Non-normality of Market Returns. A framework for asset allocation decision-making

Transcription

1 Non-normality of Market Returns A framework for asset allocation decision-making

2 About J.P. Morgan Asset Management Strategic Investment Advisory Group The Strategic Investment Advisory Group (SIAG) partners with clients to develop objective, thoughtful solutions to the broad investment policy issues faced by corporate and public defined benefit pension plans, insurance companies, endowments and foundations. Our global team is one of J.P. Morgan s primary centers for thought leadership and advisory services for institutional clients in the areas of asset allocation, pension finance and risk management. The team s expertise is supported by powerful analytical capabilities for conducting asset/liability, risk budgeting and optimal asset allocation analysis, in line with client-specific investment guidelines, risk tolerance and return requirements. In response to the changing needs of CFOs, treasurers and CIOs, our suite of tools has been expanded to include corporate finance-based risk management analytics for assessing and proactively managing the impact of the pension plan on the corporation as a whole. SIAG brings a deep knowledge and understanding of capital markets behavior to all its advisory services, ensuring that results and recommendations have real-world consistency and can be tested under a variety of market scenarios. Strategic Investment Advisory Group services are offered as part of an overall asset management relationship to complement the many ways in which J.P. Morgan Asset Management provides clients with value-added insights. About J.P. Morgan Asset Management For more than a century, institutional investors have turned to J.P. Morgan Asset Management to skillfully manage their investment assets. This legacy of trusted partnership has been built on a promise to put client interests ahead of our own, to generate original insight, and to translate that insight into results. Today, our advice, insight and intellectual capital drive a growing array of innovative strategies that span U.S., international and global opportunities in equity, fixed income, real estate, private equity, hedge funds, infrastructure and asset allocation.

3 Foreword When market stress and disruptions hit portfolios especially hard, it is natural for investors to ask whether anything could have been done differently. We revisit our tools and strategies looking for ways to improve them. And often, it is in such times of reflection that we arrive at important insights and begin to craft effective solutions. The turmoil of 2007/2008 is just one among many such financial crises over the past 30 years rare and unpredictable events that can, and should, challenge conventional ideas about portfolio construction. Of course, we can never know in advance when such events will occur and how bad they will be. We do know, however, that we empirically observe such events with much greater frequency than current models allow for. So, the proper line of questioning for investors becomes: Can current risk management frameworks be modified to better capture the long-term, downside risk associated with these rare but dangerous anomalies i.e. frameworks that take into account more than just one-standarddeviation event, and better reflect their observed frequency? The goal of this paper is to present such a revised framework. In broad scope, we believe that two specific weaknesses in conventional risk assessment may be contributing to a quantifiable underestimation of portfolio risk: Frameworks assuming normality 1 : Conventional asset allocation frameworks typically make a range of assumptions about the normality of asset returns, the most problematic of which are that returns are independent from period to period 2 and normally distributed. In reality, we can empirically observe that in many cases returns are not independent, and in all cases they are not normally distributed. Inadequate risk measures: If one adopts an asset allocation framework that incorporates non-normality, then standard deviation becomes ineffective as the primary quantifier of portfolio risk. Using the latest statistical methods, however, we believe that these shortcomings can be addressed. And based on the results presented here, we argue that a modified risk framework may help investors improve portfolio efficiency and resiliency. Of course, no single statistical framework no matter how robust can render portfolios immune to such extreme events. Even the most important concepts and discoveries in finance continue to evolve through a process of dialogue and refinement. But our hope is that the work presented here will be an effective step toward confronting and clarifying some critically unresolved issues in asset allocation specifically, the habit of sacrificing accuracy in favor of ease-of-use when addressing the issue of non-normality of returns. This trade-off is potentially very harmful as it understates portfolio risk, and given advancements in statistical techniques, it is also no longer necessary. The challenge of asset allocation is one of both art and science, and to this end we hope that this work will prompt both discussion and further research, all geared toward helping investors better address the most pressing issues of the day. We appreciate all comments, questions, and contributions that this paper might prompt from clients and colleagues alike. 1 The normal distribution recognizable by its bell shaped probability density function is a statistical distribution commonly used to model asset class returns in traditional Mean-Variance Optimization frameworks. 2 Though independence is not in strict statistical terms a form of normality, we include it here because the assumption of independence is one of the central tenets of conventional asset allocation frameworks built around the concept of normality of asset returns. Abdullah Z. Sheikh, FIA, FSA Director of Research Strategic Investment Advisory Group J.P. Morgan Asset Management 1

4 Table of Contents 1 Foreword 3 Overview 7 Empirical evidence of non-normality in asset returns 14 Incorporating non-normality into an asset allocation framework 24 Estimating downside portfolio risk in a non-normal framework 27 Implications of non-normality on optimal portfolio solutions 31 Conclusion: Incorporating non-normality can lead to more efficient portfolios 32 Appendix 2 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

5 Overview Non-Normality and Its Impact on Portfolio Risk As sudden market disruptions go, the events of the past year the sub-prime mortgage crisis and ensuing global financial meltdown are not without precedent. Just in the last three decades, investors have been faced with a number of financial crises: Latin American debt crisis in the early 1980s Stock market crash of 1987 U.S. Savings & Loan crisis in Western European exchange rate mechanism crisis in 1992 East Asian financial crisis of 1997 Russian default crisis and the LTCM Hedge Fund crisis in 1998 Bursting of U.S. Technology bubble in While these anomalous events are rare, we observe such extreme non-normality in real-world markets more frequently than current risk management approaches allow for. Said another way, we believe that conventionally derived portfolios carry a higher level of downside risk than many investors believe, or current portfolio modeling techniques can identify. The primary reason for this underestimation of risk lies in the conventional approach to applying mean-variance theory, which was pioneered by Harry Markowitz in Traditional meanvariance frameworks have become the bedrock of top-down asset allocation decision making. As suggested above, a standard assumption in the mean-variance framework, and indeed many other holistic asset allocation frameworks, is that future asset class returns will be independent from period to period and normally distributed. Despite being widely recognized as overly simplistic, such broad assumptions of normality have appeal due to the ease with 3 In this context, the converse is also true, i.e. empirical observations of non-normality are the result of extreme market scenarios. 4 Asset classes include U.S. Aggregate Bonds, U.S. Large Cap Equity, International Equity, Emerging Markets Equity, Real Estate Investment Trusts, Hedge Fund of Funds and Private Equity. 5 Influenced in this context refers to a statistically significant coefficient when one month s return is regressed against the previous month s return. which they can be implemented. To implement an asset allocation framework based on normal asset return distributions, practitioners need only make two assumptions for each asset class (mean and standard deviation) and one assumption for each pair (co-variance). The latter applies because one implicitly assumes the relationship between each pair of asset classes is linear (another problematic issue discussed later in the paper). But what if asset returns are not normally distributed? In fact, in the real world we can observe empirically that returns are not normally distributed. This leads us to ask: How would incorporating non-normality affect the strategic asset allocation process? Empirically Identifying Non-Normality Each of the market events mentioned above was due to an incidence of non-normality of one sort or another 3. And the practical implication is that incorporating non-normality into the asset allocation process would force recognition of greater downside risk to the portfolio, precisely from such extreme, unexpected negative events. Our focus here is on capturing the impact of non-normality on downside portfolio risk as well as the asset allocation/ optimization process rather than identifying the specific sources of non-normality itself (although we do discuss economic or behavioral factors where relevant). Specifically, we test seven asset classes 4 and empirically confirm three primary categories of non-normality: Serial Correlation: A critical pillar of many traditional asset allocation frameworks (i.e. frameworks built on a premise of normality ), is the assumption that asset returns from period to period are independent and identically distributed. However, if one month s return is influenced 5 by the previous month s return, then there may be a need to account for this effect in future asset projections. Typically, traditional asset allocation frameworks do not allow for serial correlation, J.P. Morgan Asset Management 3

6 but we identify serial correlation in four of the seven asset classes we model. Serial correlation, if not adjusted for in the underlying data, masks true asset class volatility and biases risk estimates downwards, leading to underestimation of overall portfolio risk. Fat Left Tails (Negative Skewness and Leptokurtosis): Our second form of non-normality relates to observing negative returns in greater magnitude and with a higher probability than implied by the normal distribution. This phenomenon is commonly referred to as fat left tails. Exhibit 1 illustrates this phenomenon for monthly dollar-hedged International Equity returns over the ten years to October The chart plots the empirical (i.e. observed) probability density function of the data relative to a normal distribution. One can see that the observed return series (blue line) is more peaked, has a higher density at the extreme left, and leans further to the right than the normal distribution (orange line). The rightward lean is its negative skewness. The consequence of this skewness is that the left slope of the blue line is longer than the left slope of the orange line i.e. it has a longer tail, which indicates a greater magnitude of extreme negative events. In addition, the blue line is taller at its apex and shows a higher density at the extreme left end (i.e. leptokurtosis). In particular, the higher density at the left tail indicates a higher probability of extreme negative events. Exhibit 1: International Equities Fat Left Tails in historical returns Density Fat left tails Empirical Normal An asset allocation framework based on the normal distribution would understate both the frequency and magnitude of extreme negative events, as well as their potential effects on portfolio returns and efficiency. Correlation Breakdown: The simple correlations often used in traditional asset allocation models assume a linear relationship between asset classes i.e. they assume that the relationship between the variables at the extremes is similar to their relationship at less extreme values. Using simple linear correlations is the equivalent of assuming that the joint distribution of asset returns is (multivariate) normal. Joint distributions capture how asset classes behave together rather than individually. However, we find that in many cases correlations under extreme conditions are quite different than under normal conditions. In other words, the expected linear correlations breakdown and asset classes exhibit quite different joint behavior. The relationships, in fact, are not linear and the assumption of linearity (by using linear correlation matrices) underestimates the probability of joint negative returns under extreme conditions. Relying on linear correlation matrices tends to overestimate the benefits of portfolio diversification during periods of high market volatility. This leads to a systematic underestimation of downside portfolio risk. Statistical Approaches for Incorporating Non-Normality Fortunately, we have at our disposal sophisticated statistical tools that allow us to correct for these types of non-normality. Unsmoothing Serial Correlation: Serial correlations can be unsmoothed. That is, we can correct for the influence of prior-period returns and restore independence to singleperiod returns. To arrive at our new adjusted return series we apply a variation of Fisher-Geltner-Webb s well-established unsmoothing methodology 6. Our new adjusted return series is better reflective of the risk characteristics of the asset class. Notably, the new unsmoothed return series has the same mean as our original return series, but shows a higher volatility, and thus higher downside risk. 0-20% -15% -10% -5% 0% 5% 10% 15% 20% Monthly return 6 For a more detailed treatment, please refer to Fisher, J., D. Geltner, and B. Webb Value Indices of Commercial Real Estate: A Comparison of Index Construction Methods. Also, Fisher, J. and D. Geltner De-Lagging the NCREIF Index: Transaction Prices and Reverse-Engineering. 4 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

7 Modeling Fat Left Tails (Negative Skewness and Leptokurtosis): Fat left tails can be addressed using Extreme Value theory a body of work specifically designed to look at the probability of high-risk, but low-probability, events such as floods, earthquakes and large insurance losses. In other words, its focus is estimating tail risk. By applying Extreme Value theory we can create asset return distribution models that are a closer fit to the return series we observe empirically, much more similar than normal distributions. Exhibit 2 shows a probability density function for dollar-hedged International Equities, calculated using Extreme Value Theory (orange line). Exhibit 2: International Equities Applying Extreme Value theory for a better fit Density Much better fit for left tail Empirical Extreme value 0-20% -15% -10% -5% 0% 5% 10% 15% Monthly return It is a much closer approximation to empirically observed performance in terms of negative skewness (rightward tilt) and leptokurtosis ( fat left tail). This process can be applied to all assets in the portfolio, the overall result being an increase in the portfolio s downside risk profile. Simulating Correlation Breakdown: Finally, we can address the issue of correlation breakdown using copula theory 7 a body of work that explicitly looks at the impact of contagion or converging correlations at the total portfolio level. Copulas 7 Copulas have been applied extensively to the pricing of Collateralized Debt Obligations in addition to other areas in finance. For a detailed treatment, please refer to An Introduction to Copulas by Nelson R. R A key tenet of behavioral finance is the idea of loss aversion, i.e. a tendency of investors to prefer avoiding losses than making gains. This translates to risk preferences that are asymmetric in nature. 9 Our model calculates real portfolio value by discounting the nominal portfolio value using projected inflation. Inflation itself is projected stochastically in our framework. are mathematical functions that allow us to model the joint distribution of asset returns separately from the marginal (i.e. individual asset class) distributions. By considering joint distributions, we turn our focus to how asset classes behave together rather than individually. In particular, copulas allow us to model an increased incidence of joint negative returns (i.e. the fatter joint left tails) in our simulation results, just as we observe empirically in real-world market data. And again, using a more accurate proxy for observed behavior results in recognizing higher downside portfolio risk, specifically due to increased dependence of asset returns during periods of market stress. A Better Risk Quantifier: Conditional Value at Risk We believe that empirical evidence suggests an imperative to incorporate various types of non-normality into the asset allocation and portfolio modeling process, specifically to better understand and model downside portfolio risk. Yet if we take this step, we have to ask whether or not our conventional risk measure (i.e. standard deviation) is up to the new task. We would argue that, in a framework based on non-normality, standard deviation may not be investors most appropriate measure of portfolio risk because it rewards the desirable upside movements as hard as it punishes the undesirable downside movements. This is generally inconsistent with investor risk preferences primarily as observed in the field of behavioral finance 8. Conditional Value at Risk (CVaR 95 ) overcomes many of the drawbacks of standard deviation as a risk measure. Primarily, as it only measures risk on the downside, it captures both the asymmetric risk preferences of investors and the incidence of fat left tails induced by skewed and leptokurtic return distributions. Further, given the widespread use by major institutional investors and regulators of its first cousin Value at Risk we judge it to be the most appropriate risk measure to incorporate it into our framework. We define Conditional Value at Risk (CVaR 95 ) as the average real 9 portfolio loss (or gain) relative to the starting portfolio value in the worst five percent of scenarios, based on our 10,000 Monte Carlo simulations. It is simply the average real loss (or gain) in the worst 500 (5% of 10,000) scenarios i.e. the left tail of the portfolio distribution. J.P. Morgan Asset Management 5

8 Incorporating Non-Normality: A Potentially Better Way to Assess and Manage Downside Risk Through rigorous analysis, we examine the impact of incorporating non-normality into the asset allocation process and determine that it reveals increased downside risk, compared with portfolios optimized using conventional mean-variance frameworks. These results are compelling: ignoring empiric observations of non-normality in equity (and equity type) return distributions understates portfolio downside risk. Likewise, using standard deviation, rather than more behaviorally attuned conditional value-at-risk measures, may in fact inadvertently increase rather than decrease downside risk. Such an underestimation of downside risk can have severe consequences for investors, even in extreme cases presenting a solvency risk. The remainder of this paper is focused on statistical proofs of non-normality and the steps we apply to allow for them in the asset allocation and optimization process. The applied methodologies can be, statistically speaking, quite intricate; but they are very logical in their flow, and following the sequence of steps is very straightforward. But again, what we would emphasize here is the goal and overall impact of this work: to better capture downside portfolio risk that is observed in real-world markets, but missed by traditional asset allocation frameworks. The intended result is a more robust portfolio modeling approach that may help investors improve portfolio efficiency and resiliency, in light of a clearer understanding of portfolio risk. 6 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

9 Empirical evidence of non-normality in asset returns Addressing the problem of non-normality entails employing quantitative techniques that are rather more sophisticated compared to traditional asset allocation techniques. However, our end goal is conceptually very simple: to produce an asset allocation framework that balances future portfolio return expectations with the potential for more robust downside risk management. First, however, we must test to see whether and what kinds of non-normality may be present in empirically observed asset returns. In this section, we present the results of such tests, which indicate the presence of several types of non-normality that are typically not allowed for in traditional asset allocation frameworks to the detriment of risk estimation. We address three categories of non-normality 10 in the following order: A. Serial correlation in asset returns this occurs when one period s return is correlated to the previous period s return, inducing dependence over time. B. Fat left tails (negative skewness and leptokurtosis) this occurs when extreme negative returns are observed, with a magnitude and frequency greater than implied by the normal distribution. C. Correlation breakdown in joint asset class returns this occurs during periods of high market volatility and is typically not captured by linear correlation matrices. Throughout this section, we base our tests on ten years of monthly return data to October 31, In later sections we then incorporate these empirical results into a revised asset allocation framework. Exhibit 3: Testing for serial correlation in monthly asset class returns Aggregate Bonds Benchmark index Test statistic P-value Barclays Capital U.S. Aggregate Index Reject null hypothesis of no serial correlation Statistically significant lag No None U.S. Equity S&P500 Total Return Index No None International Equity MSCI EAFE (hedged) Index Yes One Emerging Markets Equity Real Estate Investment Trusts (REITs) Hedge Fund of Funds Private Equity MSCI Emerging Markets Yes One Index FTSE / S&P NAREIT Index No None HFRI Fund of Funds Diversified Index Dow Jones Wilshire Microcap Total Return Index Yes One Yes One For those familiar with this statistical procedure, we note that the complete official moniker for the Q-statistic is the Ljung-Box Q-statistic. It is computed as follows: Where T j is the j-th serial correlation and T is the number of observations. 10 There are other forms of non-normality one can observe in asset returns. For example, heteroskedasticity or serial correlation in volatilities (also called volatility clustering) is another such form. 11 Our sources for these return streams are Datastream, Bloomberg, Barclays Capital, MSCI, HFRI, FTSE, and Dow Jones. J.P. Morgan Asset Management 7

10 A. Serial Correlation of Returns For each asset class, we formally test for serial correlation by calculating what is called the Q-statistic. In this test, we are looking to either affirm or reject the null hypothesis, which asserts that serial correlation does not exist in the data. If the Q-statistic for a given asset class has significance at a 5% confidence level (i.e. a p-value of less than 0.05), we can conclude that there is sufficient evidence to reject the null hypothesis of no serial correlation. In this case, we must allow for the effect of serial correlation on future asset class returns. If the p-value is higher than 0.05, the null hypothesis is not rejected and we conclude that there is insufficient evidence to reject the null hypothesis of no serial correlation. In Exhibit 3 on the previous page, we show the results of testing for serial correlation in the return series of our universe of seven asset classes. We find statistical evidence of first-order serial correlation 12 in the returns of several asset classes: International Equity, Emerging Markets Equity, Hedge Fund of Funds, and Private Equity. We hypothesize that serial correlation is common in alternative investment strategies such as Hedge Fund of Funds and Private Equity because they often hold illiquid and hard-to-price assets. The difficulty in valuing the underlying assets at regular intervals requires managers or administrators to estimate prices (for example, with reference to the closest marketable security or based on certain economic indicators). If current asset prices are derived for example by updating last month s asset prices (after allowing for changes in the economic environment since the last valuation), then serial correlation reflects a gradual recognition of the true underlying value of the asset. Such a valuation methodology would explain our empirical observation of first order serial correlation in the asset returns of our alternative asset classes. And a consequence of gradual (rather than instantaneous) recognition of the true underlying value of an asset class is that conventional risk estimates derived from a serially correlated return stream will be underestimated. In the case of International Equity and Emerging Markets Equity, the presence of serial correlation is a very recent phenomenon. In fact, it is a direct consequence of the financial crises experienced globally by investors over 2008 and into Public equity markets around the world have fallen precipitously over 2008 and into 2009, displaying month-upon-month of negative returns. In Developed International Equity, nine of the last twelve monthly returns to October 2008 were negative. In the case of Emerging Markets Equity, eight of the last twelve monthly returns were negative. This has had the effect of inducing statistically significant serial correlation in monthly public equity market returns for International and Emerging Markets Equity. Unfortunately, the presence of serial correlation distorts the true risk characteristics of an asset class. In particular, it is incorrect to model future returns assuming independence, if in fact returns are serially correlated (as our statistical tests indicate). If it is not corrected, serial correlation can reduce risk estimates from a time series by inappropriately smoothing asset class volatility B. Evidence of Fat Left Tails Our second form of non-normality relates to observing negative returns in greater magnitude and with a higher probability Exhibit 4: histogram of U.S. equity returns ten years ending october 2008 Frequency Key Statistics Mean 0.1% Median 0.7% Maximum 9.8% Minimum -16.8% Std Dev. 4.4% Skewness Kurtosis 4.20 Jarque-Bera First order serial correlation implies that the return from one month is correlated with the return from the previous month. 0-18% -15% -12% -9% -6% -3% 0% 3% 6% 9% 12% Monthly return 8 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

11 than implied by the normal distribution. As an example, let us consider U.S. Equities. Exhibit 4 shows a simple histogram of monthly returns for U.S. Equities over the last ten years. It illustrates that the distribution is clearly not symmetric. In fact it has a very noticeable negative skew (i.e. leans to the right) as well as excess kurtosis (i.e. is more peaked than a normal distribution; it has a kurtosis value of 4.15, which is above the normal value of 3.0). We can confirm this intuitive observation with a simple statistical test: the Jarque Bera (J-B) test 13 is a simple test of non-normality and conclusively rejects the null hypothesis of normality in the return distribution. When we repeat the J-B test using our other asset classes, we find that those results, too, reject the null hypothesis. Their returns also exhibit non-normality. We can illustrate the understatement of downside risk by comparing two types of density functions: 1. The density function of the normal distribution The kernel (i.e. empirical) density function implied by actual historical returns In Exhibits 5 through 11, we show density function results for all seven asset classes. Our focus, in particular, is on the left tails of the density function and on whether the normal distribution underestimates the probability of outcomes at these extremes. These kernel (or empirical) density functions clearly indicate fat left tails relative to those implied by the normal distribution. The table in Exhibit 12 summarizes the results of our tests, which identify non-normality in all seven asset classes and fat left tails across all asset classes. The fat left tail phenomenon implies that using a normal distribution to model returns underestimates the frequency and magnitude of downside events. Exhibit 5: U.S. Bonds Fat left tails in historical returns Density % -4% -2% 0% 2% 4% 6% Monthly return Exhibit 6: U.S. Equities Fat left tails in historical returns Density Density Empirical Normal Empirical Normal 0-30% -20% -10% 0% 10% 20% Monthly return Exhibit 7: International Equities Fat left tails in historical returns Empirical Normal The Jarque-Bera (J-B) test measures the departure from normality of a sample, based on its skewness and kurtosis. The J-B test statistic is defined as N/6 * (S 2 + (K-3) 2 / 4) where N is the sample size, S is the skewness, and K is the kurtosis. 14 The normal distribution density function is parameterized using the Method of Maximum Likelihood based on our sample of underlying historical data. 0-20% -15% -10% -5% 0% 5% 10% 15% 20% Monthly return J.P. Morgan Asset Management 9

12 Exhibit 8: Emerging Markets Equities Fat left tails in historical returns Density Empirical Normal -40% -30% -20% -10% 0% 10% 20% 30% 40% Monthly return Exhibit 9: U.S. REITS Fat left tails in historical returns Density Empirical 4 3 Normal % -30% -20% -10% 0% 10% 20% 30% 40% Monthly return Exhibit 10: Hedge Fund of funds Fat left tails in historical returns Exhibit 11: Private Equity Fat left tails in historical returns Density Empirical Normal Density Empirical Normal % -75% -50% -25% 0% 25% 50% 75% 100% Monthly return 0-30% -20% -10% 0% 10% 20% 30% Monthly return Exhibit 12: Empirical evidence of fat left tails in asset returns Skewness Kurtosis J-B Test Statistic Rejects Normality Fat Left Tail Compared to Normal Aggregate Bonds Yes Yes U.S. Equity Yes Yes International Equity Yes Yes Emerging Markets Equity Yes Yes Real Estate Investment Trusts (REITs) Yes Yes Hedge Fund of Funds Yes Yes Private Equity Yes Yes 10 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

13 C. Correlation Breakdown: Converging Correlations During Periods of Market Stress It is a common observation in financial literature that correlations tend to be quite unstable over time. Depending on the historical period, different asset classes can provide varying degrees of diversification. Market events such as the 1987 stock market crash and the 1997 Asian Financial crisis illustrate how contagion the risk of one market negatively impacting and destabilizing another can lead to cases of converging correlations. In this section, we test the hypothesis that correlations and volatility are positively related. In particular, we investigate whether correlations between asset classes tend to increase during periods of high market volatility and stress, compared to periods of relative calm. Exhibit 13: CBOE VIX since inception If this proves to be the case, it means that the benefits of portfolio diversification may be overestimated by traditional frameworks that rely on linear correlation matrices. In other words, diversification may not materialize precisely when an investor needs it most i.e. during periods of high market volatility. To test our hypothesis 15, we analyze simple Pearson correlations 16 over two distinct historical periods a full ten-year period versus a shorter, high-volatility period of market stress. Our chosen volatility measure is the the Chicago Board Options Exchange Volatility Index (CBOE VIX). Exhibit 13 plots CBOE VIX values since its inception. The CBOE VIX tracks expected equity volatility over a 30-day period 17. It is widely regarded as one of the most reliable measures of market uncertainty available, and it is traded in real time on a daily basis. We identify high-volatility periods in relation to the long-term VIX average. More specifically, our two test periods are defined as follows: Expected volatility % Jan- 90 Jan- 92 Jan- 94 Jan- 96 Jan- 98 Jan- 00 Jan- 02 Jan- 04 Jan- 06 Jan The control period is a full ten-year history, from October 1997 to September 2007, during which the VIX averaged 20.9%. 2. The period of market stress used for comparison is from August 1998 to September 1999, during which time the VIX averaged 30.9% considerably higher than its ten-year average. This high-volatility period captured both the Asian and the Russian financial crises, which led to significant market uncertainty. Exhibit 14: SIMPLE CORRELATIONS OVER TEN YEARS TO SEPTEMBER 2007 U.S. Bonds U.S. Bonds 1.00 U.S. Equity U.S. Equity International Equity International Equity Emerging Markets Equity Emerging Markets Equity Real Estate Investment Trusts Real Estate Investment Trusts (REITs) Hedge Fund of Funds Hedge Fund of Funds Private Equity Private Equity Note that although we do not test whether the changes in correlations are statistically significant, we do believe the changes illustrate our broad point of correlation convergence during periods of market stress. 16 Pearson correlations are used to indicate the strength and direction of a linear correlation between two random variables. 17 More precisely, the CBOE VIX represents the implied volatility of S&P 500 index options. The VIX is quoted in terms of percentage points and translates, roughly, to the expected movement in the S&P 500 index over the next 30-day period, on an annualized basis. J.P. Morgan Asset Management 11

14 12 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making The results are striking. Nearly all the correlation coefficients increased to some degree (during this period of high market volatility), with only four showing a decrease. Our main diversifiers did not provide the diversification benefits anticipated.

15 Exhibit 14 shows correlation coefficients (ρ) over the full (monthly) ten-year period to September 2007 for all seven asset classes. Our correlation matrix indicates that investing in certain pairs of assets should provide diversification benefits i.e. they should either be negatively or lowly correlated. For example, let s consider correlations of asset classes with U.S. Equities. Based on historical correlations over the entire ten years, we expect the following asset classes to provide some diversification benefits relative to a U.S. Equity-dominated portfolio: U.S. Bonds ( ρ= -0.21) REITs ( ρ = 0.31) Hedge Funds of Funds ( ρ = 0.46) 18 Private Equity (ρ = 0.61) In times of market stress, however, these correlations break down. Exhibit 15 shows a simple correlation matrix for the high-volatility period from August 1998 to September We have highlighted in orange where correlations have converged (i.e. reducing diversification) and in green where correlations have diverged (i.e. increasing diversification). anticipated. The correlation coefficients with U.S. Equities were as follows: Long-run Coefficient During Market Stress U.S. Bonds REITs Hedge Fund of Funds Private Equity Our analysis reaffirms our intuition, suggesting ample evidence of correlation convergence during periods of market stress. Again, conventional frameworks which assume normality in this case normal distribution of joint returns prove inadequate to account for what we observe in real-world markets. And inappropriately assuming linearity of correlations can lead to a significant underestimation of joint negative returns during a market downturn. The results are striking. Nearly all the correlation coefficients increased to some degree, with only four showing a decrease. Our main diversifiers did not provide the diversification benefits Exhibit 15: Correlations during high volatility period (AugUST 1998 SeptEMBER 1999) U.S. Bonds U.S. Bonds 1.00 U.S. Equity U.S. Equity International Equity International Equity Emerging Markets Equity Emerging Markets Equity Real Estate Investment Trusts Real Estate Investment Trusts (REITs) Hedge Fund of Funds Hedge Fund of Funds Private Equity Private Equity Private Equity may be seen as a return enhancer rather than a diversification play. We include it in our diversification analysis despite a relatively high correlation for illustrative purposes. J.P. Morgan Asset Management 13

16 Incorporating non-normality into an asset allocation framework Based on empirical testing, we have demonstrated that conventional asset allocation frameworks understate downside risk because they do not allow for non-normality. However, we believe that achieving optimal portfolio efficiency based on a more precise estimation of portfolio risk requires investors to incorporate non-normal return distributions into the asset allocation and portfolio optimization process. In this section, we offer a statistical methodology for incorporating the three categories of non-normality already discussed. We address them in the same order as they were presented in the previous section: A. Serial correlation B. Fat left tails (negative skewness and leptokurtosis) characteristics of the underlying data generating process. Notably, our new series should have a higher volatility, demonstrate no serial correlation and have a cross correlation structure with other asset classes similar to our original data. Our procedure for unsmoothing returns (or correcting for serial correlation in the underlying data series) is a two-step process: STEP 1: We determine the correlation coefficient at lag one 20 (i.e. previous month s return) for each return series, by running the following regression: R t = a + br (t-1) where R t represents the return at time t The regressions indicate the following estimates (Exhibit 16) for the serial correlation coefficients, based on monthly returns. C. Correlation breakdown (converging correlations) A. Incorporating the Impact of Serial Correlation Our empirical work in the last section shows that certain asset classes International Equity, Emerging Markets Equity, Hedge Fund of Funds and Private Equity test positive for serial correlation. This result implies that, over time, single period returns are influenced by, and not independent from, previous periods. First-order serial correlation, if not corrected for in the underlying data, will mask true asset class volatility and bias risk estimates downwards, leading to underestimation of asset class risk and, hence, portfolio risk. In order to adjust for the impact of serial correlation on asset returns, we apply a variation of Fisher-Geltner-Webb s unsmoothing methodology 19, whereby we re-calculate an unsmoothed return stream from our original data. The new adjusted return series should be more reflective of the risk Exhibit 16: Correlation coefficients at lag one ^b Statistically significant* International Equity 0.21 Yes Emerging Markets Equity 0.25 Yes Hedge Fund of Funds Yes Private Equity 0.21 Yes Source: J.P. Morgan Asset Management. Note the coefficient b reflects the strength of the autocorrelation. For illustrative purposes only. * Statistically significant at the 5% level 19 For a more detailed treatment, please refer to Fisher, J., D. Geltner, and B. Webb Value Indices of Commercial Real Estate: A Comparison of Index Construction Methods. Also, Fisher, J. and D. Geltner De-Lagging the NCREIF Index: Transaction Prices and Reverse-Engineering. 20 Note International Equity, Emerging Markets Equity, Hedge Fund of Funds and Private Equity returns test positive for serial correlation at lag one. 14 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

17 STEP 2: We then produce our unsmoothed return series as follows, based on the serial correlation coefficient already derived: R t (corrected) = (R t br ^ t-1 ) / (1 b) ^ After applying the methodology outlined above, we obtain a new data series R t (corrected) that should display no serial correlation. Once again we use the Q statistic to test the unsmoothed data for serial correlation. The results are shown below (Exhibit 17). Exhibit 17: Tests for serial correlation on corrected data for up to six lags Test statistic p-value Evidence of serial correlation International Equity No Emerging Markets Equity No Hedge Fund of Funds No Private Equity No The test indicates that our revised data is no longer serially correlated. More important, our transformations of the underlying data series do not alter the mean return of the series over the time period considered. However, removing the effects of serial correlation does increase the standard deviation of returns, as shown in Exhibit 18 below. As discussed, the higher volatility of our unsmoothed return series is in line with our expectations, as serial correlation masks volatility and understates risk. Exhibit 18: Volatilities before and after unsmoothing for serial correlation Annualized volatility before unsmoothing Annalized volatility after unsmoothing International Equity 15.25% 18.93% Emerging Markets Equity 24.21% 31.42% Hedge Fund of Funds 6.51% 10.30% Private Equity 23.51% 29.17% Finally, we evaluate the impact on asset correlations to assess the viability of our unsmoothed data series as our unsmoothing procedure should not affect correlations. We compare our original correlation matrix using index data (Exhibit 19) with our new correlation matrix derived from unsmoothed data (Exhibit 20). We see that, as anticipated, the impact of unsmoothing on asset correlations is negligible. B. Incorporating the Impact of Fat Left Tails into our Framework All of our asset classes show fat left tails, compared to the normal distribution. Our approach to dealing with such left tail risk involves what is called a semi-parametric approach : that is, instead of modeling returns using a single distribution (such as is traditionally done with the normal distribution), we fit the left and right tails separately from the interior of the distribution. Hence, we segment each return distribution into three parts the left tail, right tail and interior (See Exhibit 21). We then fit each segment of the distribution separately. This approach enables us to more accurately capture the potential distributions associated with the empirical data in each segment of the distribution. To fit the left and right tails of the distribution we turn to Extreme Value theory a body of work specifically designed to look at the probability of low-probability but high-risk events. The central premise behind Extreme Value theory is that the probability of observing extreme values (such as negative financial market returns), can be modeled using any one of several Extreme Value distributions. 21 Our choice of Extreme Value distribution is the Generalized Pareto distribution (GPD). We choose the GPD distribution, rather than other Extreme Value distributions, due to the ease with which it can be adapted to modeling financial market returns. 21 An Extreme Value distribution is defined as the limiting distribution of extreme values sampled from any independent randomly distributed process. J.P. Morgan Asset Management 15

18 Exhibit 19: Correlations based on smoothed (or raw) data over ten years to October 2008 U.S. Bonds U.S. Bonds 1.00 U.S. Equity U.S. Equity International Equity International Equity Emerging Markets Equity Emerging Markets Equity Real Estate Investment Trusts Real Estate Investment Trusts (REITs) Hedge Fund of Funds Hedge Fund of Funds Private Equity Private Equity Exhibit 20: Correlations based on unsmoothed data over ten years to October 2008 U.S. Bonds U.S. Bonds 1.00 U.S. Equity U.S. Equity International Equity International Equity Emerging Markets Equity Emerging Markets Equity Real Estate Investment Trusts Real Estate Investment Trusts (REITs) Hedge Fund of Funds Hedge Fund of Funds Private Equity Private Equity Exhibit 21: Hypothetical dissection of return distribution Left tail Interior Right tail 16 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

19 The Fitting Process is Done in Two Steps: First: Fitting the left (and right) tail of the Generalized Pareto distribution (GPD) to our historical data. This is done by calibrating the tail of the GPD to extreme values in the sample using the Method of Maximum Likelihood. Extreme values are defined by a threshold. This method is commonly referred to as the Peaks-Over-Threshold method within Extreme Value theory. Second: Fitting the interior of a distribution is accomplished using a non-parametric (kernel or empirical) approach. What we are looking for is a better fit to the actual distribution of empirically observed data (blue line) as shown in the kernel density function. What we see is that in each case ( Exhibits 22 through 28), the GPD distribution (orange line) is a much more accurate fit to real-world data than the normal distribution. The improved fit is particularly noticeable in the left tail, where the normal distribution understates risk. Hence, in aggregate, we derive a semi-parametric probability density function to describe the data generating process. We refer to this broad approach as a semi-parametric GPD approach. We can test the results of the fitting process against both the normal and kernel (or empirical) distributions used to establish fat left tails in the previous section: Generalized Pareto Distribution For those interested in the Generalized Pareto distribution, we provide supporting equations here. The probability density function for the generalized Pareto distribution with shape parameter k 0, scale parameter σ, and threshold parameter θ, is given by: Exhibit 22: u.s. bonds fitting the semi-parametric gpd distribution to historical data Density Empirical Semi-parametric GPD % -40% -20% 0% 20% 40% Monthly return for θ < x, when k > 0, or for θ < x < σ/k when k < 0. In the limit for k = 0, the density is for θ < x. f(xik,σ,θ) 1+k If k = 0 and θ = 0, the Generalized Pareto distribution is equivalent to the exponential distribution. If k > 0 and θ = σ, the generalized Pareto distribution is equivalent to the Pareto distribution k J.P. Morgan Asset Management 17

20 Exhibit 23: U.S. Equities Fitting the semi-parametric GPD distribution to historical data Empirical Semi-parametric GPD Exhibit 24: International Equities Fitting semi-parametric GPD distribution to historical data Empirical Semi-parametric GPD Density 6 4 Density % -30% -20% -10% 0% 10% 20% Monthly return 0-20% -15% -10% -5% 0% 5% 10% 15% Monthly return Exhibit 25: Emerging Markets Equities Fitting the semi-parametric GPD distribution to historical data Density Empirical Semi-parametric GPD 0-100% -80% -60% -40% -20% 0% 20% Monthly return Exhibit 26: U.S. REITs Fitting the semi-parametric GPD distribution to historical data Density Empirical Semi-parametric GPD 0-100% -80% -60% -40% -20% 0% 20% Monthly return Exhibit 27: Hedge fund of Funds Fitting the semi-parametric GPD distribution to historical data Density Empirical Semi-parametric GPD 0-20% -10% 0% 10% 20% 30% Monthly return Exhibit 28: Private Equity Fitting the semi-parametric GPD distribution to historical data Density Empirical Semi-parametric GPD 0-40% -30% -20% -10% 0% 10% 20% 30% 40% Monthly return 18 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

21 To enhance our conviction in these results, we need to statistically test this goodness of fit both for the whole distribution as well as specifically for the tails. Below we apply the Kolmogorov-Smirnov (KS) 22 empirical distribution test under two hypotheses: first, the null hypothesis that returns are normally distributed, and second, under the hypothesis that they are sampled from a semi-parametric GPD. Exhibit 29 below sets out our results. We also show the probability that our null hypothesis stands statistical significance. Our empirical tests confirm our view that the semi-parametric GPD distribution provides a better fit for the historical data when compared to the normal distribution for all our asset classes. Given our focus is on left tail risk, however, we can also gauge fit by considering the Quantile-Quantile (QQ) plots of the empirical data against those implied by the semi-parametric GPD distribution 23. We see that in each case, the lowest quintiles are a much better fit with the semi-parametric GPD distribution than with the normal distribution, indicating that the semi-parametric GPD distribution more accurately captures the frequency of extreme negative events. Exhibit 29: Testing goodness of fit of Normal and semi-parametric GPD distribution Normal Semi-parametric GPD KS statistic p Value Reject Normality KS Statistic p Value Reject Semiparametric GPD U.S. Aggregate Bonds Yes No U.S. Large Cap Equity Yes No EAFE Unhedged Equity Yes No Emerging Markets Yes No Real Estate Investment Trusts (REITs) Yes No Hedge Fund of Funds Yes No Private Equity Yes No Exhibit 30: U.S. Bonds QQ Plot (Normal Distribution) Exhibit 31: U.S. Bonds QQ Plot (Semi-parametric GPD distribution) Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data 22 The Kolmogorov-Smirnov test is a goodness of fit test to test whether a sample comes from a particular distribution. Our null hypothesis is tested at 5% significance. 23 QQ plots illustrate how different quantiles of a sample compare to those implied by the theoretical density function. All data points being on a straight 45 degree line indicate a perfect fit of the data with the distribution. J.P. Morgan Asset Management 19

22 Exhibit 32: U.S. Equities QQ Plot (Normal Distribution).12 Exhibit 33: U.S. Equities QQ Plot (semi-parametric GPD distribution ).15 Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data Exhibit 34: International Equities QQ Plot (Normal Distribution).15 Exhibit 35: International Equities QQ Plot (semi-parametric GPD distribution ).15 Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data Exhibit 36: Emerging Markets Equities QQ Plot (Normal Distribution).20 Exhibit 37: Emerging Markets Equities QQ Plot (semi-parametric GPD distribution ).2 Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data 20 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

23 Exhibit 38: REITs QQ Plot (Normal Distribution) Exhibit 39: REITs QQ Plot (semi-parametric GPD distribution ).15.1 Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data Exhibit 40: Hedge fund of Funds QQ Plot (Normal Distribution).06 Exhibit 41: Hedge fund of Funds QQ Plot (semi-parametric GPD distribution).12 Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data Exhibit 42: Private Equity QQ Plot (Normal Distribution) Exhibit 43: Private Equity QQ Plot (semi-parametric GPD distribution ).20.3 Quantiles of Normal Quantiles of semi-parametric GPD Quantiles of data Quantiles of data Note that the results shown are for illustrative purposes and are based on applying Extreme Value theory to the raw smoothed data. In our framework because we require independence of asset returns we remove the effect of serial correlation before fitting a semi-parametric GPD. J.P. Morgan Asset Management 21

24 C. Incorporating the impact of converging correlations into our framework One of our empirical findings in the previous section was that correlations converge during periods of high market volatility and stress. Our approach to dealing with this phenomenon is to apply copula theory a body of work that explicitly looks at the impact of contagion or converging correlations at the total portfolio level. Copulas are mathematical functions that allow us to model the joint distribution of asset returns separately from the marginal (or individual asset class) distributions. By considering joint distributions, we turn our focus to how asset classes behave together (rather than individually, as we did in the last section). In a traditional framework, joint distributions are captured by simple (Pearson) correlations. Unfortunately, simple correlations assume a linear relationship between individual asset classes, and we have already shown empirically that linearity breaks down at the extremes. Copulas, on the other hand, allow us to differentiate the relationship between asset classes in times of market stress, from more normal times. In particular, we observe empirically that not only do individual asset classes have fatter left tails than allowed in a normal distribution, combinations of asset classes exhibit fat joint left tails i.e. a higher incidence of joint negative returns in times of market stress. Copulas allow us to model both the fat marginal left tails for individual asset classes (using the semi-parametric GPD approach explained earlier), while at the same time modeling more accurately the fat joint left tails (joint negative returns) as empirically observed. Just as the application of Extreme Value Theory involves choosing from among several types of Extreme Value Distributions, applying copula theory requires us to choose a copula type. Here we use the t-copula (based on the Student t distribution) 24 because it enables us to better capture the effects of converging correlations i.e. allows fatter tails than the normal distribution. 24 The Student t distribution comes from the same family of distributions as the Normal. A Student t distribution is calibrated on only one parameter i.e. the degrees of freedom. The higher the degrees of freedom, the closer it is to a normal. At 30 degrees of freedom, the Student t distribution is virtually identical to the normal distribution. Hence, assuming a lower degree of freedom parameter imposes significant increased dependence between asset classes returns. This approach better reflects the empirical observations of converging correlations during periods of high volatility, which tend to be accompanied by negative returns. Specifically, we model joint fat left tails by adjusting the copula s degree-of-freedom parameter: 30 degrees-of-freedom is virtually identical to a normal distribution. By lowering the parameter, we can increase asset class dependence to model observed asset class behavior in times of market stress. In Exhibit 44, we illustrate the impact of modifying the normal marginal distributions by adding a t-copula with a low degreeof-freedom parameter. Exhibit 44: Modeling joint dependence in asset returns using copulas Monthly simulated Hedge Fund of Funds returns Monthly simulated Hedge Fund of Funds returns 10% 8% 6% 4% 2% 0% -2% -4% -6% -8% 10% 8% 6% 4% 2% 0% -2% -4% -6% -8% Illustration of traditional linear correlations -10% -20% -15% -10% -5% 0% 5% 10% 15% 20% Monthly simulated U.S. Large Cap Equity returns Illustration of increased joint dependence through copulas -10% -20% -15% -10% -5% 0% 5% 10% 15% 20% Monthly simulated U.S. Large Cap Equity returns 22 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

25 In order to show the effect of modeling increased joint dependence, we produce a scatter plot of 2,500 simulations of U.S. Large Cap Equity returns and Hedge Fund of Funds returns first, assuming normality with simple correlations; and second, assuming normality with a Student t-copula added (with two degree of freedom 25 ). Compared to the traditional multivariate normal framework, our simulations show the impact of increased dependence, through a higher joint incidence of negative returns between U.S. Large Cap Equities and Hedge Fund of Funds (highlighted). This illustrates the phenomenon of fat joint left tails we alluded to earlier. Our simulations also illustrate increased dependence of other extreme joint occurrences. In other words, we can see an increased incidence of negative U.S. Large Cap Equity returns with positive Hedge Fund of Funds returns (the top left quadrant) or positive U.S. Equity returns with positive Hedge Fund of Funds returns (the top right quadrant). This is visible as the star pattern in the scatter plot. This occurs because our Student t distribution is symmetric around the mean (like the Normal distribution) and, hence, incorporating fat joint tails increases the probability of observing all extreme joint outcomes (positive and negative) not just extreme joint negative outcomes. This can be seen as a drawback of our choice of the Student t distribution, as in reality correlations do exhibit asymmetric behavior, i.e. the convergence properties are different in extreme positive markets compared to extreme negative markets. Exhibit 45: Incorporating non-normality Summary of model Step 1: Input historical data Step 2: Remove serial correlation from returns Unsmoothed data Step 3: Fit marginal distributions (using Extreme Value theory) to allow for fat left tails Step 4: Calibrate Student t-copula to allow for joint fat tails i.e. increased dependence during periods of market stress However, our choice of t-copula is a vast improvement when compared to linear correlation matrices used in traditional models, as the latter completely ignore the effect of correlation convergence. Incorporating fat joint left tails using copula theory allows us to more robustly model the empirical phenomenon of correlation breakdown during periods of market stress. 25 Note our model calibrates the degrees of freedom parameter for the Student t distribution assuming the marginal distributions are semi-parametric GPD. However, to illustrate the effect of adding the copula, we choose a low degree of freedom parameter of two and assume the marginal distributions are normal (rather than semi-parametric GPD). This allows us to isolate and illustrate the effect of fat joint tails separately from fat marginal tails. J.P. Morgan Asset Management 23

26 Estimating downside portfolio risk in a non-normal framework Now that we have a statistically solid framework for incorporating non-normality into the asset allocation process, we can apply this framework to a hypothetical U.S. domiciled investor. We will assume a well-diversified portfolio with an initial value of $1 billion and allocations across our seven major asset classes. Detailed allocations to each asset class are shown in Exhibit 46 below. We can now generate forward looking projections of our hypothetical portfolio s real value 26 in our newly revised Monte Carlo simulation framework. For each of our ten forward years, we generate 10,000 simulations 27. We then measure downside portfolio risk as the Conditional Value at Risk at the 5 th percentile of the portfolio. Exhibit 46: Hypothetical portfolio allocation Asset class Current Allocation Total bonds 30% U.S. Aggregate Bonds 30% Total equity 55% U.S. Large Cap Equity 40% International Equity (hedged) 10% Emerging Markets Equity 5% Total alternatives 15% REITs 5% Hedge Fund of Funds 5% Private Equity 5% Key statistics Expected arithmetic return 9.1% Expected volatility 10.0% Expected compound return 8.7% Sharpe ratio 0.51 Sharpe ratio calculated based on expected risk free return of 4.0% per year as per J.P. Morgan Asset Management Long Term Capital Market Assumptions (please see Appendix for details). We define Conditional Value at Risk (CVaR 95 ) as the average (real) portfolio loss (relative to the starting value) in the worst five percent of scenarios, based on our 10,000 Monte Carlo simulations. It is simply the average real loss in the worst 500 (5% of 10,000) scenarios (i.e. the left tail of the portfolio distribution). A Better Risk Quantifier: Conditional Value at Risk We believe that empirical evidence suggests an imperative to incorporate various types of non-normality into the asset allocation and portfolio modeling process, specifically to better understand and model downside portfolio risk. Yet if we take this step, we have to ask whether or not our conventional risk measure (i.e. standard deviation) is up to the new task. We would argue that, in a framework based on non-normality, standard deviation may not be investors most appropriate measure of portfolio risk because it punishes the desirable upside movements as hard as it punishes the undesirable downside movements. This is generally inconsistent with investor risk preferences primarily as observed in the field of behavioral finance 28. Conditional Value at Risk (CVaR 95 ) overcomes many of the drawbacks of standard deviation as a risk measure. Primarily, as it only measures risk on the downside, it captures both the asymmetric risk preferences of investors and the incidence of fat left tails induced by skewed and leptokurtic return distributions. Further, given the widespread use by major institutional investors and regulators of its first cousin Value at Risk we judge it to be the most appropriate risk measure to incorporate it into our framework. 26 Our model calculates real portfolio value by discounting the nominal portfolio value using projected inflation. Inflation itself is projected stochastically in our framework. 27 We believe 10,000 simulations should reduce simulation error sufficiently to allow us to draw robust inferences from the results. 28 A key tenet of behavioral finance is the idea of loss aversion i.e. a tendency of investors to prefer avoiding losses than making gains. This translates to risk preferences that are asymmetric in nature. 24 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

27 The calculation for CVaR 95 is illustrated graphically for the current allocation below. Exhibit 47 shows the projected frequency of the portfolio s real gains (or losses) at the end of ten years. We calculate CVaR 95 by taking the average real cumulative loss in the worst 5% of simulations. Exhibit 47: Histogram of projected cumulative portfolio gain (loss) at the end of ten years assuming non-normality Frequency (489) (289) (90) ,108 1,307 1,507 1,706 1,906 2,106 2,305 2,505 2,704 2,904 3,104 3,303 3,503 3,702 3,902 4,101 4,301 4,501 4,700 4,900 5,099 5,299 5,498 5,698 5,898 Expected portfolio gain (loss) at the end of ten years ($ mm) CVaR 95 is defined as the average real cumulative loss in the worst 5% or 500 simulations. This is equal to $168 million for the current portfolio. We should note that while the increased risk associated with modeling non-normality is striking, incremental risk in itself is not necessarily a reason for changing a plan s asset allocation (unless an absolute threshold value has been breached). More specifically, if one assumes an arbitrarily (but equally) higher downside risk for all asset classes, this in itself would not impact the efficiency of the portfolio i.e. its efficiency would not change at all, albeit the CVaR 95 would now be higher. The reason non-normality can impact asset allocation is that the downside risk associated with different asset classes is very different. Most obviously, equity and equity type asset classes entail greater degrees of downside risk than, for example, fixed income type investments. Hence, because the downside risk characteristics of different asset classes are different and cannot be accounted for using traditional modeling techniques or risk measures such as standard deviation the efficient allocations in our CVaR 95 motivated non-normal framework must also be different from a traditional framework. For this reason not merely due to higher CVaR 95 figures we believe investors need to quantitatively incorporate the impact of non-normality into the asset allocation process. In the next section, we consider the impact of non-normality on optimized portfolio solutions for investors with a long-term investment horizon. The CVaR 95 of the current allocation, based on our new methodology, is $168 million. In other words, the portfolio can expect to lose (on average) $168 million in the worst five percent of cases (based on our simulation results). This risk is significant. It indicates a real return (i.e. after allowing for inflation) of -16.8% on the portfolio over an extended time horizon a result our investor is unlikely to be very happy with. For the same portfolio, however, risk calculations that assume normality 29 would result in a CVaR 95 figure of $74 million. Incorporating non-normality more than doubles our prior estimate of CVaR 95. In absolute terms, the risk underestimation is $94 million or 9.4% of the portfolio s initial value. 29 Our estimates of CVaR 95 under a traditional framework were derived with an identical asset allocation, except that asset returns were assumed to be individually and jointly normally distributed. Risk and correlations were derived based on the same ten year historical period from November 1998 to October J.P. Morgan Asset Management 25

28 The reason non-normality can impact asset allocation is that the downside risk associated with different asset classes is very different. Most obviously, equity and equity type asset classes entail greater degrees of downside risk than, for example, fixed income type investments. 26 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

29 Implications of non-normality on optimal portfolio solutions In this section we assess the impact of non-normality on portfolio efficiency, using CVaR 95 as the risk measure of choice 30. Our optimization process involves minimizing CVaR 95 for a given expected return (or equivalently, maximizing return for a given CVaR 95 ). In any case, the portfolios we derive here are efficient in that they offer investors the optimal tradeoff between (downside) risk and reward. The optimization process is based on linear programming techniques 31 applied to 10,000 Monte Carlo simulations (generated in the previous section). We construct an efficient frontier comprising ten efficient portfolios: Our first portfolio comprises our minimum CVaR portfolio 95 such that there is no other asset allocation (derived from our simulations) that minimizes the portfolio s CVaR 95 further over ten years. It is our minimum risk portfolio. Our tenth portfolio comprises our highest return portfolio such that there is no other asset allocation (derived from our simulations) that maximizes the return of the portfolio further over ten years. The intermediate eight portfolios are equally distributed between the first and tenth in terms of expected return, but minimizing CVaR 95 over ten years at each point along the efficient frontier. 30 It is worth noting that when optimizing under the assumption of normality, the choice of risk measure (whether standard deviation or CVaR 95 ) is irrelevant. This is because a normal distribution is symmetric about its mean so choosing a different percentile (e.g. two standard deviations) does not impact the derived solution. Under non-normality, the choice of risk measure is critical to the derived solution. 31 Linear programming is required because as we move away from traditional normal assumptions, there are no shortcuts to computing total portfolio risk. Individual downside risk measures such as standard deviation can no longer be aggregated to the total portfolio level using analytical formulae. 32 Our traditional efficient frontier was derived under a Monte Carlo simulation framework except that asset returns were assumed to be individually and jointly normally distributed. Exhibit 48: Efficient frontier based on traditional and non-normal framework 12% 11% 10% 9% 8% 7% 6% Expected compound return (% per year) Normal framework Non-normal Mean CVaR framework 5% (200) ,000 Source: J.P. Morgan Asset Management. Conditional Value For illustrative Risk 95 purposes ($ millions) only. In aggregate, a plot of the expected compound return (in % per year) versus the CVaR 95 (in $ millions) constitutes our efficient frontier (Exhibit 48). The efficient frontier created using a non-normal framework differs markedly from the equivalent frontier produced from a traditional model 32 built on assumptions of normality. Notably all points along the new efficient frontier demonstrate for a given expected return significantly higher risk, as captured by CVaR 95. Detailed allocations underlying each efficient frontier are shown in the Appendix. These results confirm our intuition that capturing the nonnormality associated with equity and alternative return distributions should increase the magnitude of CVaR 95 of any portfolio with an allocation to these asset classes. Crucially, as traditional frameworks fail to capture non-normality, they underestimate the downside risk associated with such portfolios. Hence, the efficient frontier derived from a traditional model is likely to show a misleading, lower risk figure for portfolios with a similar expected return. J.P. Morgan Asset Management 27

30 Optimization Constraints Optimizations in general whether based on modern or, as in our case, post-modern portfolio theory suffer from certain inherent drawbacks. A particular drawback associated with traditional frameworks is the sheer number of constraints applied to the optimization process. Constraints reduce the credibility of the optimization process as, by applying constraints, the practitioner is guiding the model towards a particular solution rather than allowing the model to calculate one itself independently. In deriving our optimal portfolios, we do not apply any constraints. Hence, we let the model dictate the direction and magnitude of results. Optimal Portfolio Solution for our Hypothetical Investor In this section, we ask ourselves how incorporating non-normality into our return distributions affects our optimal portfolio solution for our hypothetical investor. Exhibit 49 compares our current hypothetical investor with a portfolio allocation using an identical target arithmetic return of 9.1%, but optimized using our revised non-normal framework 33. For illustrative purposes, we also show the optimized allocation derived from a traditional-mean variance framework. We do not impose any constraints on our optimization process. 33 All optimizations are based on J.P. Morgan Long Term Capital Market Return Assumptions. Exhibit 49: Optimization results Sharpe ratio calculated assuming risk free return of 4.0%. Current allocation Optimized, Unconstrained Normal allocation Optimized, Unconstrained Non-normal allocation Total bonds 30.0% 0.0% 34.5% U.S. Aggregate Bonds 30.0% 0.0% 34.5% Total equity 55.0% 18.3% 36.0% U.S. Large Cap Equity 40.0% 8.9% 21.7% International Equity 10.0% 7.6% 5.8% Emerging Markets Equity 5.0% 1.9% 8.5% Total alternatives 15.0% 81.7% 29.5% REITs 5.0% 11.9% 11.1% Hedge Fund of Funds 5.0% 64.1% 7.0% Private Equity 5.0% 5.6% 11.5% Total 100.0% 100.0% 100.0% Key statistics Target expected arithmetic return 9.1% 9.1% 9.1% Expected volatility 10.0% 8.6% 9.5% Expected compound return 8.7% 8.6% 8.7% Sharpe ratio CVaR 95 ($ million) allowing for non-normality $168 mm $206 mm $148 mm CVaR 95 vs. current allocation - 23% 12% Return per unit of CVaR Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

31 Our results indicate that the optimal portfolio using a traditional mean-variance framework actually increases (rather than decreases) risk by 22.6% relative to the current allocation as defined by CVaR 95. As a traditional framework minimizes standard deviation 34 which we argue is an inadequate risk measure it inadvertently exposes our investor to even worse scenarios on the downside than the current allocation. However, the more significant issue by far and the biggest drawback of the traditional approach is that it produces a highly concentrated and impractical asset allocation. This is because in the absence of formal constraints, it over allocates to asset classes based on small differences in assumptions. This limits our ability to draw useful insights into the portfolio construction process, using such a framework. On the other hand, our non-normal CVaR 95 based framework produces a diversified solution with allocations across the asset class spectrum. No single asset class significantly dominates the portfolio. Despite the fact that our investor already holds quite a diversified portfolio, our framework suggests that there is still scope for our investor to improve portfolio efficiency further. The optimal portfolio identified by the non-normal framework improves both the expected Sharpe ratio and reduces the CVaR 95 relative to the current allocation. This signifies that our optimal portfolio is more efficient (in Sharpe ratio and CVaR 95 space) than the current hypothetical portfolio. Based on our Long Term Capital Market Return assumptions, the allocations to fixed income and alternatives increase, while the allocation to equities decreases. 34 Minimizing standard deviation for a given expected return target is the equivalent of maximizing Sharpe ratio. J.P. Morgan Asset Management 29

32 Comparing a Traditional Model with a Non-normal Framework So, how does each form of non-normality affect our optimal portfolio solution relative to a traditional mean-variance optimization framework? Exhibit 50 shows the marginal and then combined impact of each form of non-normality on the optimal portfolio solution, when compared to a traditional mean-variance framework 35. (Note the analysis below is based on J.P. Morgan s Long Term Expected Return Assumptions. It is worth noting that the results below will vary depending on the expected return assumptions i.e. relative attractiveness of the asset classes.) Each arrow in the table indicates the broad directional impact of the particular form of non-normality on the allocation to the asset class relative to the allocation implied by a mean-variance approach. For example, an up arrow indicates the allocation to the asset class increases in a non-normal framework, relative to the allocation implied by a traditional framework. Each subsequent column allows for the prior form (or forms) of non-normality hence, the attribution in each case is truly marginal. Exhibit 50: Attribution analysis of non-normality U.S. Bonds U.S. Equity International Equity Emerging Markets Equity REITs Hedge Fund of Funds Private Equity Phenomenon 1: Serial correlation Phenomenon 2: Fat left tails Phenomenon 3: Converging correlations 35 Note the impact of the form of non-normality on the optimal portfolio solution varies along the efficient frontier. The analysis in this section is broadly representative of a portfolio with an expected (arithmetic) return of 9.0% based on our Long Term Capital Market Return Assumptions. Serial correlation The impact of serial correlation is negative for International Equity, Emerging Markets Equity, Hedge Fund of Funds and Private Equity as these asset classes test positive for first order serial correlation. Recall that the presence of serial correlation increases the risk associated with the particular asset class hence its negative marginal impact. As U.S. Bonds, U.S. Equity and REITs do not test positive for serial correlation, the marginal impact for these asset classes is positive (relative to a traditional mean-variance framework). Fat left tails Our second form of non-normality fat left tails has a negative marginal impact on optimal allocations to U.S. Equities, REITs and Hedge Fund of Funds (relative to a traditional mean-variance framework). This is driven by the asset classes distributional properties its negative skewness, excess kurtosis and extreme negative values (all of which are bad for investors). In particular, our model indicates higher excess kurtosis for U.S. Equity, REITs and Hedge Fund of Funds compared to U.S. Bonds, International Equity, Emerging Markets Equity and Private Equity (after allowing for serial correlation). This implies that these asset classes have fatter left tails than the other asset classes, when compared to a normal distribution. Hence, our model reduces allocations to U.S. Equity, REITs and Hedge Fund of Funds (relative to a traditional framework) due to the prevalence of this phenomenon. Converging correlations Our calibration results for the copula and optimization results suggest correlation convergence during periods of market stress is much more pronounced for International Equity and Hedge Fund of Funds (after allowing for serial correlation and fat left tails). Hence, the model reduces allocations to these asset classes relative to traditional mean-variance frameworks. In other words, the degree of non-linearity in correlations is much more severe for International Equity and Hedge Fund of Funds, compared to the other asset classes. This implies that diversification benefits from these asset classes do not materialize to the extent implied by linear correlation matrices (during periods of market stress). This detracts from relative allocations. 30 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

33 Conclusion Incorporating non-normality can lead to more efficient portfolios Until recently, investors have been constrained in their ability to incorporate non-normality into the asset allocation process. But now, with the availability of sophisticated statistical tools, we can meet this challenge. Why should we change? The most straightforward answer is that this is how the world really works i.e. we empirically observe non-normality with much greater frequency that current mean-variance frameworks allow for. The more important answer is that ignoring non-normality in equity (and equity type) return distributions significantly understates downside portfolio risk in the worst of the worst-cases, potentially posing a solvency risk for the investor. We also believe that investors need to allow for downside risk in a more robust fashion than standard deviation measures have traditionally assumed. For this reason we recommend CVaR 95. This measure is a better fit for investors asymmetric risk preferences, as well as the fat left tails recognized by non-normal asset allocation frameworks. Finally, an asset allocation incorporating non-normality has the benefit of reducing the need for external constraints. Investors often impose such constraints in an effort to get normal frameworks to provide non-normal solutions i.e. to better reflect the non-normality we see in the real world. A framework that builds in non-normality up front, however, provides much a more direct, efficient, and elegant way of addressing the problem. Ultimately, we believe the quantitative results illustrate the point best, and speak for themselves: incorporating non-normality may reduce the portfolio s volatility, improve its efficiency (Sharpe ratio), and reduce its risk relative to unpredictable, extreme negative events. So, we argue for a new asset allocation framework because beyond its pure statistical merit, there lies a significant, practical benefit for investors: the potential to improve portfolio efficiency and resilience, in light of a clearer understanding of portfolio risk. Limitations of reliance It should be noted that a quantitative framework is only one input into the asset allocation process and cannot replace the professional skill and judgment necessary to arrive at an appropriate strategy. The importance of allowing for subjective and often qualitative factors in decision making remains. Further, there is always an explicit need to account for the investor s specific circumstances, including liabilities, when arriving at an appropriate portfolio allocation. J.P. Morgan Asset Management 31

34 Appendix Exhibit 51: J.P. Morgan Asset Management Asset Allocation Framework Graphical User Interface 32 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

35 Detailed Asset Allocations and Assumptions Underlying Efficient Frontiers Exhibit 52: Efficient frontier portfolio allocations (Traditional Mean-Variance based on J.P. Morgan Long Term Expected Returns) Mean CVaR Allocations Portfolio 1 Portfolio 2 Portfolio 3 Portfolio 4 Portfolio 5 Portfolio 6 Portfolio 7 Portfolio 8 Portfolio 9 Portfolio 10 U.S. Aggregate Bonds U.S. Large Cap Equity International Equity Emerging Markets Equity Real Estate Investment Trusts Hedge Fund of Funds Private Equity Exhibit 53: Efficient frontier portfolio allocations (CVaR 95 motivated non-normal framework based on J.P. Morgan Long Term Expected Returns) Mean CVaR Allocations Portfolio 1 Portfolio 2 Portfolio 3 Portfolio 4 Portfolio 5 Portfolio 6 Portfolio 7 Portfolio 8 Portfolio 9 Portfolio 10 U.S. Aggregate Bonds U.S. Large Cap Equity International Equity Emerging Market Equity Real Estate Investment Trusts Hedge Fund of Funds Private Equity j.p. morgan asset management Long-term capital market return assumptions Assets Expected arithmetic return Historic annualized volatility Historic correlations U.S. Inflation 2.76% 1.30% 1.00 U.S. Aggregate Bonds 5.59% 3.58% U.S. Large Cap Equity 10.36% 15.15% International Equity 10.61% 15.27% Emerging Market Equity 13.50% 24.04% Real Estate Investment Trusts 9.95% 18.17% Hedge Fund of Funds 8.12% 6.49% Private Equity 13.09% 23.36% Based on ten years of monthly returns from November 1998 October J.P. Morgan Asset Management 33

36 Bibliography Bacmann, F., Gawron, G Fat tail risk in portfolios of hedge funds and traditional investments. Bouye, E., Durrleman, V., Nikeghbali, A., Riboulet, G., Roncalli, T. (2000), Copulas for Finance: A Reading Guide and Some Applications. Christoffersen, P., Diebold, F.X., and Schuermann, T. (1998), Horizon Problems and Extreme Events in Financial Risk Management, Economic Polcy Review, Federal Reserve Bank of New York, October, Coleman, M., Mansour, A. Real Estate in the Real World: Dealing with Non-Normality and Risk in an Asset Allocation Model. David Geltner, 1999, Using the NCREIF Index to Shed Light on What Really Happened to Asset Market Values in 1998: An Unsmoother s View of the Statistics. Nystrom, K., Skoglund, J. (2002), Univariate Extreme Value Theory, GARCH and Measures of Risk, Preprint, Swedbank. Patton, A. (2006) Copula-Based Models for Financial Time Series Rockafellar, R. and Uryasev, S. (1999) Optimization of Conditional Value at-risk. Roncalli, T., Durrleman, A., Nikeghbali, A., (2000), Which Copula Is the Right One? Zeevi, A., Mashal, R., (2002), Beyond Correlation: Extreme Comovements between Financial Assets, Preprint, Columbia University. Dorey, M., Joubert, P. Modelling Copulas: An Overview (The Staple Inn Actuarial Society). Embrechts, P., McNeil, A., Straumann, D. (1999), Correlation and Dependence in Risk Management: Properties and Pitfalls. Fernandez, V. (2008) Copula-based measures of dependence structure in assets returns. Fisher, J., D. Geltner, and B. Webb Value Indices of Commercial Real Estate: A Comparison of Index Construction Methods. Also, Fisher, J. and D. Geltner De-Lagging the NCREIF Index: Transaction Prices and Reverse-Engineering. Georges Gallais-Hamonno, Huyen Nguyen-Thi-Thanh, 2007, the necessity to correct hedge fund returns: empirical evidence and correction method. Giliberto, M. (2004) Assessing Real Estate Volatility, Journal of Portfolio Management Real Estate Hu, W Portfolio optimization for t and skewed t returns. Idzorek, T.M Developing Robust Asset Allocations. Longin, F The choice of the distribution of asset returns: How extreme value theory can help? Markowitz, H., Portfolio Selection, New York, NY: John Wiley & Sons, Miller, M., Muthuswamy, J., Whaley, R. (1994) Mean Reversion of Standard & Poor s 500 Index Basis Changes: Arbitrage-Induced or Statistical Illusion? Nelsen R.R An Introduction to Copulas. Springer, New-York. 34 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

37 Authors Abdullah Z. Sheikh, FIA, FSA Director of Research Strategic Investment Advisory Group Hongtao Qiao, FRM Strategic Advisor Strategic Investment Advisory Group J.P. Morgan Asset Management 35

38 36 Non-normality of Market Returns A Framework for Asset Allocation Decision-Making

39 IMPORTANT DISCLAIMER This document is intended solely to report on various investment views held by J.P. Morgan Asset Management. All charts and graphs are shown for illustrative purposes only. Opinions, estimates, forecasts, and statements of financial market trends that are based on current market conditions constitute our judgment and are subject to change without notice. We believe the information provided here is reliable but should not be assumed to be accurate or complete. The views and strategies described may not be suitable for all investors. References to specific securities, asset classes and financial markets are for illustrative purposes only and are not intended to be, and should not be interpreted as, recommendations. Indices do not include fees or operating expenses and are not available for actual investment. The information contained herein employs proprietary projections of expected returns as well as estimates of their future volatility. The relative relationships and forecasts contained herein are based upon proprietary research and are developed through analysis of historical data and capital markets theory. These estimates have certain inherent limitations, and unlike an actual performance record, they do not reflect actual trading, liquidity constraints, fees or other costs. References to future net returns are not promises or even estimates of actual returns a client portfolio may achieve. The forecasts contained herein are for illustrative purposes only and are not to be relied upon as advice or interpreted as a recommendation. The value of investments and the income from them may fluctuate and your investment is not guaranteed. Past performance is no guarantee of future results. Please note current performance may be higher or lower than the performance data shown. Please note that investments in foreign markets are subject to special currency, political, and economic risks. Exchange rates may cause the value of underlying overseas investments to go down or up. Investments in emerging markets may be more volatile than other markets and the risk to your capital is therefore greater. Also, the economic and political situations may be more volatile than in established economies and these may adversely influence the value of investments made. J.P. Morgan Asset Management is the marketing name for the asset management businesses of JPMorgan Chase & Co. Those businesses include, but are not limited to, J.P. Morgan Investment Management Inc., JPMorgan Investment Advisors Inc., Security Capital Research & Management Incorporated and J.P. Morgan Alternative Asset Management, Inc. 245 Park Avenue, New York, NY JPMorgan Chase & Co.

40 J.P. Morgan Asset Management 245 Park Avenue I New York, NY