Rate Forecasts Research Articles

How Brazil’s, Mexico’s and Argentina’s economies coped with the Covid-19 pandemic

This paper assesses how Brazil’s, Mexico’s and Argentina’s economies coped with the Covid-19 pandemic by generating two series of Q3 2022 to Q4 2050 Real GDP growth rate forecasts with wavelet analysis: 1) using data including the pandemic from Q4 1995 to Q1 2022 for Brazil, from Q4 1995 to Q2 2022 for Mexico and from Q1 2004 to Q1 2022 for Argentina) and 2) using data not including the pandemic from Q4 1995 to Q3 2019 for Brazil, from Q4 1995 to Q3 2019 for Mexico, from Q1 2004 to Q3 2019 for Argentina. The difference in forecast averages is an indicator of the resilience of the economy during the pandemic, the greater the difference, the better the resilience. Argentina’s economy shows a better resilience (+3.24%) than Mexico (+2.18%) and Brazil’s (-2.25%).

Accurate and explainable mortality forecasting with the LocalGLMnet

Recently, accurate forecasting of mortality rates with deep learning models has been investigated in several papers in the actuarial literature. Most of the models proposed to date are not explainable, making it difficult to communicate the basis on which mortality forecasts have been made. We adapt the LocalGLMnet of Richman, R. & Wüthrich, M. V. (2023). [LocalGLMnet: Interpretable deep learning for tabular data. Scandinavian Actuarial Journal 2023(1), 71–95] to produce explainable forecasts of mortality rates using locally connected neural networks, and we show that these can be interpreted as autoregressive time-series models of mortality rates. These forecasts are shown to be highly accurate on the Human Mortality Database and the United States Mortality Database. Finally, we show how regularizing the LocalGLMnet can produce improved forecasts, and that by applying auto-encoders, observations of mortality rates can be denoised to improve forecasts even further.

Exchange rate prediction research based on LSTM-ELM hybrid model

The fluctuation of exchange rates holds paramount importance for a country's economic and trade activities. Due to the non-stationary and nonlinear structural characteristics of exchange rate time series, accurately predicting exchange rate movements is a challenging task. Single machine learning models often exhibit lower precision in exchange rate prediction compared to combined machine learning models. Hence, employing a combined model approach aims to enhance the predictive performance of exchange rate models. Both Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) exhibit intricate structures, making their direct integration challenging. To address this issue, an innovative weighted approach is adopted in this study, combining LSTM and ELM models and further refining the combination weights using an improved Marine Predators Algorithm. This paper encompasses both univariate and multivariate prediction scenarios, employing two distinct allocation strategies for training and testing datasets. This is done to investigate the influence of different dataset allocations on exchange rate prediction. Finally, the proposed LSTM-ELM weighted combination exchange rate prediction model is compared with SVM, Random Forest, ELM, LSTM, and LSTM-ELM average combination models. Experimental results demonstrate that the LSTM-ELM weighted combination exchange rate prediction model outperforms the others in both univariate and multivariate prediction settings, yielding higher predictive accuracy and superior fitting performance. Consequently, the LSTM-ELM weighted combination prediction model proves to be effective in exchange rate forecasting.

Multivariate risks assessment for complex bio-systems by Gaidai reliability method

Spread of novel coronavirus and other flu-like illnesses, periodically causing increased death and morbidity rates, places pressures on national health systems. In order to provide a reliable long-term forecast of the new coronavirus infection rate, this research employs novel Gaidai bio-system reliability technique, especially suitable for multi-regional biological, environmental and public health systems. The goal of this study was to directly apply state of art statistical techniques to unprocessed raw clinical data, utilizing a multicenter, population-based biostatistical methodology. Epidemiological risks have been accurately forecasted, specifically for European Union member states. Based on their clinical survey data, suggested spatiotemporal methodology may be applied in a variety of public biological and health applications.

Modelling Mortality in Kenya

This research work seeks to analysis the mortality trend experienced in Kenya over the sample period 1950 to 2021 using a multidimensional modeling framework. Life table functions, namely; life expectancy, survival function and age at death distribution are applied to depict mortality characteristics. Life expectancy and survival rate have significantly improved. There has been a shift in mortality status from a high mortality population, to an intermediate stage and mortality risk factors have increased across age. Mortality concentration curve and index within the Lorenz curve and Gini coefficient framework are used to analyze the lifespan inequality. Lifespan inequality is high with negligible improvements over time. Gompertz force of mortality is then estimated, which is statistically significant at 5% level. Deaths at exact age 25 is about 35 per ten thousand, with the rate death rate increasing by 6.09% per year starting from age 25. Under the assumptions of stable population, where the mortality and fertility functions are independent of time, Malthusian parameter is estimated which is less than zero for selected years. Kenya is a shrinking population and death rate decrease with increase in Malthusian parameter. Finally, to model long-term mortality rate forecast, Lee-Carter model is estimated. The model is statistically significant at 5% level explaining 78.4% of the variations. Expected life expectancy at a given age is projected to increase, with life expectancy at birth in 2030 and 2071 being 65.6 and 70.5 years respectively.

The Research and Forecasts on Real GDP Growth and the Unemployment Rate in the United States

Empirically, the real GDP growth rates and the unemployment rates are typically negatively correlated. From the post-COVID-19 era, countries around the world have not fully recovered from the pandemic. In this case, forecasts of GDP growth and unemployment rate are a vital reference for governments to recover economies in the future. This study collected the real GDP and unemployment rate data of the United States from 1948 to 2023, studied their relationship through linear and nonlinear regression, and predicted their future trends, respectively, with the ARIMA model. By comparing linear regression and nonlinear regression (locally estimated scatterplot smoothing) between the GDP growth rate and change in the unemployment rate, this study found that nonlinear regression can more accurately express the relationship between these two factors. The forecasts of GDP growth rates and unemployment rates provided by the ARIMA model show a relatively optimistic future with healthy GDP growth and low unemployment. However, wide ranges of confidence intervals also pointed to the danger of low GDP growth and widespread unemployment. The relationship between GDP and unemployment and their projections in this study serve as valuable references for future economic planning and incentive policies in the U.S. government. It may also be applicable to other countries that have similar economic conditions.

RMB/USD Exchange Rate Forecasting and Analysis by SES and ARIMA Model

The RMB/USD exchange rate is important in the worldwide financial sector. Predicting the future RMB/USD exchange rate is critical in such circumstances. Forecasting the RMB/USD exchange rate effectively can assist people in analyzing the economic situation and avoiding financial dangers. This research proposes two statistical models, Simple Exponential Smoothing (SES) model and Autoregressive Integrated Moving (ARIMA) model, for forecasting the RMB/USD exchange rate, and then discovers a better model for forecasting exchange rates. To find a better model, forecast value with accuracy, and uncertainty and model limitation are compared. We find that although SES model shows a better accuracy, the residual check and uncertainty shows ARIMA is a better forecast. Moreover, ARIMA is more complex and has relatively less limitations for RMB/USD exchange rate. Thus, Autoregressive Integrated Moving model is a better forecast compared with Simple Exponential Smoothing model.

The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic

AbstractThis paper tests the accuracy and predictability of two term structure models using both yields‐only and factor‐augmented specifications focusing on the recent COVID‐19 crisis. In addition, we test the predictive ability of the yield curve on economic activity for the United States and other advanced countries. We provide evidence that models with an enhanced information set, including COVID‐19 factors, improve interest rate forecasts for this period. Also, we point out that term structure models can determine future variations in economic activity but are time‐ and country‐sensitive. Finally, out‐of‐sample analysis reveals that the use of factor‐augmented term structure models, to reflect the current economic and market conditions, improves their forecasting accuracy.

Geological and dynamic similarity for reservoir state prediction by well connectivity

For decades, computational reservoir modeling remained the main instrument for assessing reservoir state during the operations, and for the forecasting of future production rates. The recent development of reduced-order physics modeling and pure data-driven solutions made it possible to monitor and forecast the reservoir state in a more prompt way. In this work, we propose a new data-driven approach aimed at assessing inter-well connectivity. The solution is based on a machine learning (ML) forecasting model for production parameters (production rates, bottom-hole pressure) and the extraction of feature importances by additive explanation. Resulted inter-well connectivity coefficients are used as edges weights in well connectivity graphs. The validation of the proposed solution was performed by the comparison with Capacity Resistance Model performance and static geological similarity between wells derived from well logs analysis. Two case studies on benchmark reservoir models from clastic and carbonate environments are presented to ensure reliability of the proposed solution.

Multi-population mortality modelling and forecasting with divergence bounds

AbstractUnderstanding the mortality dynamics and forecasting its future evolution is crucial for insurance companies and governments facing the risk that individuals might live longer than expected (the so-called longevity risk). This paper introduces a neural network model that allows an accurate modelling and forecasting of the mortality rates of many populations. The neural network model we propose is designed to present a fully explainable structure, allowing for understanding how predictions are formulated. Furthermore, the model addresses the problem of measuring and managing the divergence of the long-term forecasts of the mortality rates arising when one decides to model the mortality of two or more populations simultaneously. Indeed, for many models available in the literature, this divergence grows over time, resulting in an ever-increasing trend in the gap in life expectancy among countries that appear unrealistic and biologically unreasonable. The proposed model allows the construction of analytical bounds for this divergence and illustrates that these bounds can be exploited to analyse and measure the dissimilarities between two or more populations and identify opportunities for longevity risk diversification. Numerical experiments performed using all the data from the Human Mortality Database data show that our model produces more accurate mortality forecasts with respect to some well-known stochastic mortality models and allows us to obtain valuable insights about the mortality pattern of the population considered.

Oil Production Rate Forecasting by SA-LSTM Model in Tight Reservoirs

Abstract The accurate forecasting of oil field production rate is a crucial indicator for each oil field’s successful development, but due to the complicated reservoir conditions and unknown underground environment, the high accuracy of production rate forecasting is a popular challenge. To find a low time consumption and high accuracy method for forecasting production rate, the current paper proposes a hybrid model, Simulated Annealing Long Short-Term Memory network (SA-LSTM), based on the daily oil production rate of tight reservoirs with the in situ data of injection and production rates in fractures. Furthermore, forecasting results are compared with the numerical simulation model output. The LSTM can effectively learn time-sequence problems, while SA can optimize the hyperparameters (learning rate, batch size, and decay rate) in LSTM to achieve higher accuracy. By conducting the optimized hyperparameters into the LSTM model, the daily oil production rate can be forecasted well. After training and predicting on existing production data, three different methods were used to forecast daily oil production for the next 300 days. The results were then validated using numerical simulations to compare the forecasting of LSTM and SA-LSTM. The results show that SA-LSTM can more efficiently and accurately predict daily oil production. The fitting accuracies of the three methods are as follows: numerical reservoir simulation (96.2%), LSTM (98.1%), and SA-LSTM (98.7%). The effectiveness of SA-LSTM in production rate is particularly outstanding. Using the same SA-LSTM model, we input the daily oil production data of twenty oil wells in the same block and make production prediction, and the effect is remarkable.

Analysis and Forecast of China's Unemployment Rate

The unemployment rate is an important economic indicator that measures the proportion of the unemployed labor force. The natural unemployment rate is the normal unemployment rate based on economic fluctuations. Analyze the unemployment rate to determine its root causes. By forecasting the unemployment rate, people can obtain an estimate of the future conditions of the labor market. Not only can the government make policy adjustments based on this, people can use this prediction to make wise career choices and planning, or to learn the necessary skills. The risk of unemployment is closely related to everyone, especially now during the economic downturn due to the impact of global catastrophic events such as COVID-19. Understanding the unemployment rate is an important part of analyzing the characteristics of the current labor market. If we can make more connections about the labor market and even make rough predictions about future trends, college students will be able to make future career choices that are more suitable for them. This study predicts the unemployment rate in the next nine years based on China's unemployment rate from 2012 to 2022, which will fluctuate within a fixed range. The government can increase spending (such as on education) and use monetary and finance polices to ensure that unemployment does not exceed forecasts.

Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis

AbstractThis paper uses the container shipping capacities of 11 major trade lanes, obtained from automatic identification system (AIS), to construct a common factor based on gray incidence analysis (GIA) in the aim of improving the predictability of containerized freight index. Our results show that the common factor generated by GIA consistently exhibits better out‐of‐sample prediction performances than principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO), meaning that GIA can extract more useful information for forecasting freight index. Our main findings are first, GIA can evaluate the similarity between the predictors and the predicted value. Unlike popular information combination method PCA, which cannot extract the relevant information from the predictors, GIA can extract the most relevant information of the predictors to the predicted value. Second, different from LASSO, which drops some information, GIA maintains the most information, because the container shipping capacities of different lanes all impact the freight index. Third, AIS data do provide information increments for freight rate forecasting. This research explores a new field application of gray relational analysis in information combination and presents one application of GIA in big data processing. This research shows the usefulness of AIS information in predicting freight index. Additionally, this research enlightens the prediction of freight rate based on big data from AIS.

A Method for Assessing Threats for Critical Infrastructure Objects on the Example of an Enemy’s Attack on the Sludge Storage Facilities of the Avdiivka Coke Chemical Plant


 
 
 The difficult situation in eastern Ukraine has made the problem of assessing potential man-made threats more urgent. Among the many industrial enterprises that suffered damage as a result of hostilities, the most environmentally hazardous ones were identified, namely: Yasinivka, Avdiivka, and Yenakiieve coke-chemical plants; Yenakiieve, Makiiivka, and Donetsk metallurgical plants; Alchevsk metallurgical plant; Lysychansk oil refinery; Donetsk state chemical plant; Sloviansk, Luhansk, Vuhlehirsk, and Myronivka thermal power plants; chemical industry enterprises such as Severodonetsk Azot plant and Horlivka Styrol plant. The work proposes a method for assessing threats to critical infrastructure objects from enemy attacks. By applying graph theory and probability theory, the proposed method allows for obtaining quantitative assessments of threats and analyzing possible scenarios to develop cascading effects of emergencies. This helps determine the potential consequences of an attack and evaluate the risks associated with damaging critical infrastructure. The proposed model is based on a structural-logical approach that considers the inter- relationships between different components of the natural-technological geosystem. It will enable obtaining more objective and reliable results when assessing potential military-technological threats to CIOs and analyzing possible cascading effects in the event of damage to these objects. Developing such a model is essential in improving the approaches to assessing threats to CIOs, providing more accu- rate forecasts, and effective risk management. The results of this research can be useful for practical applications in developing strate- gies for the security of CIOs in the context of armed conflict. The results obtained in this work can be useful in developing models for assessing ecological and technological threats to critical infrastructure objects, particularly in the context of military operations. This method will improve the level of protection and help develop effective strategies to counter potential threats to critical infrastructure.
 
 

QLIKE and VaR evaluation approaches for volatility forecasts of exchange rates using structural models in US and UK

QLIKE and VaR evaluation approaches for volatility forecasts of exchange rates using structural models in US and UK

СТРЕС-ТЕСТУВАННЯ У ЗАБЕЗПЕЧЕННІ ФІНАНСОВОЇ СКЛАДОВОЇ ЕКОНОМІЧНОЇ БЕЗПЕКИ ПІДПРИЄМСТВА З УРАХУВАННЯМ КРИТЕРІЮ ФІНАНСОВОГО РЕСУРСОЗБЕРЕЖЕННЯ

The purpose of the article is to research stress testing for the implementation of the mechanism for ensuring the financial component of the economic security of the enterprise, taking into account the criterion of financial resource saving. The relevance of this research lies in the application of forecasting within the framework of stress testing, the determination of the possible options for the development of the events in the relation to external and internal factors of the enterprise and its implementation in the mechanism of financial security. Research methods are applied in the research: system analysis and synthesis, systematization, comparison, method of statistical data processing. In the conditions of uncertainty of the external and internal conditions of the company’s activity, the role of forecasting, on which stress testing is based, is growing especially acutely. The article examines the essence of the concept of stress testing and considers the views of various authors regarding its application in order to ensure the economic activity of the enterprises. The use of the stress testing is proposed in order to improve the mechanism of the ensuring the financial component of the economic security of the enterprise, taking into account the criterion of financial resource saving. It was determined that the stress testing process is associated with risks, and since deposit risk is the main factor for financial resource conservation, the influence of external and internal factors on deposit risks was considered. The external factors include: the reliability of a commercial bank, changes in the legal framework, the political and economic situation in the country, exchange rate fluctuations, inflation, and the internal factors include: an unsuccessful choice of a bank for the investing funds and the depositor’s immediate need for the cash. Tools for the ensuring financial security are proposed, taking into account the criterion of the financial resource saving. As a part of stress testing, a forecast of the US dollar exchange rate and the consumer price index was made. The practical value of the article lies in the fact that, based on the completed forecast, measures are proposed to ensure the financial component of economic security based on financial resource saving.

Forecast and Analysis of Inflation Rate Based on Improved BVAR Model

Forecast and Analysis of Inflation Rate Based on Improved BVAR Model

System Identification Techniques for Soft Sensors and Multiphase Flow Metering

Production rates estimation and forecasting is a complex problem in multiphase flow systems, such as oil wells. In the oil and gas industry, this task is normally performed by first principles simulation models, which are costly to maintain and adjust. An alternative to this process could be black-box models based on historical data to perform this task. This work proposes to perform an extensive evaluation of existing regressors to create a black-box system identification model to predict oil well liquid flow rates. Improvement strategies such as the creation of error models, time series decomposition, and the use of current-time data are also evaluated. The final results demonstrated the importance of pressure and temperature data for the well flow rate determination and identified the use of current-time data as the best tested technique to improve the performance of the final model.

Enhancing Trade Settlement: A Meta- Analysis and VAR Model Approach to RMB Activation in International Transactions

Purpose - This paper aims to empirically analyze factors associated with international trade settlement methods and cross-border Renmingbi (RMB) settlement. RStudio was employed to perform an empirical analysis of prior research via meta-analysis, and to examine research trends pertaining to international trade settlement. Following the preceding research, factors such as economic scale, trade scale, RMB exchange rate forecast, money supply, inflation rate, and overseas financial market development have been identified as influential factors on RMB settlement. This study aims to investigate the impact on international RMB settlement. This paper provides a summary of the research areas pertaining to trade settlement methods by Chinese scholars, and examines deficiencies in China's research in this field.
 Design/methodology - The empirical model takes into account both direct and indirect factors that influence RMB settlement. Utilizing the established theoretical framework to ascertain the correlation between different factors and the influence on RMB settlement, with the aim of offering recommendations for the facilitation of RMB settlement is the goal of the study.
 Findings - The study reveals that RMB settlement transaction volume has a significant impact, while other factors such as economic scale, exchange rate, and trade scale also exert influence. Among these factors, economic scale exerts the most significant influence. However, over time, economic growth may reach a point of saturation, leading to a gradual reduction in its impact on RMB settlement.
 Originality/value - This research aims to conduct a meta-analysis to statistically analyze the current research status and trends of trade settlement methods. This approach seeks to provide an objective and quantitative understanding of the research context and direction. Furthermore, the findings of the meta-analysis served as foundational data to employ a Vector Autoregression (VAR) model in a comprehensive analysis to simulate and forecast the interrelationships and effects of diverse factors.

FREIGHT RATE AND DEMAND FORECASTING IN ROAD FREIGHT TRANSPORTATION USING ECONOMETRIC AND ARTIFICIAL INTELLIGENCE METHODS

The digitisation of the transportation sector and data availability have opened up new opportunities to implement data-driven methods for improving company performance. This article analyses demand and freight rate forecasting techniques in the context of the road freight transportation company. The European market was analysed in this research, and direction from the Netherlands to Italy was selected for the case study. Performed investigation showed that econometric models such as Auto-Regressive Integrated Moving Average (ARIMA) used for demand prognosis provide good results. Freight rate forecasting is different; econometric models, including multivariate models ARIMA with exogenous variables (ARIMAX) and Seasonal ARIMAX (SARIMAX), do not perform satisfactorily under specified time intervals, therefore MultiLayer Perceptron (MLP) was used as a solution. It can be seen that Artificial Intelligence (AI) based methods provide better results. Despite its success, the AI-based approach alone is not recommended for practical implementation since forecasted input parameters are necessary. Lastly, the study uncovers a valuable insight. A strong correlation (0.86) between spot and contract rates was found, and the article shows how current spot rates can be used for contract rate forecasting.