Equity Investing in a Volatile Market March 22

Equity Investing in a Volatile Market March 22, 2012 Minneapolis

Introduction Jeff Sheran, CFA Managing Director

Key Challenges § Need to reduce equity § Can’t reduce equity

Agenda § Section I Managed Volatility Strategies: Exploiting the Low Volatility Anomaly Mark Roemer § Section II Convertibles: Participate and Protect Michael Yee § Section III Hedging the Tail: Challenges and Opportunities Jeff Sheran, CFA

Managed Volatility Strategies: Exploiting the Low Volatility Anomaly Mark Roemer Portfolio Manager

Capital Asset Pricing Model (CAPM) § Financial theory suggests markets are efficient and investors are rational and demand to be compensated for risk § Expected excess return should be proportional to a stock’s sensitivity to the market § Total risk is a combination of market risk (non-diversifiable) and stock specific risk (diversifiable) Risk Free Rate Excess Market Return Expected Return = Rf + Beta [E(Rm) – Rf ] Total Risk = Market Risk + Stock Specific R

Empirical CAPM § The low volatility anomaly appears to be in Efficient Frontier direct conflict with CAPM, with the ability to plot outside the efficient frontier Empirical CAPM Efficient Frontier Hypothetical Managed Volatility Portfolio § Historically, investors have been willing to – Academics theorize that this anomaly exists because of behavioral biases – Leverage constraints may also contribute to this anomaly Excess Return take uncompensated risk Market Portfolio Risk/Beta

Examining the Evidence Historically Lower Volatility Stocks Have Provided Both Superior Absolute and Risk Adjusted Returns 100 $46. 87 Growth of a $1 10 1 Q 1 (Low Volatility) 0. 1 Q 2 Q 3 $0. 09 Q 4 Q 5 (High Volatility) 0. 01 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Low Volatility Strategies: Long-Term Benefits Low Volatility Stocks Have Consistently Outperformed High Volatility Stocks Since the Mid 1970 s Lowest Volatility Quintile “Q 1” (Annualized Return%) Low Volatility Outperforms High Volatility in 95% of rolling 5 -year periods Highest Volatility Quintile “Q 5” (Annualized Return%)

Exploiting the Low Volatility Anomaly § Contrary to financial theory (ex. capital asset pricing model), higher risk has not historically been rewarded with higher return – Academic research and 40 years of historical data support this anomaly § Certainly if investors knew beforehand that they would not be compensated for taking additional risk, they would avoid higher risk assets and the anomaly would disappear § Numerous behavioral theories have been put forward to explain why the low volatility anomaly persists: – Lottery Preference: Investors demonstrate a preference for stocks with a low probability of a very high return – Representativeness: Investors base future expectations on recent experience – Overconfidence Bias: Individuals over-estimate their ability to predict future outcomes – Loss Aversion: Investors discount positive expected outcomes in favor of minimizing losses

Behavioral Example: The Lottery Preference Consider Two Possible Gambles 50%: $11 GAMBLE A 50%: -$10 GAMBLE B 2%: $500 98%: -$10 § In scenario A, there are even odds for a potential profit of $1 § In scenario B, there is a slim chance of potential profit of $490 § Which scenario would you choose?

Behavioral Example: The Lottery Preference Consider Two Possible Gambles 50%: $11 GAMBLE A 50%: Expected Profit: 50¢ -$10 GAMBLE B 2%: $500 98%: Expected Profit: 20¢ -$10 § Based purely on the odds, rational individuals should prefer the first bet § It is precisely the allure of this second scenario that causes investors to be drawn to stocks that have exhibited a past behavior of extreme positive returns

Stocks with Normally Distributed Returns Stocks With a History of Consistent Returns Tend to Outperform in the Future Frequency Hypothetical Stock A: Histogram of Daily Returns: March 2008 – March 2011 Daily Return § We expect stocks with more uniformly distributed returns to deliver more consistent results in the future and are less likely to become overpriced

Stocks with Abnormally Distributed Returns Stocks With a History of Extreme, or Lottery-like Returns, Tend to Underperform in the Future Frequency Hypothetical Stock B: Histogram of Daily Returns: March 2008 – March 2011 Daily Return § Investors demonstrate a behavioral preference for stocks with lottery-like characteristics § These stocks are more likely to become overpriced and comprise an undue weight in a capitalization-weighted benchmark

The Perils of Cap-Weighted Benchmarks But Surely Sophisticated Investors are Unlikely to Fall Victim to the Lottery Effect Hypothetical Stock Example: Now 2. 3% Benchmark Weight Stock Goes Up: + 130% 1% Benchmark Weight “I’ve Gotta Own It!” Stock Goes Down: -75% Now 0. 6% Benchmark Weight § Passive Managers: ETFs and passive index investing propagates this anomaly § Active Managers: Tend to think of risks in purely relative terms and may also fall victim to this effect

Stocks with Normally Distributed Returns When Returns Are Normally Distributed Higher Risk = Higher Return § Normally distributed returns are a fundamental assumption of the capital asset pricing mode (CAPM) § When returns are normally distributed we find that CAPM holds Average Monthly Return (%) Low Skew Stocks: Average Monthly Return Ranked by Volatility, October 1976 – March 2011 (Low) (High) Total Volatility (Low to High)

Stocks with Positively Skewed Returns When Returns Are Positively Skewed Higher Risk ≠ Higher Return § Lottery-like stocks with high levels of positive skewness have an inverse relationship between risk and return § Stocks with both high levels of volatility and high skewness have abysmally low future returns Average Monthly Return (%) High Skew Stocks: Average Monthly Return Ranked by Volatility, October 1976 – March 2011 (Low) (High) Total Volatility (Low to High)

Constructing A Low Volatility Portfolio Taking Advantage of the Covariance Matrix § Covariance measures the co-movement of stocks § Portfolio construction takes advantage of the fact that stocks can have complementary risk characteristics § This doesn’t necessarily mean that a low volatility portfolio should be built solely with low beta stocks Benefits of Optimization 3. 0 Optimized Low Volatility Portfolio Russell 1000 Index Russell 1000 Lowest Volatility Quintile 2. 0 A B C D E F G A 1. 0 — — — B 0. 86 1. 0 — — — C -0. 62 0. 09 1. 0 — — D 0. 57 0. 97 0. 73 1. 0 — — — E 0. 33 0. 21 -0. 14 0. 25 1. 0 — — F 0. 72 -0. 69 0. 43 0. 35 0. 66 1. 0 — G 0. 88 0. 14 -0. 75 0. 86 0. 91 0. 23 1. 0 1. 5 1. 0 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 0. 0 2000 0. 5 1999 Growth of a $1 2. 5 Correlation Matrix

Effects of Compounding Advantage of Lowering Volatility § A benefit of managing volatility is the potential to avoid big losses and gain from the effects of compounding § Lower volatility portfolios have a distinct advantage given similar average portfolio returns § Minimizing drawdown is a significant determinant of long-term performance § Effect of compounding: 2 / 2) + s 1 u ( L L R diff = 1 + u. H - ( s. H 2 / 2) [ See additional disclosure at the end of this presentation. Source: Allianz Global Investors Capital ] T -1

Measuring Low Volatility Strategies Change Your Frame of Reference § Perhaps the worst thing an investor can do is constantly compare performance versus a capitalization-weighted benchmark § Portfolio tracking error may seem excessively large relative to the capitalization-weighted benchmark § More accurately, the benchmark has significantly higher volatility than the steady-eddy low volatility portfolio Low Volatility Strategy: Return Pattern Capitalization-Weighted Benchmark: Return Pattern

Asset Allocation Benefits of Low Volatility Substitute Equity with a Flat 20% Low Vol. Allocation: Can Improve Return and Risk § From a risk-adjusted return perspective (Sharpe ratio), low volatility strategies look far more attractive than their capitalizationweighted counterparts Efficient Frontier: Lower Equity Allocation Over the last 20 years, as of April 2011 10. 5 20% Low Vol 60% Equities 20% Bonds § In an asset allocation framework, low volatility strategies warrant a substantially higher allocation than their passive equity peers § Low volatility strategies also provide a particularly attractive way to staff the equity component of an LDI or risk parity strategy without unduly overweighting fixed income Return (% Annualized) 10. 0 20% Low Vol 40% Equities 40% Bonds 9. 5 9. 0 20% Low Vol 20% Equities 60% Bonds 8. 5 8. 0 80% Equities 20% Bonds 40% Equities 60% Bonds 20% Low Vol 80% Bonds 7. 5 60% Equities 40% Bonds 20% Equities 80% Bonds 7. 0 1. 0 3. 0 5. 0 7. 0 9. 0 Risk (% Annualized) Analysis of April 2011. 0 13. 0

So Why Should This Anomaly Persist? § Investor behavior is difficult to change - behavioral biases are not easily arbitraged away § Capitalization-weighted benchmarks give undue weight to stocks with a history of lottery-like returns § Passive investing and the growing use of ETFs will likely cause this anomaly to persist

Convertibles: Participate and Protect Michael Yee Portfolio Manager

Unique Behavior of a Convertible Bond Total Return Convertibles Can Participate in the Upside Potential of the Equity Market, While Limiting Exposure to Downside Volatility § Participation Potential Protects Like a Bond Appreciation Potential of a Stock 100 Conversion Value Bond Value Convertible Price 0 CHYFP 2 Stock Price CONVERTIBLES Convertible Price § Volatility Cushioning 60% - 80% of the Upside Potential of Equities 50% or less of the Downside Exposure

Types of Convertible Bonds Total Return Convertibles Can Participate in the Upside Potential of the Equity Market, While Limiting Exposure to Downside Volatility § Bond like or Busted Converts: Busted Total Return/ Balanced – Very little correlation to the underlying equity § Balanced or total Returns Converts: – Asymmetric risk/return – Captures 60 -80% of the upside to the equity with only 40 -50% of the downside Convertible Price – Trade like a fixed income instrument 100 § Equity Sensitive Converts: – Very highly correlated with the equity – Captures more of the upside, but also captures more of the downside 0 Stock Price Equity Sensitive

Risk/Reward Convertible Bonds Typically Have a Favorable Risk/Reward Profile vs. Other Asset Classes January 1988 to December 2011 Risk/Reward Return (% Annualized) 12 Bank of America Merrill Lynch All Convertibles All Qualities Index 8 S&P 500 Index Russell 2000 Index MSCI EAFE Index 4 0 0 4 8 12 Risk (% Annualized) 16 20

Convertibles Expand the Efficient Frontier Portfolios that Include Stocks Have Shown to be Inferior Based on Absolute and Risk-adjusted Returns Over the last 20 years as of December 31, 2011 Efficient Frontier: Including, Excluding Convertibles 8. 2 Including Convertibles Equity Allocation Replaced with Convertibles Return (% Annualized) 8. 0 7. 8 7. 6 60% Equities 40% Bonds 7. 4 75% Equities 25% Bonds 40% Equities 60% Bonds 7. 2 25% Equities 75% Bonds 7. 0 4. 0 6. 0 8. 0 Risk (% Annualized) 10. 0 12. 0

Upside Participation, Downside Protection Convertibles Typically Offer Equity-like Returns in Rising Markets With Less Downside Volatility January 1988 - December 2011 Market Participation Bank of America Merrill Lynch All Convertibles All Qualities Index 8 Average Quarterly Return (%) 6 6. 4 5. 2 4 2 0 -2 -4 -5. 2 -6 -8 -10 -7. 7 70 Up Quarters 26 Down Quarters S&P 500 Index

Correlations Convertible Bonds Normally Have Low Correlations Relative to Other Asset Classes, Providing Diversification Benefits January 1988 - December 2011 Convertible Bond Correlations 0. 87 U. S. Small Stocks U. S. Large Stocks 0. 85 Non-U. S. Stocks 0. 67 Barclays Capital Gov't. / 0. 14 Credit Bond 10 -Year Treasuries -0. 08 -0. 2 0. 4 0. 6 0. 8 1. 0

Outperform in Rising-Rate Environments Convertibles Have Provided Strong Performance in Rising Interest Rate Environments and Typically Deliver Diversification Benefits From Core Fixed Income January 1988 - December 2011 Market Participation 4. 0 Bank of America Merrill Lynch All Convertibles All Qualities Index Average Quarterly Return (%) 2. 8 Barclays Capital U. S. Government Credit Bond Index 2. 7 2. 3 2. 0 0. 0 -1. 1 -2. 0 72 Fixed Income Up Quarters* 24 Fixed Income Down Quarters*

Sector Weights The Convertibles Universe Has Exposure to All Sectors As of December 31, 2011 Absolute Sector Weights Bank of America Merrill Lynch Convertibles Index 25 22. 2 21. 6 20 15. 8 15 10. 1 5. 1 1. 3 2. 8 Utilities Telecommunications Technology Materials Healthcare Financials Energy 2. 4 Transportation 2. 7 2. 6 Media 5 0 8. 1 Consumer Staples 10 Consumer Discretionary Absolute Sector Weights (%) 30

Market Cap and Credit Quality Profiles The Convertibles Universe is Well Diversified Across Market Capitalization and Credit Quality As of December 31, 2011 Company Size Credit Quality 50 50 Portfolio Weight (%) 40 30 35. 8 37. 5 37. 3 40 33. 6 30 26. 7 20 20 10 10 0 29. 1 0 <2. 5 B 2. 5 - 10 B Market Cap >10 B Investment Grade Non-Investment Grade Not Rated

Conversion Premium Convertibles Typically Have A Positive Asymmetric Risk/Reward Profile As of December 31, 2011 Convertibles Universe Historical Conversion Premium 120 Conversion Premium (%) 100 80 60 40 20 0 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11

Universe The Total Convertible Market Declined as Redemptions Outpaced New Issuance As of December 31, 2011 Size of the Convertibles Universe 350 313 293 300 289 282 263 250 Billions ($) 218 221 212 200 166 150 120 232 186 177 155 128 104 100 50 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

New Issuance Has Been Slow As of December 31, 2011 Convertibles Universe New Issuance

Convertibles Philosophy, Strategy and Style Investment in Companies Poised to Benefit From Positive Fundamental Change § Disciplined, fundamental, bottom-up research process to build a portfolio of Convertible bond issuers that demonstrate improving fundamentals § Portfolio includes issuers who exhibit the highest visibility of future expected operating performance § Purely focused on the most optimal risk/reward structure, thereby maximizing total return potential § Minimize credit risk through our Upgrade Alert Model CHYFP 17

Hedging the Tail: Challenges and Opportunities Jeff Sheran, CFA Managing Director

Significant ‘Black Swan’ Events -12% -19% 9/11 (Sept. 10, 2001 - Sept. 21, 2001) Long-Term Capital Mgmt. Implodes (Jul. 17, 1998 - Aug. 31, 1998) -16% -18% Gulf War (Aug. 2, 1990 - Feb. 28, 1991) Tech Bubble Bursts (Jun. 22, 2002 - Jul. 22, 2002) -21% -8% 1987 Stock Market Crash (Oct. 16, 1987) Flash Crash (May 2010) -10% -28% Gulf Invasion (August 1990) Collapse of Lehman Bros. (Sept. 12, 2008 - Oct. 12, 2008)

What Do We Mean by ‘Tail Risk’?

Defining the Tail § Time Frame – One day, one month, one year? § Protection Levels – -5%, -10%, -20%? § Return Expectations

How Often Does the Market Crash? S&P 500 over 50 Years: 1960 to 2010 Calendar Months Rolling Monthly Periods Total Number of Periods: S&P 500 < -20% S&P 500 < -15% 12, 567 30 61 100% 0. 24% 0. 49% Total Number of Months: 600 October 1987 -21. 76% October 2008 -16. 94% August 1998 -14. 58% September 1974 -11. 93% November 1973 -11. 39% September 2002 -11. 00% February 2009 -10. 99% March 1980 -10. 18%

What Can We Use as a Hedge? Correlations During 2008 Financial Crisis Sept. 19, 2008 – Nov. 20, 2008 Return (%) Equities S&P 500 Index -39. 77 Russell 2000 Index -48. 72 Euro Stoxx 50 Pr -31. 18 MSCI EAFE Index -37. 92 Nikkei 225 -34. 93 Commodities XAU PHILA Gold & Silver Index -49. 61 WTI Crude Future Nov. 10 -52. 54 Gold 1 st Month -13. 04 Real Estate S&P GS Commodities Total Return Index -44. 26 Bloomberg REIT Index -62. 35 Fixed Income BC US Aggregate Total Return Value -1. 52 Volatility CBOE S&P Volatility Index (VIX) 152. 14

Hedging with Volatility Instruments § On equity – Index options – Single-stock options § On volatility – VIX futures – VIX options – Variance swaps § 3 key considerations

1. Need to be Hedged at All Times § Never know when a tail event will happen – (That’s what makes it a tail event) § Crucial to be long volatility when tail event occurs

2. Avoid Eroding Capital § Hedging programs are typically expensive – Equity index put options are typically priced at the point of inefficiency – Options expire and must be rolled § The laws of compounding are against you

3. Maintain an Equity Profile § Don’t turn your equity allocation into a market-neutral exposure – Upside potential must be preserved – Objective is asymmetry in your favor § Call selling can be costly if not implemented properly – Beware of post-crash periods e. g. 2003, 2009 – If gains are capped at 10%, the costs can be enormous

Volatility Solutions Structured Alpha Enhanced Equity Hedged Equity Hedge Long Volatility Objective Absolute return Alpha enhancement Absolute return Beta reduction Downside protection without capping upside Hedge declines yet don’t erode in flat/up markets Return Target (Annual) 5% or 10% net plus underlying 1. 0% to 1. 5% net plus underlying 7% to 10% net n/a Positive in down markets; flat/positive in up markets Volatility Target (Annual) 2% to 4% 1. 5% 4% to 7% Reduce volatility 50%; protect against declines N/A Underlying Exposure (Beta) Cash, Equity, Fixed Income / Active, Passive - Equity - Passive Equity (bundled or as a separate overlay) Cash Buy and sell calls and puts Primarily sell OTM calls Primarily sell ITM calls Buy puts, sell calls Long 6 -month VIX futures Equity indexes (SP 500, RU 2000, NASDAQ 100) Equity indexes S&P 500 Index options only Short 1 -month VIX futures Long deep OTM puts; More long than short puts Greater potential for premium retention Hedged down to short call strike price Long puts 4% to 7% at all times Strong return potential as implied volatility increases Management Fee 2 0% 0. 35% 0. 50% 60 bps 1% Performance Fee 2 30% None 20% Inception September 1, 2005 July 1, 2009 April 1, 2008 2012 Liquidity 3 Monthly Daily Monthly Instruments Down markets Notes 1. Structured Alpha refers to the following four existing AGIC strategies: Absolute Yield, US Large Cap Core, Structured Alpha – 10 Year Treasury and Absolute Yield 10. 2. Please see AGIC ADV Part 2 A Brochure for the standard fee schedules for these strategies. 3. The liquidity information presented reflects the expected liquidity for each strategy. Client exercise of minimum liquidity provisions during market disruptions and other adverse market conditions could have a detrimental effect on account performance. See additional disclosure at the end of this presentation. NYFPSP 10 47

Our Approach, Part I: Equity Hedge § Objectives – Designed to attach to any long-only equity strategy – Provide a significant hedge during market declines – No interference with the underlying equity strategy (goal is to keep pace with equities in rising markets) – Long equity strategy + hedging module functions as a long equity asset allocation

Equity Hedge § Construction – Consists of listed, exchange-traded long put and short call equity index options – Provides hedging on 100% of notional equity portfolio at all times – All option positions are held to expiration (no liquidity risk) – Strategy never exceeds 100% of notional on short calls – Strategy follows rules-based discipline

Equity Hedge § Application – Large capacity ($20 -30 bn with S&P 500 options) – Can be combined with both passive and active underlying equity portfolios – Can be used to hedge US large-cap core/growth/value-type equity portfolios

To Hedge or Not to Hedge? Backtested Gross Performance, January 1, 1996 to February 29, 2012 Equity Hedge – Large Cap Core S&P 500/ BCAG Blend Cumulative Return 357. 4% 197. 1% 197. 2% Annualized Return 9. 9% 7. 0% Annualized Standard Deviation 9. 0% 16. 2% 9. 1% 1996 – February 2012 The backtested performance results do not represent the results of actual trading and might not reflect the impact that material economic and market factors would have on the decisionmaking if AGIC was actually managing client’ money. All returns are gross of fees. Gross returns do not give effect to investment advisory fees, which would reduce such returns. Past performance is not indicative of future results. See additional disclosure at the end of this presentation.

Annual Returns in Different Environments Backtested Gross Performance Results, January 1, 1996 to February 29, 2012 Equity Hedge - Large Cap Core vs. S&P 500 and S&P 500/BCAG 400% Equity Hedge - Large Cap Core S&P 500/BCAG Blend 350% 300% 250% 200% 150% 100% 50% 0% 1996 S&P 500 SPX/BCAG EH-LCC 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 23. 0% 14. 0% 19. 7% 33. 4% 22. 4% 30. 1% 28. 6% 20. 0% 24. 1% 21. 0% 10. 9% 17. 9% -9. 1% 0. 0% -8. 2% -11. 9% -2. 7% -0. 2% -22. 1% -8. 2% -5. 1% 28. 7% 17. 2% 22. 4% 10. 9% 8. 0% 10. 0% 4. 9% 3. 8% 15. 8% 10. 5% 15. 1% 5. 5% 6. 3% 7. 1% 2008 2009 2010 2009 -37. 0% 26. 5% -20. 0% 17. 4% -9. 4% 28. 8% 2012 2010 2011 YTD 15. 1% 11. 7% 5. 7% 2. 1% 5. 0% 2. 0% 9. 0% 5. 3% 6. 4% 1996 - 1999 Bull 2000 - 2002 Bear 2003 - 2006 Bull 2007 - 2012 YTD Bear Volatile Volatility Environment Rising Declining Rising S&P 500 (Annual) 26. 4% -14. 6% 14. 7% 1. 4% EH-LCC 22. 8% -4. 5% 12. 6% 7. 3% Equity Market 2011 S&P 500/BCAG Blend is a hypothetical portfolio consisting of 55% S&P 500 and 45% Barclays Capital Aggregate Index, rebalanced monthly. The backtested performance results do not represent the results of actual trading and might not reflect the impact that material economic and market factors would have on the decisionmaking if AGIC was actually managing client’ money. All returns are gross of fees. Gross returns do not give effect to investment advisory fees, which would reduce such returns. Backtested performance is not indicative of future actual results and performance. See additional disclosure at the end of this presentation. Source: Bloomberg, CBOE, Options VUE.

Our Approach, Part II: Long Volatility § Objectives – Be net long volatility at all times to generate gains in rising-volatility environments (usually associated with equity-market declines) – Minimize erosion of capital during periods of rising equity markets/declining volatility – Flexibility - combine with other underlying strategies (equity, absolute return) or as an uncorrelated standalone strategy

Long Volatility § Construction – Portfolio consists of 100% long T-Bills as well as VIX futures positions – Uses a combination of 6 -month and 1 -month VIX futures (net long volatility at all times) – Positions taken either through total-return swaps or via direct VIX futures positions – VIX futures positions are rebalanced daily – Gross VIX futures exposure ranges from 80% to 100% of underlying cash portfolio – Strategy follows rules-based discipline

Long Volatility § Application – Limited capacity ($1 bn) – Flexible and uncorrelated alpha source that can be used to complement underlying equity portfolios or absolute return strategies – Notional size of the strategy can be customized

Backtested Gross Performance January 1, 1996 to February 29, 2012 January 1996 – February 2012 S&P 500 Long Volatility Cumulative Return 197. 1% 1301. 3% Annualized Return 7. 0% 17. 7% Annualized Standard Deviation 16. 2% 18. 0% The backtested performance results do not represent the results of actual trading and might not reflect the impact that material economic and market factors would have on the decisionmaking if AGIC was actually managing client’ money. All returns are gross of fees. Gross returns do not give effect to investment advisory fees, which would reduce such returns. Past performance is not indicative of future results. See additional disclosure at the end of this presentation.

Annual Returns in Different Environments Backtested Gross Performance Results, January 1, 1996 to February 29, 2012 Long Volatility vs. S&P 500 1400% Long Volatility S&P 500 1200% 1000% 800% 600% 400% 200% 0% 1996 S&P 500 Portfolio 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 YTD 23. 0% 38. 1% 33. 4% 17. 2% 28. 6% 39. 9% 21. 0% -5. 3% -9. 1% 6. 7% -11. 9% 7. 9% -22. 1% 16. 0% 28. 7% -11. 4% 10. 9% 27. 6% 4. 9% 6. 8% 15. 8% 13. 9% 5. 5% 9. 9% -37. 0% 61. 9% 26. 5% 17. 9% 15. 1% 40. 3% 2. 1% 11. 0 % 9. 0% 8. 8% 1996 - 1999 2000 - 2002 2003 - 2006 2007 – 2012 YTD Bull Bear Volatile Volatility Environment Rising Declining Rising S&P 500 (Annual) 26. 4% -14. 6% 14. 7% 1. 4% Portfolio 21. 0% 10. 1% 8. 3% 27. 9% Equity Market 2011 The performance results do not represent the results of actual trading and might not reflect the impact that material economic and market factors would have on the decision-making if AGIC was actually managing client’ money. All returns are gross of fees. Gross returns do not give effect to investment advisory fees, which would reduce such returns. Backtested performance is not indicative of future actual results and performance. See additional disclosure at the end of this presentation.

Q&A