Correction Vector Research Articles

Digital payment and banking stability in emerging economy with dual banking system

Previous studies provided limited conclusions on the relationship between digital payment and financial stability, particularly in an emerging economy with a dual banking system in which Islamic banking operates in parallel with its conventional counterpart, such as Indonesia. Therefore, this study aims to examine the impact of digitalisation on financial stability, particularly banking stability, in Indonesia. It uses Vector Error Correction Model (VECM) and Vector Autoregressive (VAR) models to investigate the relationship using monthly data during December 2013–July 2021 period. The digital payment transaction is proxied by payment penetration ratio (PPR); meanwhile financial stability is proxied by the value of Z-Score for the Indonesian banking industry. It also conducts a robustness check using Autoregressive Distributed Lag (ARDL) model. The study found a cointegrating relationship between PPR and Z-Score, suggesting that digital payment transactions have an equilibrium and long-run relationship with banking stability in Indonesia. Further examination shows a one-direction causality from digital payment to banking stability and a positive short-run relationship between the variables. Interestingly, despite being the largest Muslim country globally, the estimation result shows no significant causality between digital payment and Islamic banking stability in Indonesia. While this might be due to the small size of Islamic banking in the country, it is expected that the impact will be more significant as Islamic digital banking starts to emerge and gain strong support from society and government in the post-covid period. Overall, our findings support policies promoting a more supportive regulatory ecosystem for a resilient banking and financial system in an emerging economy with a dual banking system.

Nonlinear effects in Thomas precession due to the interplay of Lorentz contraction and Thomas–Wigner rotation

It is demonstrated that the 3-vector varvec{S} currently associated to the spin in an inertial frame does not contract, but rather dilates, in the direction of the velocity. The correct vector varvec{T} is individuated. The equation of motion for the two vectors is shown to contain two terms, a common linear rotation, identified with Thomas precession, and also a nonlinear rotation depending on the direction of the spin itself.

A sensor fusion approach to MARG module orientation estimation for a real-time hand tracking application

This paper introduces a new algorithm (Gravity & Magnetic North Vector correction — Double SLERP, or “GMV-D”) to estimate the orientation of a MEMS Magnetic/Angular-Rate/Gravity (MARG) sensor module using sensor fusion in the context of a real-time hand tracking application, for human–computer interaction purposes.Integrated MEMS MARG modules are affordable, small, light and consume minimal power. As such, there is interest in using them for monitoring the orientation of various body segments, to which they can be attached (e.g., the finger segments of a gloved hand). However, each of the 3 types of signals they provide has proven insufficient to yield robust orientation estimates, particularly in regions of space where the geomagnetic field is distorted. The significance (main contribution) of the approach we present is the computation of a final orientation estimate that uses all the signals generated by inexpensive (e.g., less than 20 USD, in large quantities) integrated MEMS MARG modules but weighs their contributions with simple, real-time-updatable parameters that prevent erroneous corrections when the pre-conditions for their valid use are not met. This will enable the use of inexpensive integrated MEMS MARG modules for hand tracking applications in human–computer interaction and other areas of work where tracking the orientation of body segments in real-time is important.In each iteration, GMV-D defines an initial orientation estimate from integration of gyroscopic signals (“dead reckoning”), and also calculates accelerometer-based and magnetometer-based corrections. These corrections are defined on the assumptions that the module is near static and affected by an undistorted geomagnetic field.Because these assumptions are seldom fully met simultaneously, the information fusion challenge is to apply each correction only to the extent that its corresponding pre-conditions are met, as inappropriate corrections will introduce significant error in the future orientation estimates. To achieve this, GMV-D develops an accelerometer correction trustworthiness parameter, 0 ≤α≤ 1, and a magnetometer correction trustworthiness parameter, 0 ≤μ≤ 1, both of which are updated on a sample-by-sample basis and are available at each iteration of the algorithm. The information fusion phase of the algorithm implements the corrections in a two-tiered application of Spherical Linear Interpolation (SLERP) of the quaternions representing the initial dead reckoning estimate and the available corrections, scaled according to their corresponding levels of trustworthiness.GMV-D was evaluated in comparison to 2 other orientation correction approaches (Kalman Filtering and GMV-S) and contrasted with 2 contemporary complementary filter approaches (Madgwick , Mahony). The results confirm that GMV-D displayed better orientation estimation performance when the algorithms operated in an area with known distortion of the geomagnetic field.

Overcoming Fundamental Restrictions: Novel Multistation Measurement While Drilling Survey Correction

Summary The traditional multistation analysis (MSA) method for drilling string magnetization compensation considers significantly limited information, only consisting of a single measurement while drilling (MWD) six-axis survey run. The limitations of this approach are an excessive scatter in the corrected azimuth values, poor accuracy in attitudes near the horizontal east/west directions, and low robustness of the algorithm, which make it nearly impossible to automate the survey processing. A novel MSA correction, involving further elaboration of the conventional MSA method, is proposed in this study. The proposed algorithm utilizes all available drilling information, including data from geomagnetic reference, error models, multiple six-axis runs, bottomhole assembly (BHA) designs, and slide sheets. This improvement moves the optimization task to find the survey correction vector from multiple independent spaces of total gravity field, total magnetic field, and dip angle (GBD) for each MWD run into a single GBD + north/east/vertical space of the entire well. Unlike the conventional MSA, which processes only a small portion of the available information, each MWD runs independently, the proposed algorithm finds the survey correction vector for all MWD runs simultaneously with additional constraints of trajectory steering and drillstring interference (DSI) modeling. Consequently, the proposed MSA method provides the best borehole positional accuracy for MWD surveys. Depending on the input conditions, the accuracy of the proposed MSA can be improved by an order of magnitude relative to the accuracy performance of the conventional MSA correction. In addition, this approach demonstrates better stability and robustness than the conventional MSA. The proposed method provides high startup performance, especially during the drilling of the tangent section. This is critically important for building automatic drilling systems and avoiding nonproductive time. Under normal conditions, the proposed MSA algorithm does not have a no-go zone and allows the correction of MWD surveys at the horizontal east/west tangent sections.

Vector data partition correction method supported by deep learning

ABSTRACT Road vector data are an important part of geographic information databases and play a leading role in social and economic development. Among them, the accuracy and current situation of vector data are the key values of their application. In recent years, with the development of remote sensing technology, remote sensing images contain increasingly more road information, which provides a large number of available features for the establishment of vector correction models. Therefore, the vector-to-image vector correction method based on remote sensing images has become an important means to ensure the accuracy and current situation of vector data. However, in actual scenes, there are great differences in the design structure and design index between urban roads and rural roads, which is reflected in the serious spatial heterogeneity between them in the remote sensing images. Therefore, when the existing methods use remote sensing images for vector correction, the universality is limited by the change of regional scenes, so it is difficult to realize the simultaneous correction of urban road and rural road vectors, with poor applicability and low compatibility. To solve this problem, this paper proposes a vector data partition correction method supported by deep learning. First, the U2-net model and line segment sequence method are used to generate the feature set. Second, according to the characteristics of the poor quality of urban road extraction results, the regular geometric form of vector data and the poor structural information of road images, the methods of vector line decomposition, subvector line correction, subvector line centralization and vector line synthesis are proposed to correct the vector lines of urban roads. Finally, according to the characteristics of high-quality rural road extraction, the irregular geometric form of vector data and the strong information of road image structures, this paper proposes road edge extraction, a road centreline reasoning model and a vector line connection method to complete the correction of rural vector road data. The quantitative analysis of the experimental results shows that compared with other methods, the urban road vector correction method in this paper is much better than the comparison methods and has achieved better correction results in rural areas. This method has the advantages of compatibility and better accuracy for roads in different areas.

Image preprocessing to improve the accuracy and robustness of mutual-information-based automatic image registration in proton therapy

PurposeWe propose a method that potentially improves the outcome of mutual-information-based automatic image registration by using the contrast enhancement filter (CEF). MethodsSeventy-six pairs of two-dimensional X-ray images and digitally reconstructed radiographs for 20 head and neck and nine lung cancer patients were analyzed retrospectively. Automatic image registration was performed using the mutual-information-based algorithm in VeriSuite®. Images were preprocessed using the CEF in VeriSuite®. The correction vector for translation and rotation error was calculated and manual image registration was compared with automatic image registration, with and without CEF. In addition, the normalized mutual information (NMI) distribution between two-dimensional images was compared, with and without CEF. ResultsIn the correction vector comparison between manual and automatic image registration, the average differences in translation error were < 1 mm in most cases in the head and neck region. The average differences in rotation error were 0.71 and 0.16 degrees without and with CEF, respectively, in the head and neck region; they were 2.67 and 1.64 degrees, respectively, in the chest region. When used with oblique projection, the average rotation error was 0.39 degrees with CEF. CEF improved the NMI by 17.9 % in head and neck images and 18.2 % in chest images. ConclusionsCEF preprocessing improved the NMI and registration accuracy of mutual-information-based automatic image registration on the medical images. The proposed method achieved accuracy equivalent to that achieved by experienced therapists and it will significantly contribute to the standardization of image registration quality.

Comparison of the Autoregressive Vector VAR with the Dynamic Error Correction Vector DVECM for Modeling COVID-19 Deaths

In this article, the Vector Auto-Regressive model and the Dynamic Error Correction Vector Model will be used in modeling data representing the number of deaths due to infection with the COVD-19 virus as a dependent variable and the variable platelet rate in the blood as an independent variable and finding the model equations that represent the relationship between the two variables using the two models and then estimating the equations that were obtained by estimating the two models using the least squares method, then choosing the best estimated equation from each model, and then, using the standard error of the regression and the coefficient of determination, selecting the best equations from the two models. The Dynamic Error Correction Vector Model is superior to the Vector Auto-Regressive model in assessing the link between corona virus mortality and the proportion of platelets in the blood, according to the analysis carried out using the E-Views application, and that there is a direct relationship through the equation for the Dynamic Error Correction Vector Model between the deaths of the corona virus and the proportion of platelets in the blood in the long term, which is logical as a result of the increase in the impact of the deaths of the Corona virus, the increase in the platelet rate, and thus the increase in the deaths of the corona virus.

Design and optical performance research of a fully customizable collimating lens based on total internal reflection.

An optical design method of a fully customizable collimating lens is proposed. The initial model of the complete lens is constructed by two parts. One part is calculated by the total internal reflection algorithm; the other part is constructed by trial-and-error method. The lens is further optimized by normal vector correction and high- and low-angle compensation. The optical performance of the fully customizable collimating lens is studied. It is proven to have good collimation performance with a divergence angle of 1.8° at 50% of the maximum luminous intensity; the uniformity is as high as 98.4%.

Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model

In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power time series obtained by implementing an ideal single diode model. This synthetic time series was validated against an actual photovoltaic module. The NARX model has been implemented in conjunction with the corrective vector multiplier (CVM) technique, which uses solar radiation under clear sky conditions to adjust the forecasting results. In addition, collinearity and the Granger causality tests were used to choose the input variables. The forecasting horizon was 24-h-ahead. The hybrid NARX-CVM model was compared to a nonlinear autoregressive neural network and persistence model using the typic forecasting error measures such as the mean bias error, mean squared error, root mean squared error and forecast skill. The results showed that the forecasting skills of the hybrid model are about 34% against the NAR model and about 42% against the Persistence model. The model was validated by blind forecasting. The results demonstrated evidence of the quality of the conformed forecasting model and the convenience of its implementation and building.

Analyzing the impact of exchange rate fluctuations and inflation on the GDP in Iraq using the modern methodology of Cointegration for the period (1988-2020)

The research aims to analyze the impact of exchange rate fluctuations (EXM and EXN) and inflation (INF) on the gross domestic product (GDP) in Iraq for the period 1988-2020. The research is important by analyzing the magnitude of the macroeconomic and especially GDP effects of these variables, as well as the economic effects of exchange rates on economic activity. The results of the standard analysis using the ARDL model showed a long-term equilibrium relationship, according to the Bound Test methodology, from explanatory (independent) variables to the internal (dependent) variable, while the value of the error correction vector factor was negative and moral at a level less than (1%). The relationship between EXM and GDP was inverse, with partial flexibility for EXM (-7.666), meaning that an increase in EXM (1%) will lead to a decrease in GDP (7.666%). This applies to the reality of the Iraqi economy, as the method used in recent times is to devalue the local currency. The effect of EXN was steady in the long run, with the partial flexibility of EXN (5.785), that is, an increase of EXN (1%) will lead to an increase in GDP (5.785%), and the model as a whole is statistically significant. The explanatory power of the model is high, as it was (R2=0.816), indicating that 81% of the changes in GDP in constant prices are due to the change in (EXM, EXN, and INF). The study concluded that in light of the outbreak of the coronavirus, the disruption of most economic projects, and the decline in the prices of the Iraqi government, as a result of the decrease in the balance of oil and oil revenues, as a result of the balance of the government part of the solution is to devalue the local currency by controlling the nominal exchange rate. At present, the Iraqi Monetary Authority recommends that the Managed Flexible Exchange System be continued as an effective means of avoiding external shocks for the Iraqi economy, as it allows for appropriate adjustments to be made continuously

Structural Feature-Preserving Point Cloud Denoising Method for Aero-Engine Profile

The ex-service and old type aero-engines are valuable for education. In many cases, these aero-engines only have physical objects, but lack geometric models. This brings difficulties to talent cultivation. Therefore, the education department needs to reconstruct geometric models of above aero-engines. The laser scanning devices provide raw data of aero-engine profile, but noise directly affects reconstruction accuracy. In order to ensure that noise is removed without blurring or distorting structural features, a structural feature-preserving point cloud denoising method is proposed. The noisy point cloud is divided into casing feature data, pipeline feature data and complex shape feature data. According to shape characteristics of each feature data, three denoising networks are designed to estimate position correction vectors of noisy points and project them back onto underlying surfaces. Qualitative and quantitative experiments show that our method significantly outperforms state-of-the-art methods, both in terms of preservation and restoration of structural features.

Multiplex PCR Assay for the Identification of Four Species of the Anopheles Leucosphyrus Sub-Group in Malaysia.

Simple SummaryPlasmodium parasites cause malaria. The bites of infected female Anopheles mosquitoes, known as “malaria vectors,” transmit the parasites to people. To prevent the spread of malaria, precise mosquito species identification is essential. This study aims to develop a quick and accurate method for identifying the Anopheles species (An. introlatus, An. latens, An. cracens, and An. balabacensis), which have been incriminated as vectors for simian malaria in Malaysia. Overall, six primers targeting the internal transcribed spacer 2 (ITS2) region of each species were designed for this assay. This study is helpful for the researchers or vector-related field workers to correctly identify the mosquitoes for control activities.The Leucosphyrus Group of mosquitoes are the major simian malaria vectors in Malaysia. Accurate species identification is required to help in curbing the spread of simian malaria. The aim of the study is to provide an accurate molecular method for identifying the four important Anopheles vector species found in Malaysia. Mosquito specimens were collected from various localities in Malaysia, where simian malaria cases were reported. DNA from 122 mosquito specimens was tested to develop a multiplex polymerase chain reaction (PCR) assay. The specificity of this assay was tested against other mosquito species. Molecular identification of the species was further confirmed by analysing the internal transcribed spacer 2 (ITS2) DNA region of the specimens. Anopheles balabacensis and An. latens showed two distinct clades in the phylogenetic tree. The multiplex PCR assay was developed based on the ITS2 region for the identification of Anopheles introlatus (298–299 bp), Anopheles latens (197–198 bp), Anopheles cracens (421–426 bp), and Anopheles balabacensis (224–228 bp). This method will be useful to accurately identify the major Anopheles Leucosphyrus Group species in Malaysia, which are difficult to identify morphologically, to determine the correct vector as well as its geographical distribution.

Velocimeter-Aided Attitude Estimation for Mars Autonomous Landing: Observability Analysis and Filter Algorithms

This articlefocuses on the attitude estimation problem for a Mars landing that employs an inertial measurement unit and a velocimeter. At the beginning of the parachute descent phase, high dynamic oscillatory motion may degrade the gyroscope performance or saturate the gyroscope, thereby producing large attitude estimation errors and a high landing risk. To address this problem, an attitude estimation algorithm with nadir vector correction is proposed by combining data from the IMU and the velocimeter. First, the body-frame nadir vector is determined from the gravitational acceleration, which is estimated in conjunction with the body velocity aided by velocimeter measurements in the framework of the Kalman filter. The observability analysis shows that the nadir vector may be estimated as long as two velocimeter beams are available. Next, to mitigate the large attitude knowledge errors caused by high dynamic motion, a purely deterministic approach is used to combine the estimated nadir vector with the inertial navigation system propagated attitude such that the attitude knowledge error along the nadir vector direction can be reduced. Simulation results demonstrated that the proposed algorithms can cope with gyroscope performance degradation or gyroscope saturation, enabling the embedded autonomy of lander systems.

An inverse problem of determining orders of systems of fractional pseudo-differential equations

As it is known various dynamical processes can be modeled through systems of time-fractional order pseudo-differential equations. In the modeling process one frequently faces with the problem of determination of adequate orders of time-fractional derivatives in the sense of Riemann–Liouville or Caputo. This problem is qualified as an inverse problem. The correct (vector) order can be found utilizing the available data. In this paper we offer an new method of solution of this inverse problem for linear systems of fractional order pseudo-differential equations. We prove that the Fourier transform of the vector-solution $$\widehat{U}(t, \xi )$$ evaluated at a fixed time instance, which becomes possible due to the available data, recovers uniquely the unknown vector-order of a system of governing pseudo-differential equations.

A fast, fully distributed nonlinear model predictive control algorithm with parametric sensitivity through Jacobi iteration

Centralized model predictive control is impractical for many complex systems due to communication burden and robustness issues. For these systems, distributed model predictive control (DMPC) is an alternative control strategy. In DMPC, the use of nonlinear first-principle model improves the prediction accuracy. However, it also brings about computational delay due to time-consuming optimization of large, non-convex nonlinear programs, which can then degrade the control performance. In this work, a fully distributed nonlinear model predictive control (DNMPC) algorithm is developed to accelerate control feedback. The input computation procedure contains background and online stages, in which prediction–correction mode is applied. In the background stage, the future state is predicted one step forward based on the nominal plant model. Each controller optimizes its own local input and exchanges latest information with other controllers to improve decision making. After distributed optimization, the local controllers collect optimality information to prepare for future computation. When the true state is available, the state prediction error can be calculated. Each controller formulates its local sensitivity equation based on parametric sensitivity. All the sensitivity equations are solved in parallel with application of the Jacobi iterative method. After solution, the nominal optimum is updated with the correction vector and then implemented to the plant. The theoretical analysis of the proposed method is presented. Four case studies are given to demonstrate the effectiveness of the proposed algorithm.

AN APPLICATION OF CADASTRAL FABRIC SYSTEM IN IMPROVING POSITIONAL ACCURACY OF CADASTRAL DATABASES IN MALAYSIA

Abstract. Cadastral fabric is perceived as a feasible solution to improve the speed, efficiency and quality of the cadastral measurement data to implement Positional Accuracy Improvement (PAI) and to support Coordinated Cadastral System (CCS) and Dynamic Coordinated Cadastral System (DCCS) in Malaysia. In light of this, this study aims to propose a system to upgrade the positional accuracy of the existing cadastral system through the utilisation of the cadastral fabric system. A comprehensive investigation on the capability of the proposed system is carried out. A total of four evaluation aspects is incorporated in the study to investigate the feasibility and capability of the software, viz. performance of geodetic least squares adjustment, quality assurance techniques, supporting functions, and user friendliness. This study utilises secondary data obtained from the Department of Surveying and Mapping Malaysia (DSMM). The test area is coded as Block B21701 which is located in Selangor, Malaysia. Results show that least square adjustment for the entire network is completed in a timely manner. Various quality assurance techniques are implementable, namely error ellipses, magnitude of correction vectors and adjustment trajectory, as well as inspection of adjusted online bearings. In addition, the system supports coordinate versioning, coordinates of various datum or projection. Last but not least, user friendliness of the system is identified through the software interface, interaction and automation functions. With that, it is concluded that the proposed system is highly feasible and capable to create a Cadastral Fabric to improve the positional accuracy of existing cadastral system used in Malaysia.

Two-time interval method to circumvent particle image velocimetry dynamic range

In the velocity range outside the velocity-dynamic range of a PIV (particle image velocimetry) system, the velocity measurements are known to result in noisy velocity vectors due to erroneous detection of correlation peaks, especially in the low-velocity region. Here a simple method is proposed that uses two different timings of a pulsed laser, in conjunction with a median-test of residuals based on the velocity data, to resolve both low and high velocities in a flow field. This method detects the erroneous vectors and replaces them with the correct vectors via a comparison of two sets of PIV data, procured simultaneously with an appropriate time-delay. The validation of the proposed method is demonstrated via a comparison of the present experimental data with previous experiments and DNS (direct numerical simulations) of a round turbulent jet.•The method uses two-time intervals to resolve two sets of velocities such that each set falls within the dynamic range of PIV corresponding to the time interval. (Dynamic range heavily depends on time interval).•A quantity called normalised residual median is defined based on “universal outlier detector” which is used to consolidate the two sets of data.•The strength of the method lies in its ability to obtain instantaneous entrainment velocity even in an unsteady flow.

Оценка индекса Ki67 при раке молочной железы с применением визуальной методики global

Introduction. Neoadjuvant therapy (NAT) provides an in vivo assessment of primary tumor sensitivity to systemic therapy. However, the data on Ki67 clinical significance as a prognostic factor for the effectiveness of chemotherapy in breast cancer (BC) cause controversy. The International Ki67 Interpretation Workgroup for Breast Cancer (IKWG) meeting held in October 2019, recommended developing a standardized methodology for visual global scoring, which is proposed to be used in clinical practice. The aim of the study was to assess the clinical significance of Ki67 using visual global scoring in BC. Materials and methods. The study included 32 patients with proven BC that received preoperative neoadjuvant therapy. We studied paraffin histological blocks, analyzed the Ki67 expression over time in patients with residual tumors, and assessed the correlations of clinical and morphological signs before treatment. Ki67 was determined as the overall average with global scoring. The Ki67 scoring app for smartphones was used as an auxiliary tool to increase the accuracy. Results. The median value of the Ki67 index before NAT was 75.0% (34.8–85.0) and after the therapy it was 1.0% (0.0–6.2) [p&lt;0.0001]. The Ki67 value was significantly lower (p=0.0077) [36% (30–75)] in the presence of residual tumor compared to that in patients without it [85% (78.8–90)]. Conclusion. The Ki67 index before NAT is a strongly prognostic factor of its effectiveness. The research of the Ki67 marker for concomitant diagnosis, which showed prognostic Ki67 role, can be considered as a correct diagnostic vector, only if strict analytical validity is observed. Keywords: Ki67, breast cancer, neoadjuvant therapy, global scoring, IKWG, Ki67 scoring app

Tend and forecast of Global Market instruments for laboratory Diagnostic

In-vitro diagnostics is the process of diagnosing various diseases using test kits and analyzers. The in vitro diagnostics (IVD) market is growing at a steady pace as awareness of various diseases grows and the government initiates various plans to reduce the incidence. Most IVD tests are performed in medical and clinical laboratories, but the advent of point-of-care (POC) technology has enabled testing in the presence of the patient and even at home. This technological advancement is driving the growth of the market over the forecast period. Market growth over the forecast period is expected to be driven by factors such as the increase in lifestyle-related diseases, the growing adoption of POC testing, the growing demand for biomarker-based testing, and demand from developing countries. Factors hindering the growth of the market are the increase in sales of counterfeit glucose test strips and strict regulatory approval procedures. The regulatory scenario is of concern as there is a wide range of tools and analyzes that range from Class I to Class III devices. Recently, there have been changes in legislation in the European Region, which has become a serious problem for market participants. There is an emerging trend in the market towards non-invasive prenatal testing, next generation sequencing (NGS), fluid biopsy and circulating tumor cell testing. Markets in India, Brazil and China are expected to grow at a rapid pace during the forecast period due to the high prevalence of infectious diseases in these countries. The purpose of this study is to assess the current global market for medical devices for in vitro diagnosis, identify the main factors and drivers influencing the growth and prospects of this market in the future. To conduct this study, as materials and methods, we used the analysis of these indicators of expert assessments published by specialists in Russian and international journals specializing in the implementation, promotion and development of laboratory diagnostic products. The results of the study determine the key promising markets for medical devices for in vitro diagnostics, as well as the correct vector for the development of the medical industry. In the final part, the main regions with the highest development potential over the next 10 years were identified, and indicators of the dynamics of the global market for medical instruments over the past 5-9 years were determined.

Optimization of the impact measurement of market structure on liquidity and volatility

Liquidity and volatility are the two barometers that allow stock markets to appreciate in terms of attractiveness, profitability and efficiency. Several macroeconomic and microstructure variables condition the level of liquidity that directly impact the asset allocation decisions of different investor profiles − institutional and individuals − and therefore the dynamics of the market as a whole. Volatility is the regulatory component that provides information on the level of risk that characterizes the market. Thus, the appreciation of these two elements is of considerable help to fund managers looking to optimize their equity pockets. In this work, we will use the liquidity ratio as a proxy variable for the liquidity of the Moroccan stock market, to estimate the indicators and factors that determine its short- and long-term variability. The appropriate econometric method would be to estimate an error correction vector model (ECVM) which has the property of determining the long- and short-term relationships between the variables. The volatility of the MASI index will be the subject of a second estimate to capture the shape of the function of its evolution.