Computational Economics最新文献

A carbon trading price fusion prediction model is proposed to capture the non-linear, non-stationary, multi-frequency, and other irregular characteristics of carbon price data, as well as the temporal periodicity of environmental factors. Firstly, an adaptive Symmetric geometric mode decomposition method is introduced to address the irregularities in carbon trading prices, including nonlinearity, non-stationarity, and multi-frequency. Bubble entropy is employed to extract global features in the frequency and time domains of carbon price data. Secondly, to handle the nonlinearity, temporal periodicity, and noise in environmental influencing factors, a mapping function between the frequency components of carbon price data and environmental influencing factors is established using LightGBM (Light gradient boosting machine) with a regularization term, enabling enhanced fusion of carbon price data features. Thirdly, a Bald Eagle Search-optimized Bi-directional long short-term memory (BiLSTM) model is proposed for predicting carbon prices with different cycle and frequency components. Finally, experimental results demonstrate the superior performance of the proposed fusion prediction model over other models.

This study examines the time-varying connectedness between green bonds, Twitter-based uncertainty indices, and the S&P 500 Composite Index. We implement the time- and frequency-based connectedness methodologies and employ data between April 1, 2014 and April 21, 2023. Our findings suggest that (i) connectedness indices robustly capture prominent incidents during the episode; (ii) Twitter-based uncertainty indices are the highest transmitters of return shocks; (iii) net return spillovers transmitted by the S&P 500 Index sharply increased in 2020:1–2020:3, stemmed by the stock market crash in February 2020; and (iv) Twitter-based uncertainty indices showed significant net spillovers in July and November 2021.

This research delves into the dynamics that underlie the relationship between changes in a company's sales and its cost structure. It also explores the influence of short-term debt, often associated with sales, on a phenomenon known as cost stickiness. Furthermore, we examine the roles of product market competition and various business strategies in shaping these interactions. We used financial data from 153 companies listed on the Tehran Stock Exchange from 2015 to 2021 to conduct a rigorous regression analysis to unearth significant insights. Our findings reveal that short-term debt serves as a mechanism for companies to effectively manage their financial obligations with lenders and creditors, and it is inversely correlated with cost stickiness. Moreover, our results shed light on how the impact of short-term debt on cost stickiness varies in response to the competitive nature of the product market and a company's chosen business strategy. Specifically, when companies adopt a "prospector strategy" to explore new markets and expand their product offerings, the negative association between short-term debt and cost stickiness weakens. In a broader context, our study contributes to comprehending cost stickiness and carries practical implications for industry professionals and future scholarly pursuits.

Using 50 ETF options data from the Shanghai Stock Exchange as samples, this paper explores the predictive power of option implied volatility spread (IVS) on stock market returns, mainly from a network perspective. In this paper, we first construct a multi-scale data series by wavelet decomposition of the data, and then build a corresponding dynamic complex network on this basis. We analyze the topological features of the network to reveal the dynamic relationship between variables. At the same time, the topological features are used as input variables for machine learning to quantitatively explore the return information contained in the IVS. The conclusions show not only that IVS has the strongest correlation with stock market returns in the medium and long-term, but that the accuracy of IVS prediction is also highest at this time. Furthermore, the GBDT machine learning model is more effective in predicting future stock market returns when using IVS as an indicator.

The study focuses on constructing a mathematical housing market threatened by a major catastrophic event or crash. It incorporates the worst-case scenario portfolio optimization problem as introduced in Korn and Wilmott (Int J Theor Appl Finance 5(02):171–187, 2002) into housing markets. The standard stochastic models for housing markets assume a geometric Brownian motion and neglect sudden housing price falls during crash times. However, the size, timing, and frequency of crashes have to be included in such models. By incorporating the worst-case portfolio optimization problem into housing markets, this study introduces a methodology to construct portfolios for large investors that are robust and resilient to extreme housing market conditions. The worst-case portfolio optimization approach can be used in housing markets to incorporate stress scenarios, minimize potential losses, utilize mathematical techniques, and manage housing investment risk effectively. This study provides valuable insights for large investors seeking to construct housing portfolios prioritizing downside protection and minimizing losses in extreme housing market conditions. Utilizing numerical illustrations, it provides insights into portfolio construction, demonstrating the effectiveness of adjusting portfolios to mitigate downside risks during housing market crises. The results highlight dynamic portfolio management’s significance in safeguarding wealth when housing prices undergo significant fluctuations.

A picture fuzzy regression function approach is a fuzzy inference system method that uses as input the lagged variables of a time series and the positive, negative and neutral membership values obtained by picture fuzzy clustering method. In a picture fuzzy regression functions method, the parameter estimation is also obtained by ordinary least squares method. Since the picture fuzzy regression functions approach is based on the ordinary least squares method, the forecasting performance decreases when there are outliers in the time series. In this study, a picture fuzzy regression function approach that can be used even in the presence of outliers in a time series is proposed. In the proposed method, the parameter estimation for the picture fuzzy regression function approach is performed based on robust regression with Bisquare, Cauchy, Fair, Huber, Logistic, Talwar and Welsch functions. The forecasting performance of the proposed method is evaluated on the time series of the Spanish and the London stock exchange time series. The forecasting performance of these time series are evaluated separately for both the original and outlier cases. Besides, the proposed method is compared with several different fuzzy regression function approaches and a neural network method. Based on the results of the analysis, it is concluded that the proposed method outperforms the other methods even when the time series contains both original and outliers.

This study investigates the dynamics of nuclear trade among countries from 2006 to 2021 using ERGM and TERGM analyses. The results reveal three key conclusions. First, as countries become more politically similar, their engagement in nuclear trade becomes more active, emphasizing the significance of political similarity in promoting nuclear trade relationships. Second, countries with greater political differences tend to impede the formation of nuclear trade, highlighting political disparities as a potential barrier to cooperation. Finally, the study finds that countries involved in the global nuclear trade network maintain reciprocal relationships, indicating the presence of mutual benefits and interdependence. These findings contribute to understanding the factors influencing nuclear trade and suggest the importance of fostering politically similar partnerships for successful collaboration in the nuclear industry.

This paper focuses on solving the problem of technical efficiency estimation for panel data when residuals are right-skewed. Indeed, there is an ambiguity in stochastic frontier analysis when the residuals of the ordinary least squares estimates are right-skewed, which might indicate that either there is no inefficiency, or that the model is misspecified. To overcome and avoid this problem, we propose a panel model in which the inefficiency term has an extended-half-normal distribution. Hence, our work is an extension of existing work for the cross-section case to panel data with time varying inefficiencies. We first propose estimators of the inefficiency under the extended-half-normal distribution assuming independence between the noise and the inefficiency term. A simulation study illustrates the good performance of our procedure. An application to drinking water for forty-two Moroccan municipalities in the period 2017 to 2019 favors our extended model. Results reveal that the performance of this public sector is generally medium and therefore the waste was significant.

The unprecedented policy responses during the Global Financial Crisis and European debt crisis may have increased uncertainty about inflation and strengthen the transmission of inflation uncertainty shocks from one country to another. This paper examines empirical methodologies to measure the strength of the interdependence of inflation uncertainty between the UK and the euro area. First, I estimate inflation uncertainty by ex post forecast errors from a bivariate VAR GARCH model and find that the inflation uncertainty exhibits non-Gaussian properties. In such cases, correlations and copulas to measure the interdependence could suffer from bias if endogeneity is not properly addressed. To identify structural parameters in an endogeneity representation of interdependence, I exploit heteroskedasticity in the data across different regimes determined by the ratio of variances. The estimation results corroborate that the strength of the propagation of inflation uncertainty amplifies during the crisis while the interdependence significantly weakens in the post-crisis period.

The paper investigates the issue of volatility of stock index returns on the Warsaw Stock Exchange (WIG20 index returns volatility). The purpose of this review is to compare how other stock market indexes as HANG SENG, NIKKEI 225, FTSE 250, DAX, S&P 500 and NASDAQ 100 influance the volatility of WIG20 index returns. The innovation of this work is the usage of a new neural network with three different activation functions to predict future volatility of WIG20 index returns. The input for this network is the last 3 values of WIG20 index returns volatility and the last 3 values of one of the considered foreign index returns volatility. As measurements for the best forecasting performance of neural networks are taken common used forecast errors: ME (mean error), MPE (mean percentage error), MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root mean square error). The study shows that the Polish stock market is mainly influenced by the European and US markets.