“Asymmetric Export Volatility Shock Behaviour” of Lower Middle Income (LMI) Countries in South East Asia During COVID-19 Pandemic

COVID-19 Pandemic still affecting all countries. South Asian economies and that particularly India is no exception. Because of this “uncertainty shock”, India’s GDP has contracted by 3.1percent in the last quarter of 2020. The present empirical work covers broadly the “asymmetric spillover and related noisy shocks” surrounded with trade (export) volatility about LMI nations in “two phases” i.e. in terms of considering the During pandemic phase (DC) the period from April 2019 till June 2020 and Pre-pandemic phase (PC) from April 2013 till November 2020. A comparative trade volatility asymmetries analysis were applied using a nonlinear volatility function i.e. exponential weighted moving average (EWMA), and identification of noisy behaviour after the initial post-recovery for empirical evidence. The empirical findings discovered that there were “extended” non-smooth and noisy “shock propagation” post initial recovery across two phases by the use of VAR and VECM outcomes. Bangladesh and Pakistan were stronger “Noisy shock contributors” while Nepal and Sri Lanka were turned out to be the strongest “Noisy shock receivers”. This “noisy” behaviour implies “uncertainty” and chaos on the international trade front resulting in higherthan expected volatility in trade figures and in-built destabilized momentum in the impulses. The results also relate to the possible opportunities of intra-regional trade convergence as a policy imperative.

Implementation of a Commitment Machine for an Adaptive and Robust Expected Shortfall Estimation

This study proposes a metaheuristic for the selection of models among different Expected Shortfall (ES) estimation methods. The proposed approach, denominated “Commitment Machine” (CM), has a strong focus on assets cross-correlation and allows to measure adaptively the ES, dynamically evaluating which is the most performing method through the minimization of a loss function. The CM algorithm compares four different ES estimation techniques which all take into account the interaction effects among assets: a Bayesian Vector autoregressive model, Stochastic Differential Equation (SDE) numerical schemes with Exponential Weighted Moving Average (EWMA), a Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) volatility model and a hybrid method that integrates Dynamic Recurrent Neural Networks together with a Monte Carlo approach. The integration of traditional Monte Carlo approaches with Machine Learning technologies and the heterogeneity of dynamically selected methodologies lead to an improved estimation of the ES. The study describes the techniques adopted by the CM and the logic behind model selection; moreover, it provides a market application case of the proposed metaheuristic, by simulating an equally weighted multi-asset portfolio.

State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H∞ Filtering

When the classical H∞ algorithm (HIF) is applied to estimate the state of charge (SOC) of a lithium battery, the influence of historical data is usually ignored, resulting in an increase in the estimation error. In order to improve the accuracy of SOC estimation, this paper proposes an extended exponential weighted moving average H∞ algorithm (EE-HIF) in view of the influence of historical data. By designing the Gaussian function, the weighted distribution of the data at different times can effectively reduce the estimation error caused by the inaccuracy of the lithium battery model. In addition, when the system contains Gaussian white noise and alternating current input, the proposed method can achieve a faster convergence speed and better robustness. Simulation results show the advantages of the proposed algorithm, as compared to an HIF filtering algorithm and an exponentially weighted moving average H∞ algorithm (EWMA).

Using optimized statistical distances to confront distributed denial of service attacks in software defined networks

Software-defined networks (SDN) are an emerging architecture that provides promising amends to put an end to current infrastructure constraints by optimized bandwidth utilization, flexibility in network management and configuration, and pulling down operating costs in traditional network structures. Despite the advantages of this architecture, SDNs may become the victim of a distributed denial of service (DDOS) attacks as the result of potential vulnerabilities in various layers. Therefore, the rapid detection of attack traffic in the early stages is very important. In this paper, we have proposed statistical solution to detect and to mitigate distributed denial of service attack in software-defined networks utilizing the unique capabilities of the SDN architecture. Here, the exponential weighted moving average protection mechanism (EWMA) in statistical distances is exploited. The simulation results of our extensive experiments showed that our mechanism is able to quick detection of attack traffics and take amendatory actions. Moreover, the evaluations show the superiority of the proposed algorithm with respect to other statistical methods.

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

Memory Type Ratio and Product Estimators in Stratified Sampling

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data

A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.