/Equity Correlation for Energy, Precious
Metals, Industrial Metals, and Soft Commodities
 Indicative terms:
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 USD
 1 year
 SPX , GOLDLNPM
 Indicative pricing
Strike
95% offer
SPX Vanilla Put
6.60%
Hybrid Premium
2.90%
Cost Saving
55.0%
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Source: J.P. Morgan, J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Equities and Credit: Efficiency, Integration and Correlation:
 Credit and Equity markets have historically exhibited strong correlation
 Recent market stress has caused a recent diverge particularly in Europe according to our Credit Equity
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Valuation Model
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Implications for Asset Allocation: Risk Control
 Using volatility as a signal to allocate into risk assets
 Risk Budgeting
 Benefits
 Improve Return-Risk Profile
 Manages Tail Risk
 Written in 2008. It paid off during the crisis. It paid off
again in 2011
 More investors using similar approaches in asset
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allocation
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The Benefits of Risk Control in Stressed Markets
 Using volatility as a signal to allocate into risk assets
 Risk Budgeting
 Benefits
 Improve Return-Risk Profile
 Manages Tail Risk
 Written in 2008. It paid off during the crisis. It paid off
again in 2011
 More investors using similar approaches in asset
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allocation
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FTSE® 100 Risk Target (ER) Index
 The FTSE® 100 Risk Target (ER) Index offers investors an efficient way of gaining exposure to the FTSE® 100
Index, with the added benefit of a Risk Target mechanism.
 The graph below shows simulated historical performance of the FTSE® 100 Risk Target (ER) 10% Index during the
period May 1999 – Sep 2011. This version of the index aims for a volatility level of 10%.
FTSE® 100 Risk Target (ER) 10% Index
Risk Target
Non Risk Target
Annualised Return
3.22%
1.95%
Volatility
9.44%
20.73%
160
140
Normalised index level
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120
100
80
60
40
20
FTSE 100 Risk Target (ER) + Cash
FTSE 100 TR
0
May 99
May 00
May 01
May 02
May 03
May 04
May 05
May 06
May 07
May 08
May 09
May 10
May 11
Source: FTSE®, Bloomberg. Data: May 1999 – Sep 2011. Past performance is not a guide to future returns. “Risk Target” and FTSE® 100 Risk Target (ER)
Index refers to FTSE® 100 Risk Target (ER) Index 10%. Please see the backtesting disclaimer at the front of this presentation.
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Implications for Asset Allocation: Portfolio Selection
 Portfolio Optimization
 Short-term Returns
 Short-term Covariance (Correlation and Volatility)
 Stable risk profile and outperformance relative to static
benchmarks
Optimum Portfolios and the Efficient Frontier
12%
Return
RISE IN ASSET CLASS CORRELATION
16%
8%
Annualised Risk
4%
0%
4%
8%
12%
16%
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Implications for Asset Allocation: Portfolio Selection
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Implications: Identifying distress periods in Equity Markets
 Term structure of volatility futures can be a good indicator of distress
 A backwardated volatility curve can indicate high hedging demand
RISE IN ASSET CLASS CORRELATION
 Negative slope in August preceded the recent collapse in equity prices
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Volatility investment
Introduction
 Considered by most as an asset class, volatility is generally negatively correlated to equity markets.
 Volatility Index Futures on S&P500 (“VIX Futures”) have gained increasing popularity among investors as a transparent and liquid
way to get exposure to volatility. They were initially introduced on the CBOE Futures Exchange on 26 March 2004 and are
commonly traded with maturities up to 9 months.
 However, under certain conditions (depending on the slope of the term structure), a long-term investment in a volatility index that
rolls futures contracts could drag down the performance by virtue of the carry cost of curve slides.
Volatility index (VIX) performance vs. S&P 500
2,000
S&P 500 Index (lhs)
90
VIX Index (rhs)
80
1,800
70
60
1,400
50
1,200
40
30
1,000
20
Source: Bloomberg, as of 19 Sep 2011
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Sep-11
Sep-10
Sep-09
Oct-08
Oct-07
Oct-06
Nov-05
Nov-04
Nov-03
0
Dec-02
10
600
Dec-01
800
Dec-00
RISE IN ASSET CLASS CORRELATION
1,600
Macro Hedge Series Overview
Index (Ticker)
Key Features

Macro Hedge
(JPMZMHUS <Index>)
The J.P. Morgan Macro Hedge Index aims to provide a stable and fully transparent source of
absolute return, positively correlated with the level of implied volatility, which complements a longonly equity portfolio
 More diversified and less leveraged exposure across the VIX Futures curve aiming at mitigating the
slide cost of a long volatility overlay
Macro Hedge Enhanced
(JPMZMHEN <Index>)
 Better reactivity in the deactivation of the short leg in a risk/off scenario (short leg is unwound over
3 days versus 5 days in earlier version)
 Dynamic additional allocation to the long volatility leg (75% up to 100%), to better handle short-
dated spikes in volatility
 Similar approach to Macro Hedge Enhanced, i.e. a base long/short SPX volatility exposure (carry
RISE IN ASSET CLASS CORRELATION
optimization through a more diversified exposure across VIX futures)
Macro Hedge Enhanced
Hybrid
 Additional pickup through a variable long SX5E volatility exposure (0 up to 25%), allowing for a
(JPMZMHHG <Index>)
 Topical positioning against sovereign risks in Europe on top of a more standard US volatility carry /
better hedging reactivity in case of a short-dated spike in volatility
macro hedge trade
Macro Hedge Enhanced
Risk Control 6%
(JPMZMHE6 <Index>)
 Advanced risk-control mechanism (taking into account the long/short dynamics of the Macro Hedge
algorithm) that allows to successfully target a volatility of 6% for the Macro Hedge Enhanced Index
 Low-volatility carry strategy that can be accessed in capital-protected format
Macro Hedge Enhanced
Hybrid Risk Control 6%
 Advanced risk-control mechanism (taking into account the long/short dynamics of the Macro Hedge
(JPMZMHH6 <Index>)
 Low-volatility global carry strategy that can be accessed in capital-protected format
algorithm) that allows to successfully target a volatility of 6% for the Macro Hedge Enhanced
Hybrid Index
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Macro Hedge Index (JPMZMHUS Index)
Description
 The J.P. Morgan Macro Hedge Index aims to provide a stable and fully transparent source of absolute return, positively correlated
with the level of implied volatility, which complements a long-only equity portfolio
 In “stressed” market conditions the strategy is long volatility and therefore provides protection against equity market downturns
 When equity markets calm down, the strategy aims to generate alpha by going systematically long volatility at the longer end of the
futures curve and opportunistically short at the near end
Normal Markets Conditions
Stressed Markets
 Depending on the shape of the forward volatility curve the Macro

The Macro Hedge Index uses the shape of the VIX futures curve
as a signal to determine whether the market is stressed

When the spot VIX Index level is above the level of the futures on
any day during the month the strategy positions itself defensively
and is long forward volatility

In stressed times the strategy would benefit from the increase in
market volatility and will therefore provide an attractive hedge
against market turbulences if added to an investor’s portfolio
Hedge Index can position itself defensively
 If the forward volatility curve is upward sloping and the implied
forward volatility is trading above the spot level, the index
implements the long-short futures strategy that allows to benefit
from structural imbalances in the VIX futures markets and to
generate alpha
2 month point in the futures curve, rolling between the second and
third month futures contracts and an opportunistic short position on
the 1 month point in the futures curve, rolling between the first and
second month futures contracts.
 In normal market conditions the futures curve is upward sloping and
in particular the slope at the front of the curve is steeper than
further out
 Assuming volatility remains relatively stable, the long position is
subject to less slide cost than the short position resulting in a
positive carry
Slide is positive
Volatility
Volatility
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 The Index will take a synthetic and systematic long position on the
Slide is negative
3
6
9
12
3
20
6
9
12
Macro Hedge Index (JPMZMHUS Index)
Performance
600
Rebased to Sep 2006
500
Macro Hedge
Index
Rolling
1M/2M
S&P500
IRR
38.5%
-26.2%
-2.3%
Annualised Vol
32.7%
59.9%
25.5%
1.18
-0.44
-0.09
-36.7%
-80.1%
100.0%
Sharpe Ratio
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400
600%
Front End Short Position (rhs)
Correlation with
S&P500
Macrohedge Hedge Index (lhs)
Rolling 1M/2M futures (lhs)
S&P 500 Index (lhs)
500%
400%
300
300%
200
200%
100
100%
0
Sep-06
0%
Feb-07
JPMZMHUS Index
2006
2007
2008
2009
2010
2011
Jul-07
Jan
2.49%
(5.42%)
(3.47%)
(0.17%)
(1.73%)
Dec-07
Feb
Mar
(7.47%) (8.54%)
1.43% (3.48%)
10.25% 4.32%
4.40% 6.10%
(0.53%) (6.12%)
Apr-08
Apr
3.30%
3.67%
(0.97%)
(0.46%)
6.30%
Sep-08
May
Feb-09
Jul-09
Jun
Jul
2.68% (4.13%)
10.41% (8.25%)
3.13%
4.59%
0.98% (11.52%)
1.08%
(4.08%)
Dec-09
Aug
3.77%
11.89%
(9.72%) 5.42%
7.66%
4.09%
10.16%
8.03%
(10.24%) 34.04%
May-10
Sep-10
Feb-11
Jul-11
Sep
Oct
Nov
Dec
Full Year
2.46%
(8.04%)
4.32%
7.14%
7.50%
(1.17%)
5.16%
75.78%
(1.49%)
5.97%
0.94%
(6.17%)
19.55%
9.04%
(2.19%)
2.49%
5.15%
(3.63%)
6.20%
1.63%
(2.28%)
95.49%
62.43%
32.55%
Source: J.P. Morgan. Past performance is not a guide to future performance. Performance relates to the period Sep 06 to Sep 11. The Macro Hedge Index figures are net of an 75 bps
adjustment factor and other adjustments relating to rebalancing of notional underlying constituents. “S&P 500 Index” refers to the performance of S&P 500 Index (Bloomberg: SPX Index). 1M/2M
refers to the performance of the SPVXSP Index (Bloomberg: SPVXSP Index) . Please refer to the back-testing disclaimer at the beginning of this presentation.
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Performance since Dec 2009
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