Abstract
The weak empirical evidence linking diversification and international equity flows calls into question the diversification paradigm at the international level and the analytical framework it implies. Using the concept of Marginal Conditional Stochastic Dominance (MCSD) to estimate the diversification opportunities, this paper reexamines the role that diversification opportunities play in the determination of international equity flows. It provides strong evidence that when diversification opportunities are measured in terms of MCSD, they are significant determinants of international equity flows. Capital flows into dominant markets and flees markets that are dominated. These results are robust with respect to a range of conventional control variables documented in the outstanding literature.
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Notes
For example, third derivatives and higher are equal to zero or do not exist, which rules out prudent and temperant behaviour. For a discussion of prudence and temperance see Eeckhoudt and Schlesinger (2006)
Although integration is increasing, diversification benefits may still be sizable. For example, Cheng and Glascock (2005) find that markets in the Greater China Economic Area and not integrated with the Japanese or the US market therefore, there is potential for diversification benefits.
Fernandez-Arias (1996) argues that the effect of equity returns’ second moments on equity flows may be of less significance when a country’s default risk is high.
Clark and Berko (1996) find the opposite relationship for Mexico, i.e. unexpected inflows drive equity prices.
This type of analysis, however, is plagued by the problem that the results are valid only if the chosen market portfolio is efficient.
An exception is Portes and Rey (2005) who find that there is a statistically significant diversification effect in equity flows but it is weak and unstable.
Although most studies find a statistically significant relationship between interest rates and flows, Bekaert et al. (2002) do not find such a relationship when they take into account regime changes brought about by capital flow liberalizations.
This is the basic problem with applying second degree stochastic dominance to create efficient portfolios.
It should be noted that if market A dominates market B which in turn dominates market C, then A also dominates market C; in other words for MCSD binary relations, the transitivity property applies.
For detailed information on the Z test, see Chow (2001).
Each period includes 300 daily observations and ends at the end of each quarter in our sample.
According to Chow (2001), 10 deciles is a good partitioning for our sample size. If we ‘slice’ the distribution in too few segments, there will not be enough segments of the two distributions for a proper comparison. If we ‘slice’ the distribution in too many segments, there will not be enough observations in each segment to make inferences.
It is important to note that the individual moments of the distributions are not considered directly and individually as in mean variance optimization. They are considered collectively through the risk averse utility functions where the trade-offs among the individual moments are translated into preferences.
We remind the reader that in order to increase their utility investors should buy into dominant markets and sell dominated markets. However, it could be the case that investors react only to dominant or dominated markets but not both. Using CSP and CSN allows us to examine this effect.
This period was chosen to balance the number of countries included in the study against the number of observations per country. A longer period would provide data for only a few developed countries. A shorter period would include a few more countries at the expense of available observations.
To classify a country as developed we use the classification of MSCI (as of February 2009). The developed markets in our sample are Australia, Austria, Canada, Finland, France, Germany, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, UK, and the USA.
If this were not true, return maximizing investors would always invest in assets with lower expected returns.
For example, the Chow statistics for 1996 quarter 1 are estimated from daily return data from the 6th of February 1995 to the 29th of March 1996, i.e. 300 daily returns. Daily returns are used because a lower frequency would include data of several years prior to each date, which may not be relevant. For example, weekly returns to estimate the Chow statistics for 1996 quarter 1 would have to include observations from 1991 to 1996.
Because we use net aggregate flows, the gravity models à la Portes and Rey (2005) that underline the role of geographical proximity as an informational proxy to explain local equity bias cannot be addressed.
The same interval is necessary to make the data compatible. CSP and CSN account for the entire distribution of equity returns while R (or ΔR) represents the first moment of the return distribution.
It should be noted that following Shalit and Yitzhaki (1994), the index proxies for daily changes in individual wealth. In this setting, any monotone transformation of individual wealth is appropriate.
Besides these three variables, Chuhan et al. (1998) also use the secondary market debt prices as a variable capturing domestic economic conditions. This data is not available to us, however, Chuhan et al. (1998) find that a principal component of secondary market debt prices and credit ratings is not significant in explaining equity flows.
As in Chuhan et al. (1998) we also use the first principal component of the real interest rates and US industrial production and the results remain the same.
Credit ratings are available semi-annually. For intermittent quarters, we use the ratings of the previous quarter. To make the series compatible with the linear regression model, we use the logistic transformation of the ranking (Cosset and Roy 1991).
Since we have 49 observations per country and there are 16 countries in our sample, the total number of observations is 784. The use of lagged variables as instruments means that we lose one observation per country so we have a total of 768 observations.
The F-statistic of the Hausman test has a value of 12.13 favouring the fixed effects model at any conventional level of significance. As a robustness test, we also run Eq. 8 using the random effects estimator. In results not reported here but available upon request, the dominance variables remain statistically significant.
SUR estimation uses the OLS residuals to obtain a consistent estimate of the system covariance matrix which however, is not efficient if any of the right hand side variables are endogenous. The use of the instrumental variables presented above sorts out this problem.
In all regressions reported in Table 3 we used both R and ΔR as a proxy for past equity performance and ΔR is always statistically insignificant and does not affect the results. For economy of space we report only the regressions which include R. Those with ΔR are available upon request.
For example, Bohn and Tesar (1996) and Froot et al. (2001) find evidence that investors chase returns, while Brennan and Cao (1997) present a model where due to the information disadvantage that foreign investors have compared to local investors, they tend to buy foreign securities when returns are high and sell when returns are low.
A Hausman endogeneity test verifies that.
While we have established that dominance drives equity flows, we do not draw conclusions with respect to the effect of dominance on equity prices in these markets. To examine if dominance does indeed affect prices in national markets we would have to isolate the effect of international investors from that of domestic investors and to examine if any effect on prices is due to dominance or systematic risk. This is a separate issue which is beyond the scope of this paper.
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Clark, E., Kassimatis, K. International equity flows, marginal conditional stochastic dominance and diversification. Rev Quant Finan Acc 40, 251–271 (2013). https://doi.org/10.1007/s11156-012-0277-0
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DOI: https://doi.org/10.1007/s11156-012-0277-0