A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long‐memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression‐based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID‐19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

Combination forecast based on financial stress categories for global equity market volatility: the evidence during the COVID-19 and the global financial crisis periods

The 2008 global financial crisis and the COVID-19 pandemic both decrease economic growth and lead to high uncertainty in global stock markets, and financial stress information is closely linked to financial crises. To improve the predictability of the realized volatility of the global equity indices during crises, we examine the predictive role of the Global Financial Stress Index (GFSI) and its categories. We find that the combination predictions based on GFSI's five incorporated categories and three region-based categories outperform the predictions based on the raw GFSI for most indices. Specifically, the DMSPE combination model with a low discount factor has accurate forecasts for 5- and 22-day-ahead realized volatility, and it also performs better than the equal-weighted and the trimmed mean combination methods. In this study, we present a comprehensive analysis of the predictive role of financial stress information in stock market volatility during crises, and the empirical evidence provides a positive case against the ‘forecast combination puzzle'. Our findings are very instructive for policymakers and investors to make their own short-term and long-term plans in crisis. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

COMPARISON OF PERFORMANCES OF FUZZY LOGIC AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) FOR ESTIMATING EMPLOYEE LABOR LOSS

As a result of artificial intelligence research that started in the 1950s, the need for human beings in all sectors and labor markets constantly decreases. The increase in the total cost of the labor force increases the productivity pressure on the labor. For this reason, the workforce participating in production is expected to be more efficient and productive. For this reason, the loss of labor is carefully monitored and tried to be reduced as much as possible. However, with each passing day, labor losses are inevitable due to personnel turnover, work accidents, dismissals, and absenteeism. Humanity is still struggling, mainly due to the contagious covid-19 virus, which has recently affected the world. Since it is a condition that affects human health, its adverse effects have been observed in many areas where people are present. Especially in this period, unpredictable workforce losses have occurred in the production and service sectors since people are mostly the primary workforce. Since there is no plan and measure for such a situation in most risk planning, it also brings labor losses and costs. In this study, In order to examine the relationship between health problems and loss of labor, the amount of lost labor due to employees who could not come to work due to health-related reasons was tried to be estimated by Fuzzy Logic and ANFIS methods. This study examined three-year absenteeism data of employees in a courier company, and twenty-eight reasons for absenteeism were determined. The amount of labor loss was estimated using Fuzzy Logic and ANFIS methods, using five factors that cause absenteeism. Estimated and actual values were statistically compared with MAD MAPE, MSE, and RMSE performance measurement values. With fuzzy logic, the MAD value is 4.76;the MAPE value is 155.7;The MSE value was calculated as 52.7, and the RMSE value as 7.26. In ANFIS, the MAD value is 3.2, the MAPE value of 86.24, MSE of 27.5;The RMSE value was calculated as 5.25. When the results are compared, it has been seen that the ANFIS method obtains closer estimations than the fuzzy logic method.

Hedging potentials of green investments against climate and oil market risks

PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.

The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country's overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely-exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country's economy.

ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION.

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box-Ljung test. The ARIMA(11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population.

Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis

Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007-2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

Modeling SARS-CoV-2 True Infections in Catalonia through a Digital Twin

A dynamic view of the evolution of the infections of SARS-CoV-2 in Catalonia using a Digital Twin approach that forecasts the true infection curve is presented. The forecast model incorporates the vaccination process, the confinement, and the detection rate, and virtually allows to consider any nonpharmaceutical intervention, enabling to understand their effects on the disease's containment while forecasting the trend of the pandemic. A continuous validation process of the model is performed using real data and an optimization model that automatically provides information regarding the effects of the containment actions on the population. To simplify this validation process, a formal graphical language that simplifies the interaction with the different specialists and an easy modification of the model parameters are used. The Digital Twin of the pandemic in Catalonia provides a forecast of the future trend of the SARS-CoV-2 spread and information regarding the true cases and effectiveness of the NPIs to control the SARS-CoV-2 spread over the population. This approach can be applied easily to other regions and can become an excellent tool for decision-making. © 2023 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.

The role of categorical EPU indices in predicting stock-market returns

This study investigates the predictive ability of categorical economic-policy uncertainty (EPU) indices for stock-market returns. The results indicate that some categorical EPU indices have superior predictive ability for stock returns and even achieve higher realized utility than the original EPU index and popular predictors. Furthermore, the diffusion indices based on EPU categories, especially those that use partial least squares (PLS) to extract the principal components, more effectively use the forecast information contained in categorical EPU indices, resulting in improved forecast performance, including reduced forecast errors and increased economic value for investors. In addition, the categorical EPU indices show superior forecasting performance during economic-expansion, the China-US trade-war, and COVID-19 pandemic periods.

Analyst Research Activity During the COVID-19 Pandemic

This paper documents that, in response to the COVID-19 pandemic, analysts increase their research activity and significantly revise their forecasts when compared to the pre-pandemic period. Uncertainty-adjusted forecast errors are either comparable or smaller during the pandemic compared to the pre-pandemic period. Investor attention and price reactions to analyst forecast revisions are higher during the pandemic and the effect is stronger in periods where investors actively search for information about firms. During the pandemic, investors value analyst price discovery role more than their role in interpreting public information. Jointly, the results suggest that analysts play an important information intermediation role during the COVID-19 pandemic. © 2023 The Author. Abacus published by John Wiley & Sons Australia, Ltd on behalf of Accounting Foundation, The University of Sydney.

Urban Public Epidemic Prevention and Control Model Based on Nonlinear Differential Equations

This paper proposes a new epidemiological mathematical model based on the dynamics of urban public epidemic prevention and control model. Then, the nonlinear differential equation of epidemic propagation dynamics is deduced. Secondly, this paper uses the exponential equation to fit the curve, takes three days as the optimal window time, and estimates the turning point of the urban public epidemic. Again, this paper establishes a dynamic model of dynamic experience transfer. Finally, this paper uses the COVID19 example to verify the public epidemic prevention and control problems described in the text. Experimental simulations show that the algorithm can better grasp important epidemiological dynamics.

US weekly economic index: Replication and extension

We revisit the US weekly economic index (WEI) put forth by Lewis, Mertens, Stock and Trivedi (2021). In a narrow sense, we replicate their main results with data gathered from its original sources. In a wide sense, we apply the methodology established in Wegmüller, Glocker and Guggia (2023) to adjust the weekly input series for seasonal patterns, calendar day effects, and excess volatility. In a long sense, we show that our proposed data adjustment significantly improves the nowcasting performance of the WEI. © 2023 John Wiley & Sons, Ltd.

Forecasting COVID-19 cases in Egypt using ARIMA-based time-series analysis

Objectives: The World Health Organization declared the novel coronavirus (COVID-19) outbreak a public health emer-gency of international concern on January 30, 2020. Since it was first identified, COVID-19 has infected more than one hundred million people worldwide, with more than two million fatalities. This study focuses on the interpretation of the distribution of COVID-19 in Egypt to develop an effective forecasting model that can be used as a decision-making mechanism to administer health interventions and mitigate the transmission of COVID-19. Method(s): A model was developed using the data collected by the Egyptian Ministry of Health and used it to predict possible COVID-19 cases in Egypt. Result(s): Statistics obtained based on time-series and kinetic model analyses suggest that the total number of CO-VID-19 cases in mainland Egypt could reach 11076 per week (March 1, 2020 through January 24, 2021) and the number of simple regenerations could reach 12. Analysis of the ARIMA (2, 1, 2) and (2, 1, 3) sequences shows a rise in the number of COVID-19 events. Conclusion(s): The developed forecasting model can help the government and medical personnel plan for the imminent conditions and ensure that healthcare systems are prepared to deal with them.Copyright © 2021 by Eurasian Journal of Medicine and Oncology.

Modeling reality with mathematics

Simulating the behavior of a human heart, predicting tomorrow's weather, optimizing the aerodynamics of a sailboat, finding the ideal cooking time for a hamburger: to solve these problems, cardiologists, meteorologists, sportsmen, and engineers can count on math help. This book will lead you to the discovery of a magical world, made up of equations, in which a huge variety of important problems for our life can find useful answers. © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. All rights reserved.

Can AIS data improve the short-term forecast of weekly dry bulk cargo port throughput? - a machine-learning approach

This study examines the development of a machine-learning model to forecast weekly throughputs of dry bulk cargo in the short term based on automatic identification system (AIS) data. Specifically, the weekly amounts of iron ore exported from several major ports in Australia and Brazil in the latter half of 2019 are forecasted three weeks in advance using a long short-term memory model. We examine many variables extracted from AIS data, including the vessel position, speed, draught, and destination, as the input features of the model. Consequently, we develop a highly accurate forecasting model that uses four influential variables derived from AIS data, namely, vessel traffic around the target port and in the region, vessel traffic at major partner import ports, and vessel traffic at the target port during the past year. Finally, by forecasting the weekly port cargo throughputs in the first half of 2020, which was affected by the COVID-19 pandemic, the applicability of the model is confirmed, even for ports where the throughput fluctuates significantly. In particular, this study demonstrates that AIS data are beneficial not only as a real-time traffic database but also as a database containing various related explanatory variables, including historical vessel traffic. [ FROM AUTHOR] Copyright of Maritime Policy & Management is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

Growth of an epidemic is influenced by the natural variation in climatic conditions and enforcement variation in government stringency policies. Though these variations do not prompt an instant change in the growth of an epidemic, effects of climatic conditions and stringency policies become apparent over time. Time-lagged relationships and functional dynamic connectivity among meteorological covariates and stringency levels generate many lagged features deemed to be important for prediction of reproduction rate, a measure for growth of an epidemic. This empirical study examines the importance scores of lagged features and implements distributed lag inspired feature selection with back testing for model selection and forecasting. A verification forecasting scheme is developed for continuous monitoring of the growth of an epidemic. We have demonstrated the monitoring process by computing a week ahead expected target of the reproduction rate and then by computing a one day ahead verification forecast to evaluate the progress towards the expected target. This evaluation procedure will aid the analysts with a decision making tool for any early adjustment of control options to suppress the transmission.

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model, and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic. © 2022 IEEE.

Social Vulnerability and Macroeconomic Forecast: A Review from the Current Context.

In the current context, various factors add to the existing social and economic crisis, such as the COVID-19 pandemic and the Russian invasion of Ukraine, which, when articulated with common conflicting facts, increase projections regarding the slowdown in the economy, with an impact on inflation and declines in global economic growth. As part of the adverse effects of these variables, if the increase in the cost of basic products and services was anticipated, the supply chain escaped, food safety, the widening of social gaps, poverty, contributing to the massification of restrictive medicines, increasing vulnerabilities in social and political scenarios, in addition to less dynamism in the global economy, in the availability of food resources, increases in energy prices, inflationary pressure, among others. Previously, this investigative note explores the macroeconomic forecast, taking as reference the informants of the year 2022 brought by the World Bank, the International Monetary Fund, the Economic Committee for Latin America and the Caribbean and the Inter-American Development Bank. (English) [ FROM AUTHOR] En el contexto actual, diversos factores se suman a la crisis social y económica a existente, como la pandemia COVID-19 y la invasión rusa a Ucrania que, al articularse con los hechos conflictivos comunes, aumentan las proyecciones con respecto a la desaceleración de la economía, con incidencia en la inflación y mermas en el crecimiento económico global. Como parte de los efectos adversos de estas va1riables, se prevé el aumento en el costo de los productos y servicios básicos, escasez en la cadena de suministros, inseguridad alimentaria, ampliación de las brechas sociales, de la pobreza, contribuyendo a la toma de medidas restrictivas, aumentando las vulnerabilidades en los escenarios sociales y en materia política, además de un menor dinamismo en la economía global, en la disponibilidad de recursos alimentarios, aumentos de los precios de la energía, presión inflacionaria, entre otros. En virtud de lo anterior, la presente nota de investigación explora la prognosis macroeconómica, tomando como referentes los informes del año 2022 aportados por el Banco Mundial, el Fondo Monetario Internacional, la Comisión Económica para América Latina y el Caribe y el Banco Interamericano de Desarrollo. (Spanish) [ FROM AUTHOR] Copyright of Revista de Filosofía is the property of Revista de Filosofia-Universidad del Zulia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Forecasting the Impact of the COVID-19 Outbreak on China's Cotton Exports by Modified Discrete Grey Model with Limited Data

The sudden outbreak of COVID-19 has created dramatic challenges for public health and textile export trade worldwide. Such abrupt changes are difficult to predict due to the inherently high complexity and nonlinearity, especially with limited data. This article proposes a novel modified discrete grey model with weakening buffer operators, called BODGM (1,1), for forecasting the impact of pandemic-induced uncertainty on the volatility of cotton exports in China under limited samples. First, the Mann-Kendall test examines how pandemic-induced uncertainty affects cotton exports, based on China's monthly cotton export data from June 2014 to August 2022. Second, buffer operators are employed to weaken the nonlinear trends and correct the tentative predictions of the discrete grey model. Then, the BODGM (1,1) model was validated by comparison with four alternative models. The results indicate that the BODGM (1,1) model was particularly promising for identifying mutational fluctuations in cotton exports and outperformed the GM (1,1), DGM (1,1), ARIMA and linear regression models in fitting and prediction accuracy under volatility and limited data. The BODGM (1,1) model forecast results for China showed that cotton export volume was expected to show signs of recovery over the next 12 months. The findings of this study may provide a basis for formulating trade policies to mitigate the impact of the COVID-19 outbreak on export resources and build their resilience to future pandemics.

Can Consumer Credit Stimulate Consumer Confidence? Evidence from the Time-Varying Aspect

This study discusses the nexus between consumer credit (CC) and consumer confidence (CF) in the case of China with a bootstrap rolling-window causality test. The new empirical results demonstrate that CC improves CF in specific periods by loosening liquidity constraints and increasing consumer power temporarily. Meanwhile, a negative link is also found, which can be explained by policy adjustment and financial instabilities. On the contrary, CF negatively influences CC in some periods, reflecting consumers' attitudes toward the future would change borrowing behaviors. But this relationship would be disrupted by government intervention and public events such as the COVID-19 pandemic. The contribution is that time-varying, multiple-directional and dynamic causalities are captured, which enriches the theoretical framework between CC and CF. Therefore, the government must design and adjust loaning policies against specific circumstances and transmit positive signs to consumers. Future study needs to pay attention to different types of CC and try to reveal their heterogeneous influences on CF. In addition, the effect evaluation for CC policy is also another focus in the next research.