James Chong

Conditional Correlation and Volatility in CommodityFutures and Traditional Asset Markets

The impressive rise in commodity prices since 2002 and their subsequent fall since July 2008 have revived the debate on the role of commodities in the strategic and tactical asset allocation process. Commonly accepted benefits include the equity-like return of commodity indexes, the role of commodity futures as risk diversifiers, and their high potential for alpha generation through long-short dynamic trading. This article examines conditional correlations between various commodity futures with stock and fixed-income indices. Conditional correlations with equity returns fell over time, which indicates that commodity futures have become better tools for strategic asset allocation. The correlations between the S&P 500 Index and several commodities also fell in periods of above-average volatility in equity markets. This is desirable for long institutional investors as they need the benefits of diversification most in periods of high volatility in equity markets. Similarly, the results suggest that adding commodity futures to T-bill portfolios reduces risk further in volatile interest rate environments.

Integrating A- and B-Share Markets in China: The Effects of Regulatory Policy Changes on Market Efficiency

We investigate the effectiveness of two recent regulatory policy changes on market efficiency in the Chinese A- and B-share markets. Overall, the opening of the B-share market to domestic Chinese investors and the limited opening of the A-share market to foreign investors increase market efficiency. The opening of the B-share market significantly reduces the price differential between A- and B-shares. Furthermore, there is no longer feedback in returns between the two markets in recent years. Our results provide evidence that there is no detrimental effect to market efficiency by integrating Chinese investors to international markets and foreign investors to the Chinese stock markets.

Low- (Economic) Volatility Optimization

This article evaluates several low-volatility portfolio strategies to identify the return penalty, if any, associated with increased downside safety. The authors compare the S&P 500 Low Volatility Index with standard benchmarks and with portfolios specifically constructed to have low-volatility characteristics. They find that portfolios constructed using low-frequency economic measures for stock screening and portfolio optimization outpaced the S&P Low Volatility Index in absolute and relative terms. The authors conclude with practical suggestions for wealth managers about incorporating low-volatility methods into their practices.

Low- (Economic) Volatility Investing

Low-volatility investing has recently witnessed a surge in media coverage and experienced renewed interest in academic research. We assess various low- (economic) volatility portfolios against the S&P 500 Index, S&P 500 Low Volatility Index, and Fama–French-inspired U.S. Core Equity 1 Portfolio. Our portfolios outperformed the benchmarks, for the whole period as well as subperiods, and even more so when economic variables and criteria were incorporated with the down-market beta. This study shed further light on the efficacy of economic factors—that by constructing a low-volatility portfolio with economic factors would enhance a portfolio’s risk–reward ratio over a portfolio constructed purely with return-based measures.

Tactical Asset Allocation with Macroeconomic Factors

Tactical asset allocation is explored using an economic-based factor pricing model. Using a filtering method based on asset responses to the economy and current economic data, alternative optimization methods are considered including equally-weighted, low-volatility, and mean-variance (maximum Sharpe ratio) allocations. Using exchange-traded funds as proxies for asset classes, portfolios were constructed and rebalanced every half year from 2006 through 2013. We find that the economic response filtering with the maximum Sharpe ratio optimization provided the best overall performance in terms of returns while the low- (economic) volatility portfolio had the least volatility.

Portfolio Size Revisited

Using a sophisticated sampling technique, the authors compare randomly constructed stock portfolios with portfolios using the underlying population and evaluate them with 18 different measures. The randomization included portfolio size and portfolio start date to eliminate timing bias from the analysis. By comparing the 18 statistics across the portfolios, average portfolio sizes to reproduce the population characteristics were computed. The optimal portfolio size depended greatly on the criterion being used to judge the adequacy of diversification.

Eta® Analysis of Portfolios:The Economy Matters

The authors introduce a macroeconomic factor model, the Eta model, and its various applications. The underlying message regarding the Eta model, be it for replication, wealth maximization, or wealth preservation, is that “the economy matters.” The core feature of the Eta model is its replication methodology, from which portfolios could be customized to fit the risk–reward preferences of investors with respect to the economy. They then evaluate the portfolios against the Dimensional Fund Advisors Core Equity 1 Portfolio, which adopts the methodology promoted by the Fama–French three-factor model.

Dynamic correlations between REIT sub-sectors and the implications for diversification

The issue of whether Real Estate Investment Trusts (REITs) should pursue a focused or diversified investment strategy remains an ongoing debate within both the academic and industry communities. This article considers the relationship between REITs focused on different property sectors in a Generalized Autoregressive Conditional Heteroscedasticity-Dynamic Control Correlation (GARCH-DCC) framework. The daily conditional correlations reveal that since 1990 there has been a marked upward trend in the coefficients between US REIT sub-sectors. The findings imply that REITs are behaving in a far more homogeneous manner than in the past. Furthermore, the argument that REITs should be focused in order that investors can make the diversification decision is reduced.

Beta Measures Market Risk Except When It Doesn’t:Regime-Switching Alpha and Errors in Beta

A graphical comparison of CAPM beta and dual-beta estimates illustrates how different alpha values in up-market and down-market can muddle CAPM beta estimates. CAPM beta statistics from 23,060 investable assets are computed and compared against up- and down-market beta statistics estimated for the same group of assets. The empirical results demonstrate that it is common for the estimated beta to be more extreme in value than either the up-market or downmarket beta for a given asset. That is, estimated beta might be overstating risk, understating risk, or fairly representing risk and without other information, wealth managers and investors won’t know which statement is correct. The use of up-market and down-market alphas and betas can help wealth managers make better decisions about asset risk.

Sector Rotation with Macroeconomic Factors

Implementing sector rotation strategies with a set of low-frequency economic measures, the authors construct long-only sector exchange traded fund (ETF) portfolios that respond differently to the economy via alternative optimization methods, such as mean–variance and low-volatility allocations. These economic-based portfolios, when assessed against the S&P 500 Index and the equal-weighted ETF portfolio, performed relatively well in absolute and relative terms, for the whole period as well as subperiods. This study sheds further light on the effectiveness of economic factors when applied to investment strategies.