Browsing by Subject "volume"

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  • Li, Hongzhu (Svenska handelshögskolan, 2004)
    Economics and Society
    A better understanding of stock price changes is important in guiding many economic activities. Since prices often do not change without good reasons, searching for related explanatory variables has involved many enthusiasts. This book seeks answers from prices per se by relating price changes to their conditional moments. This is based on the belief that prices are the products of a complex psychological and economic process and their conditional moments derive ultimately from these psychological and economic shocks. Utilizing information about conditional moments hence makes it an attractive alternative to using other selective financial variables in explaining price changes. The first paper examines the relation between the conditional mean and the conditional variance using information about moments in three types of conditional distributions; it finds that the significance of the estimated mean and variance ratio can be affected by the assumed distributions and the time variations in skewness. The second paper decomposes the conditional industry volatility into a concurrent market component and an industry specific component; it finds that market volatility is on average responsible for a rather small share of total industry volatility — 6 to 9 percent in UK and 2 to 3 percent in Germany. The third paper looks at the heteroskedasticity in stock returns through an ARCH process supplemented with a set of conditioning information variables; it finds that the heteroskedasticity in stock returns allows for several forms of heteroskedasticity that include deterministic changes in variances due to seasonal factors, random adjustments in variances due to market and macro factors, and ARCH processes with past information. The fourth paper examines the role of higher moments — especially skewness and kurtosis — in determining the expected returns; it finds that total skewness and total kurtosis are more relevant non-beta risk measures and that they are costly to be diversified due either to the possible eliminations of their desirable parts or to the unsustainability of diversification strategies based on them.
  • Kangas, Annika; Korhonen, Kari T. (The Finnish Society of Forest Science and The Finnish Forest Research Institute, 1995)
    Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.
  • Luoma, Ville (Helsingfors universitet, 2013)
    There develops heartwood in the stems of the Scots pines (Pinus sylvestris L.) that differs by its natural characteristics from the other sections of the wood material in the pine stem. Pine heartwood is natural-ly decay resistant and it can be used in conditions where the normal wood products can’t be used. The aim of this study was to develop a method, which can be used for predicting the diameter and volume of heartwood. There is a need for this kind of method, because it still is not possible to estimate the amount of heartwood in a standing tree without damaging the tree itself. The variables measured from single trees describing the diameter of the heartwood on eight relative heights were analysed by using linear regression. When the best explanatory variables were selected, a mixed linear model was created for each of the relative heights. The mixed linear models could also be used for predicting the diameter of pine heartwood at those relative heights. With the help of the pre-dicted diameters a taper curve could be created for the heartwood. The pine heartwood taper curve describes the tapering of the heartwood as function of the tree height. By integrating the taper curve, it was also possible to predict the total volume of the heartwood in a single tree. The models that used tree diameter at breast height and the length of the tree as explanatory variables were able to explain the variation of heartwood diameter on relative heights between 2,5 % and 70 % with coefficient of determination ranging from 0,84 to 0,95 and also recorded a relative RMSE from 15 % to 35 %. Models for relative heights of 85 % and 95 % were not as good as the others (R2-values 0,65 and 0,06 as well as RMSE-values of 74 % and 444 %). Despite not succeeding on all the relative heights, the most important thing is that the models worked best on that area of the stem where most of the heart-wood is located. The volume predictions for single trees based on the heartwood diameter models rec-orded relative RMSE of 35 % and bias of -5 %. Based on the results of the study it shows that exact prediction of pine heartwood diameter is much easier in the base of the stem than in the top part of it. A great deal of variation could be observed whether there was heartwood or not in the top parts of the stem. The volume of heartwood can already be estimated for single trees, but the amount of heartwood can be predicted also in larger scale, such as forest stands. But to get more accurate results in the future, there is a need for more detailed and com-prehensive research data, which would help to determine the still unknown parts of the behaviour of pine heartwood.
  • Kulp-Tåg, Sofie (Svenska handelshögskolan, 2008)
    Economics and Society
    Financial time series tend to behave in a manner that is not directly drawn from a normal distribution. Asymmetries and nonlinearities are usually seen and these characteristics need to be taken into account. To make forecasts and predictions of future return and risk is rather complicated. The existing models for predicting risk are of help to a certain degree, but the complexity in financial time series data makes it difficult. The introduction of nonlinearities and asymmetries for the purpose of better models and forecasts regarding both mean and variance is supported by the essays in this dissertation. Linear and nonlinear models are consequently introduced in this dissertation. The advantages of nonlinear models are that they can take into account asymmetries. Asymmetric patterns usually mean that large negative returns appear more often than positive returns of the same magnitude. This goes hand in hand with the fact that negative returns are associated with higher risk than in the case where positive returns of the same magnitude are observed. The reason why these models are of high importance lies in the ability to make the best possible estimations and predictions of future returns and for predicting risk.
  • Kilkki, Pekka (Suomen metsätieteellinen seura, 1983)