Динаміка параметрів α-стійкого процесу Леві для розподілів прибутковостей фінансових часових рядів

In this paper, we develop a method based on a deep learning method in financial market prediction, which includes BRICS economies as the test cases. Financial markets are rife with volatility that is affected by a "bed of complexity," coddled by local and distal factors. To leverage these vast datasets both deep learning models such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks as well as hybrid architectures are used in this study. The paper evaluates the predictive accuracy of the models, and by so doing, identifies their strengths in predicting temporal dependencies and intricate market patterns. In particular, deep learning techniques are applied to case studies of individual countries in the BRICS to highlight the application of deep learning to disparate country specific problems, such as liquidity crises and market shocks. These findings show that classical statistical methods are outperformed by deep learning systems in a precise and reliable financial forecasting. This research highlights the ability of AI driven systems to change financial decision making processes, improving investor confidence and improving economic stability in BRICS nations. This study also readers the value of deep learning in financial market analysis, especially in economies in the developing countries. Application of techniques and architectures e.g. Convolutional Neural Networks (CNNs) that excel at identifying spatial patterns, and Long Short-Term Memory (LSTM) networks renowned for their prowess on sequential and time series data, for real world market prediction are explained. In addition, the research discusses hybrid architectures which extend knowledge, fusing strengths of both architectures to improve prediction accuracy and how deep learning develops to solve particular financial challenges. Through reading these notes readers get exposed to data preprocessing techniques such as normalization and feature selection which are important for boosting deep learning performance. The paper also includes an introduction to the evaluation of models using MSE and R-squared values for validating them in terms of reliable outputs. This research combines deep learning theory and practical case study to offer a useful educational resource for students, researchers, and practitioners who want to apply AI in financial forecasting in complex and dynamic global markets.

The prediction of soil respiration (Rs) has traditionally been studied using classical statistical methods. These methods do not consider temporal/spatial coordinates and assume independence between samples. The aim was to determine the primary factors influencing Rs and to develop a state-space model able to predict soil respiration. This study was conducted during one growing season, from July to October 2010, in temperate, semi-arid grassland. Data were collected for Rs, air temperature, soil surface temperature, soil temperature at a depth of 5 cm, soil moisture, air pressure, and relative humidity. Additionally, a novel autoregressive state-space method was used to simulate and predict Rs based on primary factors, and the quality of prediction was compared with the quality of prediction using classical statistics. Soil surface temperature and soil moisture were identified as primary factors affecting Rs. The state-space model that included soil surface temperature was a simple but effective model, accounting for 95% of the variation in Rs. The classical statistical models, however, represented only 39–69% of the variation in Rs. Furthermore, the quality of prediction of the state-space models was consistently much better than the quality from the classical statistical methods. State-space analysis is an effective tool for studying the temporal relationships between soil respiration and influencing factors. Additionally, the state-space method is recommended for predicting soil respiration using soil surface temperature in semi-arid grassland in northern China.

<div> <h2>Abstract</h2> <p><strong>Background:</strong> Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. <strong>Objectives:</strong> Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum sensing with the assumption that the noise follows a non-Gaussian distribution with heavier tails. <strong>Methods/findings:</strong> A fuzzy logicbased method is proposed for primary user detection under non Gaussian Noise. The results are highlighted for the Laplacian noise. Through Monte Carlo simulations it is observed that Laplacian noise noticeably affects the performance of energy detector. Also, a fractional change in noise uncertainty degrades the performance of energy detector. The performance of the proposed scheme is presented through receiver operating characteristic (ROC) and plot of the detection probability versus signal-to-noise ratio (SNR) using simulations. It is shown that by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed fuzzy logic method for spectrum sensing provides reliable detection.</p> <p><strong>Keywords:</strong> NonGaussian noise; Fuzzy logic; Spectrum sensing; noise uncertainty</p> </div> <div></div> <div> </div>

Adaptive Models and Heavy Tails

We present an agent based model of a single asset financial market that is capable of replicating most of the non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. In our model agents employ strategies inspired on those used in real markets, and a realistic trade mechanism based on a double auction order book. We study the role of the distinct types of trader on the return statistics: specifically, correlation properties (or lack thereof), volatility clustering, heavy tails, and the degree to which the distribution can be described by a log-normal. Further, by introducing the practice of “profit taking”, our model is also capable of replicating the stylized fact related to an asymmetry in the distribution of losses and gains.

In the classical optimal execution problem, the basic assumption of underlying asset price is Arithmetic Brownian Motion (ABM) or Geometric Brownian Motion (GBM). However, many empirical researches show that the return distribution of assets may have heavy tails than those of normal distribution. The uncertain information impact on financial market may be considered as one of the main reasons for heavy tails of return distribution. To introduce this information impact, our paper proposes a Jump Diffusion model for optimal execution problem. The jumps in our model are described by the compound Poisson process where random jump amplitude depicts the information impact on price process. In particular, the model is simple enough to derive closed-form strategies under risk neutral and Mean-VaR criterion. Simulation analysis of the model is also presented.

ObjectiveOne of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process.MethodsThis was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches.ResultsThe proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex.ConclusionsThese results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.

In this article, the authors develop a new multi-asset option pricing model where underlying dynamics are assumed to follow the normal tempered stable (NTS) process and its correlation structure evolves over time. The model is constructed by extending the constant correlation term in the previous NTS framework to the stochastic correlation making use of the Ornstein-Uhlenbeck process. We then derive its closed-form solution under the risk-neutral measure and apply it to an empirical study of a quanto option. The in-sample tests and calibration practices justify our model specification reflecting the empirical stylized facts on asset returns such as heavy tails, skewness, kurtosis, and stochastic correlation. Building on the empirical results, we can also identify the presence of stochastic correlation in the risk-neutral world.

Survival analysis with high-dimensional data deals with the prediction of patient survival based on their gene expression data and clinical data. A crucial task for the accuracy of survival analysis in this context is to select the features highly correlated with the patient's survival time. Since the information about class labels is hidden, existing feature selection methods in machine learning are not applicable. In contrast to classical statistical methods which address this issue with the Cox score, we propose to tackle this problem by discretizing the survival time of patients into a suitable number of subgroups via silhouettes clustering validity. To cope with patients' censoring, we use “k-nearest neighbor” based on clinical parameters. Feature selection is then accomplished using Fast Correlation-Based Filtering approach from machine learning community. The effectiveness and efficiency of the proposed method are demonstrated through comparisons with classical statistical methods on real-world datasets and simulation datasets.

The recent developed intrinsic discriminate analysis (IDA) demonstrates superior recognition rate compared with classical methods such as PCA and LDA. In this paper, we not only re-prove the core theorem of IDA from a new perspective, but also define the Average Invariant Factor (AIF) that generalizes IDA. Two new algorithms for face recognition are built upon the AIF by using SVD and QR decomposition. Moreover, this new formulation facilitates the kernel extensions for the recognition algorithms, which relax the linear assumption for IDA. The presented kernel based AIF algorithms also significantly lower down the computational expenses of the original IDA method. A series of experiments on YALE and ORL sets demonstrate higher performance in terms of recognition rate and efficiency compared with classical statistical analysis methods (e.g., PCA, KPCA and 2DPCA) and the IDA algorithm.

This study addresses the technology for soil sampling of large agricultural fields which are inherently variable in both space and time. Three several hundred ha fields in southwest Iran initially sampled on an arbitrarily selected grid of 80 m to ascertain soil salinity levels were analyzed using both geostatistical and classical statistical methods. The results from two fields showed that the variance structure of the salinity observations were spatially dependent, and hence, geostatistical techniques allowed best linear unbiased estimates of salinity values interpolated between spatially observed sampling locations to yield contour lines of isosalinity. In the third field, salinity observations were found to be spatially independent, and hence, were analyzed by classical methods to yield the number of soil samples necessary to observe the fiducial limits within which it is expected that the true mean salinity exists for given levels of probability.

Reviewing various care-related data, such as patient satisfaction, can provide valuable information for a health care organization to improve its services. Regular and automatic data collection of internet data can save time and money. This data collection can be performed using web scraping. Web scraping is well-suited for collecting and linking (secondary) data from the internet. In this paper, a low-threshold option for web scraping is illustrated: Web scraping using the commercial software OutWit. This method is also suitable for researchers with no experience in web scraping. Following web scraping, classical statistical methods can be used for quantitative data or qualitative content analysis for qualitative data. Before collecting data using web scraping methods, the legal framework for the individual research project should be clarified. Additionally, ethical considerations should be addressed because the automated extraction of data is not always compatible with the respective data holder’s applicable terms of use.

The objective of this article is to study as extensively as possible the uncertainties affecting the annual energy produced by a windmill. In the literature, the general approach is to estimate the mean annual energy from a transformation of a Weibull distribution law. Then the issue is reduced to estimating the coefficients of this distribution. This is obtained by classical statistical methods. Therefore, the uncertainties are mostly limited to those resulting from the statistical procedures. But in fact, the real uncertainty of the random variable which represents the annual energy cannot been reduced to the uncertainty on its mean and to the uncertainties induced from the estimation procedure. We propose here a model, which takes advantage of the fact that the annual energy production is the sum of many random variables representing the 10 min energy production during the year. Under some assumptions, we make use of the central limit theorem and show that an intrinsic uncertainties of wind power, usually not considered, carries an important risk. We also explain an observation coming from practice that the forecasted annual production is always overestimated, which creates a risk of reducing the profitability of the operation.

Correlating and predicting psychiatric symptom ratings: Spearmans r versus Kendalls tau correlation

Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.