Narrow Results

This study aims to exploit the Artificial intelligence (AI)-based computing paradigm to analyze the economic system to define the price movements of unsustainable expansion, rapid collapse, and eventual equilibrium that characterize financial bubbles represented with differential equations to portray the role of societal contagion and group mentality from a behavioral viewpoint with the market population classified as bull, i.e., optimistic, neutrals, bear, i.e., pessimistic, and quitter categories. The concept of the financial bubble is characterized as an unexpected rise in prices that is rapidly followed by a shrill decline and retrospectively appears as a consequence of such uncertainty in price value. AI-based applications facilitate financial analysts with innovative computational paradigms for gaining deep insights, improving predictive accuracy, and creating sustained vigorous risk management stratagems for the financial bubble framework and solving with supervised nonlinear autoregressive exogenous networks with optimized Bayesian regularization algorithm to accomplish the reasonable predictive accuracy and malleability for the solution of financial bubble behavioral dynamics. The Adams numerical solver accomplishes the acquisition of synthetic data for the execution of a multi-layer structure of exogenous networks to solve for financial bubble parameters termed as contagion rate of optimistic behavior, bearish behavior, pessimist’s average time of staying in the bearish group, the pessimist’s population effect on the autonomous supply and the rate of optimists conversion to pessimists group while assuming other parameters values to be fixed for demand and supply functions. A consistent overlap between proposed results and synthetic numerical values of a financial bubble model is indicated by negligible error value that verifies the exogenous network’s effectiveness and is verified by the enclosure of several evaluation measures of the precision and efficiency, through mean square error objective functions, adaptive amendable parameters, error dispersal, and input-error correlation analyses.

The contributions of AI-based applications in monitoring real-time financial transactions, and detecting fraudulent activity by scrutinizing consumer behavior, transaction patterns, and other relevant measures are worth mentioning for potential threats identification in the fractional financial crime population dynamics. Leveraging these financial crime systems in terms of population dynamics with the exploitation of supervised Nonlinear Autoregressive Exogenous Networks Optimized with the Bayesian Regularization (NARX-BR) procedures for attaining sufficient accuracy and flexibility for the approximate solutions of a fractional variant of stiff Nonlinear Financial Crime Population Dynamics (NFCPDs) differential system. The population dynamics for the financial crime model are classified mainly into susceptible persons, financial criminals, individuals being prosecuted individuals under prosecution, imprisoned persons, and honest individuals by law. The acquisition of synthetic data generated with Grünwald–Letnikov (GL) fractional operator for the multi-layer structure execution of NARX-BR procedure for solving NFCPDs for varying financial crime parameters, such as influence rate, recruitment rate, conversion rate to honest people, freedom rate, financial criminal prosecution rate per capita, percentage of discharge rate from prosecution, transition rate to prison, discharge and acquittal rate from prosecutions. The estimated outcomes of NARX-BR and the calculated numerical solutions of NFCPDs consistently overlap implying that the error between the results is approximately equal to zero. The effectiveness of model performance is assessed through a variety of evaluation metrics, that include minimization of mean square error-based objective function, adaptive regulating parameters of the optimization algorithm, error distribution plots, regression studies, error endogeneity, and cross-correlation analyses. This study contributes to integrating fractional calculus with the knacks of innovative AI and open paths to provide data-driven efficient solution-based policy recommendations in the field of financial crime population dynamics.

Artificial intelligence (AI)-based applications contribute to monitoring financial transactions and detect fraudulent activity in real-time by analyzing transaction patterns, consumer behavior, and other statistics, making them essential for quickly addressing potential threats in the fight against financial crime dynamics. Leveraging financial crime systems with intelligent supervised neuro-structures exploiting nonlinear autoregressive exogenous networks integrating damped least square (NARX-DLS) optimization methods to achieve an appropriate degree of accuracy and adaptability for the estimation of complex nonlinear financial crime differential systems (NFCDSs). The representative NFCDS for financial crime indicators is expressed as susceptible individuals, financial criminals, individuals under prosecution, imprisoned individuals, and honest individuals. The Adams numerical solver accomplishes the acquisition of synthetic data for the layer structure NARX-DLS algorithm execution to solve NFCDSs for various financial crime parameters, such as recruitment rate, influence rate, conversion rate to honest people, financial criminal prosecution rate per capita, discharge and acquittal rate from prosecutions, percentage of discharge rate from prosecution, transition rate to prison, and freedom rate. A sturdy overlap between the solutions of NARX-DLSs and the reference numerical results of NFCDSs implies that the error value is close to a desirable value of zero. The effectiveness of the NARX-DLSs is evidenced by including a variety of assessment metrics that carefully examine the model’s correctness and efficacy, including mean square error-based convergence arches, adaptive regulating parameters, error distribution, and input-error/cross-correlation analyses.

One of the most empirically successful tools for studying dependence between different quantities in econometrics is the tool of vine copulas. In this paper, we explain this empirical success by showing that the most widely used vine copulas are, in effect, the results of using the general fuzzy methodology. To be more precise, vine copulas correspond to a natural extension of the traditional fuzzy methodology, when we allow several different “and”-operations (t-norms), and some of these t-norms can be nonassociative.

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

In this paper, we propose to analyze multiple cross-correlation between six stock market indexes (Dow Jones Industrial Average, Nasdaq Composite, DAX Performance, FTSE 100, S&P/TSX Composite and Nikkei 225) based on the multiple detrended cross-correlation coefficient, ${\mathrm{\text{DMC}}}_x^2$. Due to its importance and long existence, we will have as the base index, acting as dependent variable, the Dow Jones Industrial Average, which will be compared with the other five indexes in all possible combinations. In addition to calculating the ${\mathrm{\text{DMC}}}_x^2$ for all combinations with Dow Jones, we will also infer the relative strength of each index in relation to the whole, and this will be done here through an unprecedented map of relative strength. With this map it will be possible to verify that the Nikkei 225 index has less weight for all time-scales and also all combinations, and that there is better distribution in the Nasdaq and DAX combination. Finally, we present more possibilities for this application throughout the paper.

The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial "dynamically purified" price process that in theory allows to eliminate the impact of the stock price movements. The complete elimination would be possible if the option prices were available for continuous sets of strike prices and expiration times. In practice, we have to use only finite sets of available prices. We discuss the construction of this process from the available option prices using different methods. In order to overcome the incompleteness of the available option prices, we suggests several interpolation approaches, including the first order Taylor series extrapolation and quadratic interpolation. We examine the potential of the implied volatility derived from this proposed process for forecasting of the future volatility, in comparison with the traditional implied volatility process such as the volatility index VIX.

It is with profound sadness that we write this statement for the former editor of this journal, our colleague and friend, Michael McAleer. Mike passed away peacefully on July 8, 2021, and he will be sorely missed by his vast number of colleagues and friends. Mike served on the editorial board of the Annals of Financial Economics (AFE) for more than 16 years and was the Editor-in-Chief since 2016. Mike was a wonderful friend, colleague, and mentor to all that knew him, and provided countless hours of service to AFE. He touched our lives deeply and was ever ready to lend a hand in any way he could, whether through his vast knowledge of econometrics, his willingness to work together on research projects, his efforts on behalf of this journal, or his contagious joie de vivre. We will miss him greatly. In the remainder of this editorial, we include a short biography, as well as a number of statements from co-authors, colleagues and friends of Mike.

The study focused on economic influence of climate change variables on net income of agribusinesses, particularly on crop production in Niger Delta region of Nigeria. It estimated and projected economic impacts of rainfall and temperature on the net income of crop farmers. Then it was guided by two research questions and two hypotheses. This study relied on secondary data from 129 previous studies sampled purposively. Data were collected using structured document review guide. Data were analysed using Least square multiple regression and ANOVA statistic to test the tasted null hypotheses. Findings of the study revealed that 69% of the variations in net income of crop production in Niger Delta is as a result of the alterations in rainfall and temperature, as a decrease in rainfall by 1mm resulted in decrease in net income by $-\text{₦}$4.39k while an increase in temperature by 1^∘C resulted in decrease in net income by $-\text{₦}$6206.40k. The expected net income of crop production in Niger Delta is projected to continually decrease, from $\text{₦}$4062.14$\pm$$\text{₦}$5986.25k in the year 2025 to $\text{₦}$631.09$\pm$5986.25k in the year 2050, based on future values of rainfall and temperature. The null hypotheses tested showed that variability in rainfall and temperature is directly proportional ($p<0.05$) to the changes in net income of crop production for both estimation and projection in the econometric analysis. Based on the findings and implications of the study, it was recommended among others that climate change impact assessment committees should be set up at rural, state and federal levels to evaluate the impacts, and propose indigenous policies for addressing present and future losses in agribusinesses.

The following sections are included:

• Purpose

• Model specification

• The data

• Estimation results

• Implications for oil supply

• References

Climate change has gained global attention in the past few decades due to its impact on humanity. This chapter examines the impact assessment of energy, agriculture, and socioeconomic indicators on carbon dioxide emissions in Ghana using a multivariate analysis (neural network and nonlinear iterative partial least squares regression). Topics covered include drivers of greenhouse gas emissions, Ghana’s climate change profile, flood risk assessment in Ghana, climate change and finance, projected change indicators and bibliometric analysis of climate change research in Ghana.

Econometrics has become an integral part of training in modern economics and business. Together with microeconomics and macroeconomics, econometrics has been taught as one of the three core courses in most undergraduate and graduate economic programs in the world. This chapter discusses the philosophy and methodology of econometrics in economic research, the roles and limitations of econometrics, and the differences between econometrics and mathematical economics as well as mathematical statistics. A variety of illustrative econometric examples are given, which cover various fields of economics and finance.

To control domestic inflation, the Federal Reserve raised interest rates several times in 2022, which caused the appreciation of the US dollar and the depreciation of the RMB in a short period. As two important financial indicators, the relationship between the exchange rate and stock price is of great theoretical and practical significance for China to formulate corresponding macroeconomic regulations and prevent the birth of financial risks. This paper utilizes the VAR model, impulse response function, ARMA-GARCH model, and other statistical methods to select two representative data sets of the RMB/USD exchange rate and the Huawei concept index as research objects. The purpose is to test whether there is a correlation between them and the degree of influence and to focus on the long-term stability of the price fluctuation relationship between the foreign exchange market and the Huawei index. The impulse response function is used to dynamically describe the short-term impact of foreign currency exchange rate and Huawei concept index fluctuation. According to the empirical results of this paper, some suggestions are provided for policymakers. This paper also establishes the ARMA-GARCH model for Huawei concept index and analyzes and predicts its volatility. The prediction results can provide some reference for investors to judge the short-term trend of Huawei concept index and make investment decisions.

Probability perhaps has become the best analytic tool to describe any system involving uncertainties, and statistics provides a mathematical foundation to model situations involving uncertainty. As the beginning of this book, this chapter will introduce two fundamental axioms behind modern econometrics, emphasizes the important role of statistics in economics and also discusses the limitation of statistical analysis in economics.

The decisions made by petroleum producers in the world oil market are both dynamic and strategic, and are thus best modeled as a dynamic game. In this chapter, we review the literature on the world oil market and discuss our research on econometric modeling of the world oil market as a dynamic game. Our research builds on the previous literature by combining three erstwhile separate dimensions of modeling the world oil market: dynamic optimization, game theory, and econometrics. Our results show that dynamic behavior and strategic interactions are important aspects of the world oil market that must be accounted for in empirical analyses of the world oil market.