In the empirical finance literature most frequently monthly returns are applied for measuring fund performance or testing market efficiency. We propose a new return calculation method, the daily recalculated monthly returns which has not been used in academic studies for asset pricing purposes. We argue that our method outperforms daily and monthly return calculations in the case of Hungarian mutual funds when only short time series are available. Daily recalculated monthly returns induce the best fitting property of the market model while the time series remain sufficiently long to derive asymptotic tests even when we work on a one-year-long time series. Using our method the estimated parameters and the
s are very close to the results obtained when using monthly returns which are considered a good working approximation.
Authors:D. Drahos, Á. Gyulai, T. Ormos, and M. Dobróka
A new L2 norm joint inversion technique is presented and combined with the series expansion inversion method applied for different simulated erroneous Vertical Electric Sounding (VES) data sets over a complicated two dimensional structure. The applied joint inversion technique takes into consideration the complete form of the likelihood function. As a result there is no need to apply input weights to the individual objective functions. The model consists of three layers with homogeneous resistivities. The first layer boundary is a horizontal plane, the other is a two dimensional laterally varying surface. For the VES inversion the exact data sets were calculated by finite difference method, one in strike direction and the other in dip direction. These data sets were contaminated with normally distributed random errors. During inversion the second layer boundary function was determined. For comparison individual and joint inversion examples were calculated for the two data sets. The best model parameter estimate result was produced by the method of automated weighting.