Asset Allocation Policy Research Articles

Jump Mis-Specification and International Portfolio Selection

In this paper we make a distinction between systemic co-jumps and independent idiosyncratic jumps, and examine the impact of their mis-specification on asset allocation. We discuss how jumps mis-specification may lead to jumps mis-estimation and to a suboptimal portfolio. Specifically, we develop a framework where security prices have three distinct parts: a diffusion component, a systemic co-jump component, and an independent idiosyncratic jump component. In our model, the intensity and the timing of systemic co-jumps is the same across all assets whilst the timing and intensity of the idiosyncratic jump is, obviously, asset specific. The recognition that stock market returns jump together as well as separately is crucial for cross-market asset allocation policy. Univariate jump diffusion is estimated through a Markov Chain Monte Carlo (MCMC) method for weekly MSCI index returns of eleven developed and emerging markets from 1988 to 2009. The MCMC procedure produces sample paths of the latent univariate jump processes which were used in estimating the systemic and idiosyncratic jumps between series. Our results suggest that in developed markets, the omission of (idiosyncratic) jumps has little impact on portfolio selection. Developed markets are more homogeneous and their stock returns tend to jump together. The covariance matrix absorbed a large part of the linear co-jump risks producing an optimised portfolio that is not too dissimilar to one produced by a diffusion model with only the possibility for co-jumping. However, we find that, for emerging markets that tend to have idiosyncratic jumps, the assumption of co-jumps produced biased jump estimates. Losses in portfolio certainty equivalence due to this wrong jump dependence assumption are economically significant.

A meta-analysis of the equity premium

The equity premium is a key parameter in asset allocation policies. There is a vigorous debate in the literature regarding the actual measurement of the equity premium, its size and the determinants of its variation. This study aims to take stock of this literature by means of a meta-analysis. We identify how the size of the equity premium depends on the way it is measured, along with its evolution over time and its variation across regions in the world. We find that the equity premium is significantly lower if measured by ex ante methods rather than ex post, in more recent periods, and for more developed countries. In addition, looking at the underlying fundamentals, we find that larger volatility in GDP growth tends to raise the equity premium while a higher nominal interest rate has a negative impact on the equity premium. ► We conduct a meta-analysis on the literature determining the equity premium. ► The equity premium varies across time and space and also with the way it is measured. ► Larger volatility in GDP growth tends to raise the equity premium. ► A higher nominal interest rate negatively impacts the equity premium.

Overconfidence and debiasing in the financial industry

PurposeThe purpose of this paper is to measure overconfidence amongst finance professionals in domain relevant knowledge, and test for the impact of different debiasing methods.Design/methodology/approachThe approach used was survey field experiments with varying debiasing attempts.FindingsThe subjects were overconfident in terms of probability calibration, better‐than‐average beliefs, and unfounded confidence. Debiasing attempts yielded mixed results. Explicit written warnings reduced better‐than‐average‐type of overconfidence. There was a further strong effect from attending lectures on investor psychology covering relevant examples. In contrast, there was only limited success in reducing miscalibration in probability assessments.Research limitations/implicationsDifferent types of overconfidence are distinct and respond differentially to debiasing. Future research on debiasing professional judgment should concentrate on testing in‐depth/personally engaging methods.Practical implicationsIt is important for bankers to acknowledge the dangers of overconfidence. Correct confidence interval calibration is needed in order to have a sense of the risks involved in different asset allocation policies and trading strategies. Bankers should also be able to help their clients avoid overconfidence.Social implicationsDebiasing overconfidence in the finance industry likely carries public benefits. The results imply that this task is not easy, but not impossible either. The authors think further investment in this endeavor is justified.Originality/valueDocumenting an important judgment bias among finance professionals and estimating the effects of debiasing.

Rehabilitating the role of active management for pension funds

Pension fund returns can be decomposed into different sources, including market movements, asset allocation policy, and active portfolio management. We use a unique database covering the asset allocations of US defined-benefit pension funds for the period 1990–2008, and we test the role of each factor in explaining their returns. Our results shed new light on pension funds’ sources of performance. While the previous literature emphasized that policy allocation accounts for the bulk of returns, leaving little room for active management, we show that taking explicit account of market movement can change the results significantly. Although active management plays a minor role in global asset allocation, its role is predominant in explaining returns to individual asset classes, whether traditional or alternative. This paper rehabilitates the contribution of active management as a source of performance for pension funds, at least at the asset class level.

Adaptive Markets and the New World Order (corrected May 2012)

In the adaptive markets hypothesis (AMH) intelligent but fallible investors learn from and adapt to changing economic environments. This implies that markets are not always efficient but are usually competitive and adaptive, varying in their degree of efficiency as the environment and investor population change over time. The AMH has several implications, including the possibility of negative risk premiums, alpha converging to beta, and the importance of macro factors and risk budgeting in asset allocation policies.

Rehabilitating the Role of Active Management for Pension Funds

Pension fund returns can be decomposed into different sources, including market movements, asset allocation policy, and active portfolio management. We use a unique database covering the asset allocations of US defined-benefit pension funds for the period 1990-2008, and we test the role of each factor in explaining their returns. Our results shed new light on pension funds’ sources of performance. While the previous literature emphasized that policy allocation accounts for the bulk of returns, leaving little room for active management, we show that taking explicit account of market movement can change the results significantly. Although active management plays a minor role in global asset allocation, its role is predominant in explaining returns to individual asset classes, whether traditional or alternative. This paper rehabilitates the contribution of active management as a source of performance for pension funds, at least at the asset class level.

Adaptive Markets and the New World Order

The traditional investment paradigm is based on several key assumptions including rational investors, stationary probability laws, and a positive linear relationship between risk and expected return with parameters that are constant over time and which can be accurately estimated. These assumptions were plausible during the “Great Modulation” — the seven decades spanning the mid-1930s to the mid-2000s in which equity markets exhibited relatively stable risk and expected returns — but have broken down during the past decade, implying temporary but significant violations of rational pricing relationships. This tension between rational and behavioral market conditions is captured by the Adaptive Markets Hypothesis (AMH), an evolutionary perspective on market dynamics in which intelligent but fallible investors learn from and adapt to changing environments. Under the AMH, markets are not always efficient, but they are highly competitive and adaptive, and can vary in their degree of efficiency as the economic environment and investor population change over time. The AMH has several new implications for financial analysis, including the possibility of negative risk premia, the transformation of alpha into beta, and the importance of macro factors and risk budgeting in asset-allocation policies.

Asset Allocation and Consumption Choices of the Representative Agent with External Habit Formation*

In this paper, we examine the asset allocation and consumption policy of the investor with habit formation, given a set of publicly available information. We use time-separable preferences as a benchmark for comparing decisions made by an agent with habit formation. We find that external habit combined with the available information plays a prominent role in both asset allocation and consumption choices for the agent with habit. Force of habit discriminates the consumption policy with habit formation from that with time-separable preferences. The influence of external habit is found to be more pronounced and economically substantial on consumption choice than on asset allocation. In addition, we find that the intertemporal hedging demand of the agent varies considerably depending on instruments used and the investment horizon. Moreover, augmented instrument variable sets guide the agent with external habit to adopt an intertemporal hedging position that is contrary to that taken by the agent with time-separable preferences; this might seem counterintuitive but might be sensible advice when business downturn is anticipated. The degree of time nonseparability accents the external habit on the consumption policy rather than the portfolio policy.

The “New Classic” Equity Allocation? A Discussion on the Implementation of the Global Equity Allocation and Evolving Mandate Structures

The recent financial crisis led many institutional investors to review their asset allocation policies and explore alternative approaches to implementation. MSCI recently held discussions around the world with major pension plans, asset managers, and investment consultants to understand different approaches to implementing equity allocation. Following these consultations, the authors provide a framework for the implementation of global equity allocation. Our research suggests that global equity mandates, together with dedicated emerging market mandates and small-cap mandates, may be emerging as the “new classic” structure for implementing equity allocation. Investors who need to maintain a home bias can manage the domestic portfolio separately. Such a top-down mandate structure not only accrues benefits from the potential merits of an integrated global investment process, but accommodates segment-specific considerations on manager selection, legacy or mandatory home bias, and different risk and return drivers in various equity market segments. <b>TOPICS:</b>Global, emerging, portfolio theory

The FTSE StableRisk Indices

Implicit in most asset-allocation policies is the statistical assumption of “stationarity,” which means that the means, variances, and covariances of asset returns are assumed to be constant over time. This assumption is a reasonable approximation during normal market conditions but fails dramatically during periods of market turmoil and dislocation. In such periods, market volatility is highly dynamic, correlations can jump to 100% in a matter of days, and risk premia can become negative for months at a time. FTSE and AlphaSimplex Group have developed a family of rule-driven (passive), transparent, and high-capacity indices whose volatilities are rescaled as often as daily with the goal of maintaining more stable risk levels. By stabilizing the risk of each asset class over time, the FTSE StableRisk Indices have the potential to capture the long-term risk premia of asset classes and simple strategies with less severe maximum drawdowns than those of traditional indices, which have no risk controls. <b>TOPICS:</b>Passive strategies, futures and forward contracts, volatility measures, statistical methods

Stocks for the Long Run: Historical Facts and Statistical Fallacies

The doctrine of “Stocks for the Long Run” has been the cornerstone of strategic asset allocation portfolios for many decades. The catalysts of phenomenon were the contemporaneous onset of the 1980-2000 bull market, one of the strongest in history, and the emergence of modern financial theories like Modern Portfolio Theory and The Efficient Market Hypothesis. Longer historical evidence has also been used to make the case that equities invariably outperform bonds and cash over sufficiently long time horizons. However, the disappointing performance of stocks over the past decade, culminating in the 2008 market crash, has caught many investors by surprise and raised doubts about the validity of these commonly accepted investment principles.In article we take a critical look at both the theoretical principles and historical evidence to make the case that this time is different, i.e. that the Stocks for the Long Run principle may not apply for many years to come. And yet, this time is not different since the current market regime has occurred several times in the past, although history's lessons have been either disregarded and or forgotten. This has been largely due to a combination of incorrect application of the assumptions behind Modern Portfolio Theory and to an incorrect use of statistics, a problem first noted by Paul Samuelson many years ago.It can be argued that the main risks facing investors in new normal markets are a) failure to learn from history, and b) failure to understand the limitations of Modern Portfolio Theory. The challenges are not only technical, since moving away from traditional asset allocation policies and into policies more suitable for the current market cycle may entail significant career risk.

Natural Expectations, Macroeconomic Dynamics, and Asset Pricing

How does an economy behave if (1) fundamentals are truly hump-shaped, exhibiting momentum in the short run and partial mean reversion in the long run, and (2) agents do not know that fundamentals are hump-shaped and base their beliefs on parsimonious models that they fit to the available data? A class of parsimonious models leads to qualitatively similar biases and generates empirically observed patterns in asset prices and macroeconomic dynamics. First, parsimonious models will robustly pick up the short-term momentum in fundamentals but will generally fail to fully capture the long-run mean reversion. Beliefs will therefore be characterized by endogenous extrapolation bias and pro-cyclical excess optimism. Second, asset prices will be highly volatile and exhibit partial mean reversion--i.e., overreaction. Excess returns will be negatively predicted by lagged excess returns, P/E ratios, and consumption growth. Third, real economic activity will have amplified cycles. For example, consumption growth will be negatively auto-correlated in the medium run. Fourth, the equity premium will be large. Agents will perceive that equities are very risky when in fact long-run equity returns will co-vary only weakly with long-run consumption growth. If agents had rational expectations, the equity premium would be close to zero. Fifth, sophisticated agents--i.e., those who are assumed to know the true model--will hold far more equity than investors who use parsimonious models. Moreover, sophisticated agents will follow a counter-cyclical asset allocation policy. These predicted effects are qualitatively confirmed in U.S. data.(This abstract was borrowed from another version of this item.)(This abstract was borrowed from another version of this item.)(This abstract was borrowed from another version of this item.)(This abstract was borrowed from another version of this item.)(This abstract was bo(This abstract was borrowed from another version of this item.)

Cases For Asset Allocation

&lt;p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 0.5in;"&gt;&lt;span style="font-size: 10pt;"&gt;&lt;span style="font-family: Times New Roman;"&gt;Diversification is one of the most important parts of the successful portfolio investment.&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp; &lt;/span&gt;Within a diversified portfolio an investor should have wide array of assets diversified across multiple classes, including but not limited to stocks, bonds, commodities, currencies, real estates, and limited partnerships.&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp; &lt;/span&gt;There are also numbers of different investment strategies to consider when approaching the management of these assets.&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp; &lt;/span&gt;Among these management approaches are the techniques of using separately managed accounts, individual securities or brokerage accounts and mutual funds, or a combination of any or all of these.&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp; &lt;/span&gt;In addition to these management approaches, there are forces in the financial and global economic universes that need to be appropriately addressed to have effective management of these assets.&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp;&amp;nbsp; &lt;/span&gt;It is also important for investors to have the appropriate diversifications as well as asset allocations across multiple asset classes within any portfolios.&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp; &lt;/span&gt;&amp;ldquo;Asset allocation policy explains, on average, 93.6 percent of total variation in quarterly returns; in particular plans, it explains no less than 75.5 and up to 98.6 percent of total return variation&amp;rdquo; (Jahnke, William W. 1997).&lt;span style="mso-spacerun: yes;"&gt;&amp;nbsp;&amp;nbsp; &lt;/span&gt;The allocation and selection of these assets needs to be weighted and selected carefully.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

The ‘New Classic’ Equity Allocation

The recent financial crisis led many institutional investors to review their asset allocation policies and explore alternative approaches to implementation. MSCI recently held discussions around the world with major pension plans, asset managers, and investment consultants to understand different approaches to implementing equity allocation. Following these consultations, we provide a framework for the implementation of global equity allocation.Our research suggest that global equity mandates, together with dedicated emerging market mandates and small cap mandates, may be emerging as the “new classic” structure for implementing equity allocation. Investors who need to maintain a home bias can manage the domestic portfolio separately. Such a more top-down mandate structure not only accrues benefits from the potential merits of an integrated global investment process, but it also accommodates segment-specific considerations on manager selection, legacy or mandatory home bias, and different risk and return drivers in various equity market segments.

A Meta-Analysis of the Equity Premium

The equity premium is a key parameter in asset allocation policies. There is a vigorous debate in the literature regarding the actual measurement of the equity premium, its size and the determinants of its variation. This study aims to take stock of this literature by means of a meta-analysis. We identify how the size of the equity premium depends on the way it is measured, along with its evolution over time and its variation across regions in the world. We find that the equity premium is significantly lower if measured by ex ante methods rather than ex post, in more recent periods, and for more developed countries. In addition, looking at the underlying fundamentals, we find that larger volatility in GDP growth tends to raise the equity premium while a higher nominal interest rate has a negative impact on the equity premium.

The Importance of Asset Allocation

What is the impact on performance of the long term asset allocation policy relative to the impact of active management? Most of the variation in time-series returns for a typical fund comes from general market movement. The remaining variation comes about equally from asset allocation policy and active management.

Adaptive Asset Allocation Policies

This article proposes an asset allocation policy that adapts to market movements by taking into account changes in the outstanding market values of major asset classes. Such a policy considers important information, reduces or avoids contrarian behavior, and can be followed by a majority of investors.

Adaptive Asset Allocation Policies

This article proposes an asset allocation policy that adapts to market movements by taking into account changes in the outstanding market values of major asset classes. Such a policy considers important information, reduces or avoids contrarian behavior, and can be followed by a majority of investors.

The Importance of Asset Allocation

This article discusses the impact on performance of the long-term asset allocation policy relative to the impact of active management. Most of the variation in time-series returns for a typical fund comes from general market movement. The remaining variation comes about equally from asset allocation policy and active management.

The Equal Importance of Asset Allocation and Active Management

What is the relative importance of asset allocation policy versus active portfolio management in explaining variability in performance? Considerable confusion surrounds both time-series and cross-sectional regressions and the importance of asset allocation. Cross-sectional regressions naturally remove market movements; therefore, the cross-sectional results in the literature are equivalent to analyses of excess market returns even though the regressions were performed on total returns. In contrast, time-series analyses of total returns do not naturally remove market movements. Time-series analyses of excess market returns and cross-sectional analyses of either total or excess market returns, however, are consistent with each other. With market movements removed, asset allocation and active management are equally important in determining portfolio return differences within a peer group. Finally, an examination of period-by-period cross-sectional results reveals why researchers using the same regression technique can get widely different results.