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Summary

  • VWO is both highly sensitive to and correlated with the U.S. market.
  • VWO is more volatile than SPY.
  • VWO is inexpensive relative to other Emerging Market Funds.
  • VWO reduces risks associated with holding too few stocks, countries, industries, and currencies.

Summary: This article analyzes the historical disadvantages and advantages of VWO with regard to lowering the overall risk of an investment portfolio. VWO is inexpensive and reduces risks associated with holding too few stocks, countries, industries, and currencies; however, it has been volatile and both highly sensitive to and correlated with the U.S. market. Therefore, it has been a mediocre option for gaining exposure to Emerging Markets.

Background: In theory, Emerging Markets might lower the risk of an investment portfolio. Although companies in these markets tend to be less stable and more volatile than those in the U.S., they should also derive a greater proportion of their income from local sources, which are at least partially independent of the U.S. economy. As a rule of thumb, independence between bets tends to lower the volatility of an investment portfolio.

In practice, Emerging Markets may increase risk. Companies in Emerging Markets export many of their products to the U.S. and rely on U.S. financing. Consequently, their profits are linked to the American economy, and their stock prices frequently move in tandem with the U.S. market.

Introduction: This article evaluates the historical disadvantages and advantages of the Vanguard FTSE Emerging Markets ETF (VWO) with regard to risk. Subsequent analyses will analyze its past profitability and forecast its future performance. To assess the advantages of VWO, I will analyze its past volatility and extreme events; assess the historical relationship between VWO and SPY; examine the exposure of VWO to individual companies, sectors, countries, and currencies; and investigate the proportion of VWO invested in Emerging Markets. I will weigh these against its expense.

Based on this analysis, I will show that VWO has been a mediocre option for investing in Emerging Markets since it has failed to reduce risks associated with investing in the U.S. market. It failed because it was both highly sensitive to and correlated with the U.S., and it exhibited very high volatility. These disadvantages were partially offset by its low cost; limited exposure to individual countries, sectors, currencies, and companies; and high exposure to Emerging Market countries.

Data Description: The analysis relies on daily data from Yahoo Finance beginning on January 1, 2006 and ending on December 31, 2013. The U.S. market is represented by the SPDR S&P 500 ETF (SPY) and Emerging Markets by the Vanguard FTSE Emerging Markets ETF (VWO). I have sourced much of the information about VWO from the Vanguard website.

Analysis Methods: To analyze the data, I use the Probicast software from PopperTech. As shown in Figure 1, I have modeled the U.S. economy as driving the performance of Emerging Market. In this case, SPY and VWO act as proxies for the U.S. and Emerging Markets.

Figure 1 indicates the U.S. Market (NYSEARCA:SPY) influences the performance of Emerging Markets (NYSEARCA:VWO).

Source: Yahoo Finance and PopperTech

Advantages of VWO: At first glance, the advantages of VWO appear to outweigh the disadvantages. It has low exposure to several potential sources of risk, invests exclusively in Emerging Markets, and is inexpensive.

A cursory review of VWO suggests that it diversifies the risk of loss from any individual company, sector, and country/currency:

Individual Company Risk

  • Largest holding < 2% of portfolio
  • Top ten holdings < 13.5% of assets

Sector Risk

  • Largest sector allocation: Financial (25%)
  • All other sectors < 15%

Country/Currency Risk

  • Largest country/currency allocation: China (20%)

Please note: Subsequent analyses will investigate the influence of individual country volatility, correlation, and weight to determine the relative influence of each country on VWO risk.

In addition, VWO invests almost exclusively in Emerging markets. Although Taiwan (14%), South Africa (10%), Mexico (6%), and Russia (5%) are arguably not "Emerging," they are usually not included in developed indices. Therefore, these countries have the potential for reducing risk beyond a standard international allocation.

Lastly, VWO is significantly cheaper than many alternatives. According to their website, Vanguard charges 15 basis points for this fund (industry average is 1.57%).

Disadvantages of VWO: Although these advantages appear substantial, VWO must also reduce the risks associated with investing in the U.S. market for it to diversify most American investment portfolios. Unfortunately, it fails to meet this demand because it is high risk and both highly sensitive to and correlated with the U.S. market.

I will examine the historical riskiness of VWO, review its past relationship with SPY, and discuss the reasons VWO has increased the risk of an investment portfolio with significant exposure to the U.S.

To gauge the risk of VWO, Figure 2 examines the distribution of daily gains and losses. Large losses and gains were more than twice as frequent for VWO than SPY. Medium losses and gains were also more frequent. Later in this section, descriptive statistics will augment these initial observations.

Figure 2 depicts the distribution of daily gains and losses for both SPY and VWO. I have defined a large loss to be less than -3%; medium loss to be between -3% and -1%, small loss to be between -1% and 0%, small gain to be between 0% and 1%, medium gain to be between 1% and 3%, and large gain to be greater than 3%. For example, a large loss (<-3%) occurred 2.2% and 5.6% of the time for SPY and VWO, respectively.

Source: Yahoo Finance and PopperTech

To add another perspective on the riskiness of VWO, I also assess its volatility over time. As shown in Figure 3, the volatility of VWO varies considerably over the period; however, it is consistently higher than SPY. This indicates that VWO is riskier than SPY. Therefore, unless VWO has a low correlation with SPY, it will fail to reduce risks associated with investing in the U.S.

Figure 3 shows the rolling standard deviation of SPY and VWO calculated over 90 days. Sample standard deviation estimates volatility and consequently indicates investment risk. Although the standard deviation of VWO varied significantly over time, it was consistently higher than SPY. This indicates VWO was consistently riskier than SPY.

Source: Yahoo Finance and PopperTech

Figure 4 also indicates VWO is riskier than SPY:

  • Larger magnitude maximum gains and losses
  • Higher overall volatility (standard deviation)

Both datasets exhibit fat-tails (kurtosis >> 3). This suggests standard deviation alone is insufficient for describing risk. In addition, some of regression statistics in the next section are technically invalid. Nonetheless, the regression results are informative and provide further evidence regarding the riskiness of VWO. Although a thorough analysis of the tails is both beyond the scope of this article and irrelevant, the variance of the standard deviation over time is likely a factor

Figure 4 shows descriptive statistics for the U.S. and Emerging Markets. Minimum and Maximum measure the greatest daily loss and gain. Median is the value with 50% of the data above and below it. Mean is the arithmetic average of the daily gains and losses. Standard deviation measures the dispersion, or volatility, of the gains and losses. If the data were ordered by value and placed on a seesaw with the mean as the fulcrum, skewness measures the direction and magnitude of the tilt. Kurtosis indicates the frequency and magnitude of extremely negative and positive losses and gains relative to the number of small and medium values.

Source: Yahoo Finance and PopperTech

In addition to the standalone riskiness of VWO, its relationship with SPY determines whether it reduces risks associated with investing in the U.S. To assess this relationship, I analyzed the correlation between SPY and VWO with conditional probability tables, time series analysis, and regression.

In Figure 5, conditional frequency tables represent historical correlations graphically. Assuming SPY and VWO had equal volatilities and were perfectly correlated, the conditional probabilities would be close to 100% on the downward sloping diagonal. Since this table is relatively close to this pattern, you can infer a historically high level of correlation between VWO and SPY. In particular, notice the probability of 97.6% in the upper left corner of the table. This indicates VWO failed to reduce risk when a U.S. investor was most vulnerable.

Figure 5 depicts the conditional frequency table for VWO given the performance of SPY. The column and row headers specify the performance of SPY and VWO given SPY, respectively. For example, given a medium loss for SPY, VWO exhibits a large loss on 25.3% of days.

(click to enlarge)Sources: PopperTech and Yahoo Finance

Source: Yahoo Finance and PopperTech

In Figure 6, time series analysis of correlations shows how the relationship between VWO and SPY has changed over time. The correlation between VWO and SPY stays between .8 and .95 for most of the period; although, it may be trending downward towards the end. This high correlation indicates VWO has consistently failed to reduce risks associated with overexposure to the U.S. market.

Figure 6 displays the 90 day rolling correlation between VWO and SPY.

Source: Yahoo Finance and PopperTech

In Figure 7, regression analysis illustrates VWO not only moves in tandem with SPY but also is highly sensitive to it. For each 1% gain in SPY, VWO increases 1.4%. Therefore, it actually increases the risk of an American investment portfolio rather than reducing it.

Figure 7 illustrates the results of an ordinary least squares linear regression with VWO as the dependent variable and SPY as the independent variable. Please note: the descriptive statistics and rolling standard deviation indicate the use of this regression for inference is limited. However, given the magnitude of the T Stat for SPY (test for statistical significance) and model R-Squared (explanatory power)/Correlation, it is pretty clear that a relationship between VWO and SPY exists. The small magnitude of the intercept and its corresponding T STAT provide partial evidence of the model including all relevant factors.

Source: Yahoo Finance and PopperTech

Conclusion: Based on the above historical analysis, you should doubt the ability of VWO to exclusively provide exposure to Emerging Markets. However, I believe it may be a reasonable addition if it only represents a component of your Emerging Market Allocation. Future analyses will expound further on this topic.

Source: Vanguard Emerging Markets Increased Portfolio Risk Historically, But Is Inexpensive

Additional disclosure: I am long call options on VWO with expiration in 2016