Computational Economics最新文献

Copper prices are commonly used as indicators of economic development due to the increased operational risks of copper trading companies caused by their fluctuations and the effect on the government's ability to formulate market regulation policies. However, due to the high volatility of copper prices and resulting database discrepancies, traditional models exhibit lower accuracy and limited applicability. In this study, an improved hybrid prediction model based on the Butterfly Optimization Algorithm (BOA) and the Least Squares Support Vector Machine (LSSVM) is proposed. Firstly, the BOA is introduced to optimize the hyperparameters of the LSSVM. Then principal component analysis (PCA) is applied to data preprocessing, and the correlations of principal components are used to analyze and select model variables. To compare the forecasting accuracy and generalization ability based on the dataset of copper prices, some models are applied to establish multiple copper-price forecast cases, short-term, medium-term, and long-term. The results indicate that the PCA-BOA-LSSVM model demonstrates the most significant improvement, particularly in long-term forecasting cases. The highest optimization rate for RMSE reach 55.61%. The evaluation metrics of RMSE and MAPE for each case do not exceed 0.5 and 0.1, respectively, while R² remains above 0.6. In conclusion, this study provides a high-precision model for short-term, medium-term, and long-term forecasts of copper prices and provides reliable theoretical support for government policy adjustment and market investment.

In this paper, two approaches for measuring the distance between stock returns and the network connectedness are presented that are based on the Pearson correlation coefficient dissimilarity and the generalized variance decomposition dissimilarity. Using these two procedures, the center of the network is determined. Also, hierarchical clustering methods are used to divide the dense networks into sparse trees, which provide us with information about how the companies of a financial market are related to each other. We implement the derived theoretical results to study the dynamic connectedness between the companies in the Swedish capital market by considering 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation among the companies in each window. Next, we obtain a one-dimensional time series of the distances between the clustering trees that reflect the changes in the relationship between the companies in the market over time. The method from statistical process control, namely the Shewhart control chart, is applied to those time series to detect abnormal changes in the financial market.

Understanding why extreme events occur is crucial in many fields, particularly in managing financial market risk. In order to explain such occurrences, it is necessary to use explanatory variables. However, flexible models with explanatory variables are severely lacking in financial market risk management, particularly when the variables are sampled at different frequencies. To address this gap, this article proposes a novel dynamic tail index regression model based on mixed-frequency data, which enables the high-frequency variable of interest to depend on both high- and low-frequency variables within the framework of extreme value regression. Specifically, it concurrently leverages information from low-frequency macroeconomic variables and high-frequency market variables to model the tail distribution of high-frequency returns, consequently enabling the computation of high-frequency Value at Risk and Expected Shortfall. Monte Carlo simulations and empirical studies show that the proposed method effectively models stock market tail risk and produces satisfactory forecasts. Moreover, including macroeconomic variables in the model provides insights for macroprudential regulation.

In today’s dynamic financial landscape, the integration of environmental, social, and governance (ESG) principles into investment strategies has gained great significance. Investors and financial advisors are increasingly confronted with the crucial question of whether their dedication to ESG values enhances or hampers their pursuit of financial performance. Addressing this crucial issue, our research delves into the impact of ESG ratings on financial performance, exploring a cutting-edge machine learning approach powered by the Extreme Gradient algorithm. Our study centers on US-registered equity funds with a global investment scope, and performs a cross-sectional data analysis for annualized fund returns for a five-year period (2017–2021). To fortify our analysis, we synergistically amalgamate data from three prominent mutual fund databases, thereby bolstering data completeness, accuracy, and consistency. Through thorough examination, our findings substantiate the positive correlation between ESG ratings and fund performance. In fact, our investigation identifies ESG score as one of the dominant variables, ranking among the top five with the highest predictive capacity for mutual fund performance. As sustainable investing continues to ascend as a central force within financial markets, our study underscores the pivotal role that ESG factors play in shaping investment outcomes. Our research provides socially responsible investors and financial advisors with valuable insights, empowering them to make informed decisions that align their financial objectives with their commitment to ESG values.

The Greymodels package presents an interactive interface in R for the statistical modelling and forecasting of incomplete or small datasets using grey models. The package, based on the Shiny framework, has been designed to work with univariate and multivariate datasets having different properties and characteristics. The functionality of the package is demonstrated with a few examples and in particular, the user-friendly interface is shown to allow users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within a user chosen confidence interval. The built-in algorithms in the Greymodels package are extensions or hybrids of the GM((1,,1)) model, and this article covers an overview of the theoretical background of the basic grey model and we also propose a PSO-GM((1,,1)) algorithm in this package.

A recent literature within quantitative macroeconomics has advocated the use of continuous-time methods for dynamic programming problems. In this paper we explore the relative merits of continuous-time and discrete-time methods within the context of stationary and nonstationary income fluctuation problems. For stationary problems in two dimensions, the continuous-time approach is both more stable and typically faster than the discrete-time approach for any given level of accuracy. In contrast, for concave lifecycle problems (in which age or time enters explicitly), simply iterating backwards from the terminal date in discrete time is superior to any continuous-time algorithm. However, we also show that the continuous-time framework can easily incorporate nonconvexities and multiple controls—complications that often require either problem-specific ingenuity or nonlinear root-finding in the discrete-time context. In general, neither approach unequivocally dominates the other, making the choice of one over the other an art, rather than an exact science.

In recent years, artificial intelligence has helped to improve processes and performance in many different areas: in the field of portfolio optimization, the inputs play a crucial role, and the use of machine learning algorithms can improve the estimation of the inputs to create robust portfolios able to generate returns consistently. This paper combines classical mean–variance optimization and machine learning techniques, concretely long short-term memory neural networks to provide more accurate predicted returns and generate profitable portfolios for 10 holding periods that present different financial contexts. The proposed algorithm is trained and tested with historical EURO STOXX 50® Index data from January 2015 to December 2020, and from January 2021 to June 2022, respectively. Empirical results show that our LSTM neural networks are able to achieve minor predictive errors since the average of the MSE of the 10 holding periods is 0.00047, the average of the MAE is 0.01634, and predict the direction of returns with an average accuracy over the 10 investment periods of 95.8%. Our prediction-based portfolios consistently beat the EURO STOXX 50® Index, achieving superior positive results even during bear markets.

This paper proposes a Bayesian varying coefficient model to estimate parameters exhibiting time-dependence in the Cobb–Douglas (CD) production function. We expand upon the classical CD production function by incorporating time-varying properties to enable more sophisticated modeling. We utilize a flexible and efficient Bayesian approach-based computational algorithm for statistical inference in the constrained parameter space, where the sum of model elasticities must be less than 1. The proposed model is applied to four real datasets from macroeconomics, as well as various social science issues broadly covered by the CD production function. The real data applications demonstrate the effectiveness of the proposed model in estimating underlying time-varying effects for parameters in the CD production function.