Sentence examples for return distribution from inspiring English sources

The GARCH specification is consistent with the return distribution of most financial assets, which is leptokurtic and it allows long memory in the variance of the conditional return distribution.

Gouret and Hollard (2011) take this criticism seriously and try to separate the fraction of respondents that do provide valuable information about expected mutual fund return distribution.

To capture the short term dynamics of the return distribution, and the long run equilibrium relationship between the stock markets, the present study uses various time series techniques.

Inequality, social fragmentation, and lack of leadership in the community do have adverse consequences but these can be overcome by changes in project complexity, community participation, and return distribution.

While Gaussian variance is not necessarily a problem in itself, the non-normality of return distribution caused by price jumps affects not only the performance of many risk-hedging algorithms but directly influences the frequency of catastrophic market events.

This means that: (i) one needs to make rather strong assumptions on the market for eliciting a return distribution, and (ii) the parameters of this distribution need be somehow estimated, which is quite a critical aspect, since optimal portfolios will then depend on the way parameters are estimated.

Mean-variance theory is ideally suited to contexts where return distributions are defined by these moments and such a context is provided by multivariate normal return distributions.

There exists a strong long run equilibrium relationship between the return distributions of the stock markets within the region.

These return distributions reveal the possible range of variation of VaR given the first four sample moments.

Since the estimated coefficients for the Skewness of the return series are different from zero, the underlying return distributions are not symmetric.

A comparison with the estimated VaR of some important return distributions is instructive and justifies the application and further development of the CM VaR bounds.