Computational Economics最新文献

The consideration of the study on dynamic cluster flows in international tourists is an aspect that has been scarcely addressed in research despite its importance in economic development. Dynamic Time Warping is the methodology applied to identify alignments of common patterns in hotel demand time series within applied economics. The automatic determination of the number of clusters proposes an optimal number of groups for tourist destinations, and this proposition is confirmed through internal validation. Similarities among time series, including identifying outliers through boxplots, have been identified through the applied methodology. It has been employed for the primary tourist destinations in Spain for 106 international hotel demand time series. The effects of COVID-19 on the tourism sector and temporal similarities have been observed through clustering. The results that have been obtained reveal international tourist market flows that go beyond traditional analyses of seasonality or climatic factors, thus constituting a valuable tool for economic analysis in both direct and indirect markets.

In this paper, we raise a new method for numerically solving the partial differential equations (PDEs) of the Black-Scholes and Heston models, which play an important role in financial option pricing theory. Our proposed method is based on the rational spectral collocation method and the contour integral method. The presence of discontinuities in the first-order derivative of the initial condition of the PDEs prevents the spectral method from achieving high accuracy. However, the rational spectral method excels in overcoming this drawback. So we discretize the spatial variables of PDEs by rational spectral method, which yields a system of ordinary differential equations. Then we solve it by the numerical inverse Laplace transform using contour integral method. It is very important to select an appropriate parameters in the contour integral method, we revise the optimal parameters proposed by Trefethen and Weideman (Math Comput 76(259):1341–1356, 2007) in hyperbolic contour to control the effect of roundoff error. During solving the independent shifted linear systems, preconditioned Krylov subspace iteration is used to improve computational efficiency. We also compare the numerical results obtained from our proposed method with those obtained from the finite difference and spectral methods, showing its high accuracy and efficiency in pricing various financial options, including those mentioned above.

In recent years, the surge of unofficial digital currencies, often referred to as cryptocurrencies, has disrupted traditional financial landscapes. Bitcoin, being the most prominent among them in terms of market adoption and capitalization, presents unique modeling challenges. This study delves into the application of an autoregressive model of order one, incorporating a skew-normal mean-variance mixture of Birnbaum–Saunders innovations, to better capture the dynamic behavior of Bitcoin prices. The model’s robustness to atypical observations and its effectiveness in handling the inherent price volatility associated with Bitcoin make it a promising tool for financial analysis and prediction in this novel asset class.

Input–output tables provide important data for the analysis of economic states. Most regional input–output tables in Japan are not publicly available; therefore, they have to be estimated. Input coefficients are pivotal in constructing precise input–output tables; thus, accurately estimating these input coefficients is crucial. Non-survey methods have previously been used to estimate input coefficients of regions as they require fewer observations and computational resources. However, these methods discard information and require additional data. The aim of this study is to develop a method for estimating input coefficients using artificial neural networks with improved accuracy compared to conventional non-survey methods. To prevent overfitting owing to limited data availability, we introduced a data augmentation technique known as mixup. In this study, the vector sum of data from multiple regions was interpreted as the composition of the regions and the scalar product of regional data was interpreted as the scaling of the region. Based on these interpretations, the data were augmented by generating a virtual region from multiple regions using mixup. By comparing the estimates with the published values of the input coefficients for the whole of Japan, we found that our method was more accurate and stable than certain representative non-survey methods. The estimated input coefficients for three Japanese cities were considerably close to the published values for each city. This method is expected to enhance the precision of regional input–output table estimations and various quantitative regional analyses.