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Vistulian litho- and pedosedimentary cycles recorded in the Kolodiiv loess-palaeosol sequence (East Carpathian Foreland, Ukraine) determined by laser grain-size analysis

Frankowski Z., Łanczont M., Boguckyj A.

Geological Quarterly

2007

Vol. 51, No 2

147-160

In the Kolodiiv site, occur ring in the valley of the Sivka River (tributary of the Dniester River, Ukraine), Vistulian loess forms a subaerial cover over the Pleistocene terrace II. This terrace consists also of Eemian deposits (palaeosol or organic sediments) under lain by an alluvial succession of Wartanian age. The Kolodiiv 2 profile was studied in detail in order to reconstruct the conditions of loess accumulation, and consequently the climatic-environmental changes, that took place in this region. Eight lithogenetic units were distinguished in the profile: five trans formed by pedogenesis, and three loess beds. The main purpose of this study was to conduct a thorough examination of the units lithology, in particular the grain-size distribution, in order to investigate those loess-forming factors that are influenced by environmental changes (i.e. nature of source material, distance and dynamics of transport, type of deposition and redeposition, and hypergenetic processes). To achieve this, 174 samples were taken at 10 cm spacings along the profile, and the grain-size distributions of the deposits were determined using a laser method with 21 grain-size intervals examined in each sample and statistically analyses. Statistical analysis included: calculation of the main grain-size parameters (according to Folk and Ward's method), grain-size index (Ding etal., 1994) and also two statistical tests (Kolmogorov-Smirnov and Spearman rank correlation) applied in order to find differences or similarities between the grain-size distributions of the lithogenetic units distinguished. Stratigraphic variations in grain-size distribution reflect the division of the deposits into stratigraphic units previously arrived at. Mean values of grain-size index (Igs1) indicate that loess units 2, 4 and 6, differ from the palaeosol units 3, 5 and 7. The grain-size distribution of loess deposits in the Kolodiiv 2 profile varies, with marked dominance of the silt fraction, which indicates that these deposits were trans ported by winds of similar velocities carrying material a short distance from source. As the Aeolian conditions that formed loess deposits in the Kolodiiv 2 profile were generally stable, differences in the grain-size distribution of unit 2 representing the Upper Pleniglacial, suggest three cycles of loess deposition during that interval (with the middle cycle characterized by the most distinct, short-term oscillations in environmental dynamics). The variability in grain-size distribution in units 3-5, which to get her represent the Interplenivistulian (Middle Pleniglacial), reflects the climatic heterogeneity of this period. The palaeosol layers are diamictic. Higher values of grain-size indices show that all Upper Pleistocene palaeosol units of high (interglacial) and low (interstadial) rank are characterized by higher content of fine relative to coarse fraction the lowest mean values of grain-size index occur the soil unit 1, of Holocene age, suggests that this unit is probably a product of very recent, Neoholocene pedogenesis and does not represent the en tire Holocene epoch. The statistical tests results show, great similarity between loess units 2 and 4 (from the middle and upper part of the Pleniglacial), and also between palaeosol units 7 and 8 forming the Horohiv slpalaeosol unit (an Eemian palaeosol and interstadial palaeosols from the Early Vistulian). Further more, the individual nature of loess unit 6, deposited during the Lower Pleniglacial, seems to be associated with the climatic characteristics of this interval.

Application of modern portfolio theory to the Russian state bond market

Pervozvanskij A., Barinov V., Kozlova O.

Control and Cybernetics

1999

Vol. 28, no 4

799-810

The behaviour of the Russian state bond market is analyzed. Attention is mainly paid to short-term fluctuations and efficiency of short-term investments. Analysis of return time series has shown that there exists a significant autocorrelation, and that distribution of random fluctuations is non-Gaussian. It predetermines a choice of forecasting schemes. The most efficient ones appear to be non-linear. The efficiency was checked not only by the traditional statistical indices by direct numerical experiments where various types of predictors were used as basic elements of decision rules. The decision algorithms have included the solution to the modified optimal portfolio problem where the forecasts were used as expected returns and the covariance matrix was estimated via forecasting errors.