Investigating seasonality, policy intervention and forecasting in the Indian gold futures market: a comparison based on modeling non-constant variance using two different methods

This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form. The trade of gold futures relates to seasons, festivity, and government policy. So, the paper will discuss seasonality and intervention in the analysis. Due to non-constant variance, we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices. The results from the analysis show that while the standard variance transformation method may provide better point forecast values, the ARIMA/GARCH modelling method provides much shorter forecast intervals. The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.

On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables

We investigate the sub-Gaussian property for almost surely bounded random variables. If sub-Gaussianity per se is de facto ensured by the bounded support of said random variables, then exciting research avenues remain open. Among these questions is how to characterize the optimal sub-Gaussian proxy variance? Another question is how to characterize strict sub-Gaussianity, defined by a proxy variance equal to the (standard) variance? We address the questions in proposing conditions based on the study of functions variations. A particular focus is given to the relationship between strict sub-Gaussianity and symmetry of the distribution. In particular, we demonstrate that symmetry is neither sufficient nor necessary for strict sub-Gaussianity. In contrast, simple necessary conditions on the one hand, and simple sufficient conditions on the other hand, for strict sub-Gaussianity are provided. These results are illustrated via various applications to a number of bounded random variables, including Bernoulli, beta, binomial, Kumaraswamy, triangular, and uniform distributions.

An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance.

Application of Superiority Evaluation Method in Engineering Bidding Evaluation

According to Construction engineering bid evaluation is affected by many factors,the method based on extension superiority evaluation method and standard variance between-class method was proposed.First, in view of the characteristics of this project, evaluation system was constituted by eight indexes; Then the matter-element model based on superiority evaluation method was established so as to conduct bid evaluation of construction project. Finally, the superiority sort was gained. The result demonstrated this method has advantage of objectivity, practicability and high-efficiency.

Analysis on Grinding Force of SiCp/Al Composites Based on MATLAB

SiCp/Al composites were grinded with Diamond Wheel on high-precision machine. Acquiring grinding force signals of the experiment with High Speed Advantech PCI－1712 board. Acquired grinding force signals are analyzed in MATLAB which has mighty function disposing signals. Through analyzing the grinding force in time field, frequency field and the signals wavelet analysis for different grinding parameters, found that the feed velocity has much more influence on the grinding force, the grinding force ratio and the standard variance of the grinding force than the depth of grinding when the velocity of the Diamond Wheel is fixed. The frequency structure and energy distribution of grinding force are showed in PSD analysis. The signals are de-noised in wavelet tools. The result of the experiment indicates the grinding force increases, the grinding force ratio decreases and the standard variance of the grinding force varies complex for different material with the increasing of the velocity of the workpiece and the grinding depth.