THE PORTFOLIO FLOWS: The Interaction Between Flows and Returns

Again, it is useful to benchmark these results against similar variance ratios for asset market returns. These are shown in Table 4 for the regions, with countries weighted equally.19 As previous work has shown, equity market returns generally reveal evidence of high frequency persistence. This is particularly the case in the emerging markets. This is not the case in developed countries today, which show no persistence.20 As for the currencies, there is not much evidence of high frequency persistence except in East Asia. And in developed countries, there is no statistical evidence of persistence at any horizon. However, the emerging markets show strong persistence in the excess currency returns at longer horizons. This may be a result of the role of governments in setting local exchange rate and interest rate policies.

The Interaction Between Flows and Returns

In this section we explore the bivariate behavior of flows and returns. Are flows and returns correlated? Do flows forecast returns and vice versa? We begin our exploration by looking at the unconditional covariance between the two data series at various horizons. We then examine their conditional covariances within a vector autoregression framework.

The covariance of flows and returns

As described in the introduction, it is known from prior studies that the quarterly covariance of cross-border inflows and equity returns is positive. For example:
where is the ^-period return on equity, and is cumulative sum of daily flows from
t-k+1 to f.21 Note however that the covariance between ^-period returns and flows can be broken down into a series of daily cross-covariances. We can think of the quarterly covariance as being comprised of three components: (a) the covariance between current flows and past returns; (b) the contemporaneous covariance between daily flows and returns, and (c) the covariance between current flows and future returns (or past flows and current returns.) Specifically:
It is of interest to know which of these components drives quarterly covariance. If (a) turns out to be the largest fraction of quarterly covariance, we can hypothesize that there is trend-chasing behavior driving managers’ investment decisions. If (c) is large we might believe that future returns can be predicted on the basis of current flows.
The high frequency of our data allows us fo calculate these components separately. However, we would still like to make statistical inferences. In order to achieve this goal simply, we divide the quarterly covariance by к times the daily variance of the flows and In doing so estimate the following “covariance ratio” statistic (or CVR): payday loans with no credit check

This is reminiscent of the variance ratio statistic used earlier. However, notice that the denominator is not к times the covariance between daily flows and returns, but rather к times the variance of flows. The
where p {rf v >fi() is the coefficient from a regression of daily returns at time s on daily flows at time /. The formulation of CVR(k) in (8) allows us to easily decompose quarterly covariance and make statistical inference.