Michaël Allouche, Mnacho Echenim, Emmanuel Gobet, Anne-Claire Maurice
ABSTRACT We study price aggregation methodologies applied to crypto‐currency prices with quotations fragmented on different platforms. An intrinsic difficulty is that the price returns and volumes are heavy‐tailed, with many outliers, making averaging and aggregation challenging. While conventional methods rely on volume‐weighted average prices (called VWAPs), or volume‐weighted median prices (called VWMs), we develop a new robust weighted median (RWM) estimator that is robust to price and volume outliers. Our study is based on new probabilistic concentration inequalities for weighted means and weighted quantiles under different tail assumptions (heavy tails, sub‐gamma tails, sub‐Gaussian tails). This justifies that fluctuations of VWAP and VWM are statistically important given the heavy‐tailed properties of volumes and/or prices. We show that our RWM estimator overcomes this problem and also satisfies all the desirable properties of a price aggregator. We illustrate the behavior of RWM on synthetic data (within a parametric model close to real data): Our estimator achieves a statistical accuracy twice as good as its competitors, and also allows to recover realized volatilities in a very accurate way. Tests on real data are also performed and confirm the good behavior of the estimator on various use cases.
K R Raghunandan, Dhanya, Avisha V Shetty, A. Bhat · 6 authors
Counterfeiting, increased fake intermediate products, lack of transparency, unauthorized alteration of data, and other related problems throughout the supply chain have made it necessary for the modern pharmaceutical industry to ensure integrity and security. Thus, the above-mentioned problem can be solved by a decentralized blockchain-based system, which ensures security and traceability. In the proposed solution, there are seven stakeholders including manufacturers and consumers. Authorized manufacturers are allowed to store the drugs in the blockchain network, and corresponding QR and bar codes are generated. This generates a reliable record of drug movements by constructing an architecture that is decentralized to avoid alteration of the data by anyone. Consumers have the opportunity to scan QR codes to ensure that their products are genuine and trace their origin. By addressing the challenges of the conventional pharmaceutical supply chain that provides high transparency, minimal counterfeits, and assured data security, this system addresses the aforementioned disadvantages.
Pharmaceutical Quality and Counterfeiting
Innovative Microfluidic and Catalytic Techniques Innovation
Ali Yeganeh, Xuelong Hu, Sandile Charles Shongwe, Frans F. Koning
In the area of multivariate process quality control, it is sometimes important to monitor the ratio of two normal random variables denoted by RZ over time. The concept of control charts has often been harnessed in this field, leading to the application of various types of statistical models, including Shewhart, Exponentially Weighted Moving Average (EWMA), and so forth. However, there is little attention to implementation of machine learning-based control charts. To bridge this gap, a novel machine learning based model incorporating the attention mechanism approach, as an implemented Artificial Intelligence (AI) model, is proposed to monitor the RZ in Phase II applications. The proposed RZ method not only provides quicker Out-of-Control (OC) shift detection than conventional RZ control charts but also does not require the quality controller to have any prior information about the upward or downward shift patterns, which is a major assumption in most of the previous RZ models. We provide extensive performance comparison results to discuss the statistical performance of our proposed method through Monte Carlo simulations. Moreover, a comprehensive real example about surveillance of the cryptocurrency market is provided to illustrate the practical application of our proposed method. Through simulation and back-testing results, it is shown how the proposed method can lead to an automated trading strategy.
In DeFi (Decentralized Finance) applications, and in dApps (Decentralized Application) generally, it is common to periodically pay interest to users as an incentive, or periodically collect a penalty from them as a deterrent. If we view the penalty as a negative reward, both the interest and penalty problems come down to the problem of distributing rewards. Reward distribution is quite accomplishable in financial management where general computers are used, but on a blockchain, where computational resources are inherently expensive and the amount of computation per transaction is absolutely limited with a predefined, uniform quota, not only do the system administrators have to pay heavy gas fees if they handle rewards of many users one by one, but the transaction may also be terminated on the way. The computational quota makes it impossible to guarantee processing an unknown number of users. We propose novel algorithms that solve Simple Interest, Simple Burn, Compound Interest, and Compound Burn tasks, which are typical components of DeFi applications. If we put numerical errors aside, these algorithms realize accurate distribution of rewards to an unknown number of users with no approximation, while adhering to the computational quota per transaction. For those who might already be using similar algorithms, we prove the algorithms rigorously so that they can be transparently presented to users. We also introduce reusable concepts and notations in decentralized reasoning, and demonstrate how they can be efficiently used. We demonstrate, through simulated tests spanning over 128 simulated years, that the numerical errors do not grow to a dangerous level.
The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.
This paper presents a complete solution consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS) able to manage the huge amount of industrial data coming from cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework guarantees data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimisation. With the i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zero-defect manufacturing. The i4Q Solutions efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology and provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.
In this thesis, our objective is to study the relationship between transaction price and volume in the BTC/USD Coinbase exchange. In the second chapter, we develop a consecutive CUSUM algorithm to detect instantaneous changes in the arrival rate of market orders. We begin by estimating a baseline rate using the assumption of a local time-homogeneous Poisson process. Our observations lead us to reject the plausibility of a time-homogeneous Poisson model on a more global scale by using a chi squared test. We thus proceed to use CUSUM-based alarms to detect consecutive upward and downward changes in the arrival rate of market orders. In the third chapter we identify active periods from the number of consecutive upward CUSUM alarms, leading to the classification of active versus inactive periods. Finally we use One-Way ANOVA to assess the level effect on price swings for periods classified as containing at least two or three consecutive CUSUM up alarms. We show that in these active periods, price swings are significantly larger than in inactive periods.
The SHARE Compliance Profiles consist of a set of quality control indicators based on the SHARE Survey Specifications. All participating countries are evaluated on these indicators uniformly, although the environments for conducting the survey differ among European countries. As an ex-ante harmonized endeavor like SHARE cannot afford to set country-specific standards on what qualifies as good performance the combination of ex-ante Survey Specifications and ex-post Compliance Profiles levels the playing field for all participating countries and allows for a fair comparison of national survey quality. This document reports how SHARE quality standards were adhered to in Wave 4. Section 2 lists the survey agencies involved in Wave 4. Section 3 describes the data input for this evaluation. Section 4 reports the results in form of the various indicators. It is important to note that Wave 4 of SHARE was a difficult wave since it was the first wave under the new decentralized funding scheme. As opposed to the three earlier waves, survey operations in each country were financed nationally and not centrally by the EU Commission. This has put the ex-ante harmonization approach under additional pressure, not the least due to the difficulties of some countries to provide the necessary funds in time. The compliance profiles in this report therefore do not only reflect differences in survey agency performance but also the pressures of time and money in the SHARE member countries.
Abstract This article focuses on multiple regression analysis to cost control of decentralized operations in the consumer finance industry. There are potential accounting applications of multiple regression analysis in control of decentralized operations. Moreover, multiple regression can be a useful empirical research tool in other areas of accounting and finance. It is essential, however, to know the hidden limitations and assumptions in the approach and to perform the necessary tests to see that these assumptions are met be- fore plunging head-first into a sea of regression formulae. In cost analysis, one feature of multiple regression is the ability to use dichotomous variables. The advantage herein arises when a given characteristic may or may not exist in decentralized units. Multiple regression may be applied without assuming the disturbance terms are normally distributed. Multiple regression may be used in testing structural relationships between operating costs and various factors which are thought to affect these costs. Analysis of variance procedures may be extended to statistical tests of single coefficients and to statistical tests of the contribution to explained variation of sub-groups of factors included in the model.