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We study an efficient numerical method for pricing European options when the dynamics of the underlying asset are described by Levy processes. In this case. we can write a characteristic function solution for a specific Levy option model and then take its inversion numerically. Specifically we use Variance Gamma process as an example of Levy option model and consider various characteristic function representation forms of European option price such as Carr and Madan (1999), Bakshi and Madan (2000). and Lewis (2001). Fast Fourier Transform method is applied to solve the numerical inversion problem with parameters for the KOSPI 200 options data. After analysing the problems in the FFT method, we propose alternative numerical inversion method, Gaussian Quadrature. This paper reports that Gaussian Quadrature numerical inversion method with the representation form of Bakshi & Madan (2000) is more efficient and accurate than other alternatives considered in this paper.

A summary of the economic impact resulting from the celebration of a sporting mega-event in the city of Cali, Colombia, is carried out in this article.

The impacts are estimated by means of the systematic effects produced by the activities of investment/expenditure of the identified agents and the consequent multiplying effects captured from an input-output model.

The World Games represented a new income flow to the city of Cali of $101.036 million pesos (≡US$53.4 mill.) and 9.598 jobs were created (≡7,711 full-time jobs). Additionally, 2,174 foreign tourists and 11,250 national tourists were attracted to the city.

The article contributes with innovative aspects methodologically speaking, useful for future studies of economic impact in cities with low tourism located in developing countries.

The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object detection in electrical tomography applications.

The authors suggest to apply the Box-Cox transformation in Bayesian linear minimum mean square error (BMMSE) reconstruction to better accommodate the non-linear relation between the capacitance matrix and the permittivity distribution. The authors compare the results of the original algorithm with the modified algorithm and with the ground truth in both, simulation and experiments.

The results show a reduction of 50 percent of the mean square error caused by artifacts in low permittivity regions. Furthermore, the algorithm does not increase the computational complexity significantly such that the hard real time constraints can still be met. The authors demonstrate that the algorithm also works with limited observations angles. This allows for object detection in real time, e.g., in robot collision avoidance.

This paper shows that the extension of BMMSE by applying the Box-Cox transformation leads to a significant improvement of the quality of the reconstruction image while hard real time constraints are still met.

The application of statistical inversion theory provides a powerful approach for solving estimation problems including the ability for uncertainty quantification (UQ) by means of Markov chain Monte Carlo (MCMC) methods and Monte Carlo integration. This paper aims to analyze the application of a state reduction technique within different MCMC techniques to improve the computational efficiency and the tuning process of these algorithms.

A reduced state representation is constructed from a general prior distribution. For sampling the Metropolis Hastings (MH) Algorithm and the Gibbs sampler are used. Efficient proposal generation techniques and techniques for conditional sampling are proposed and evaluated for an exemplary inverse problem.

For the MH-algorithm, high acceptance rates can be obtained with a simple proposal kernel. For the Gibbs sampler, an efficient technique for conditional sampling was found. The state reduction scheme stabilizes the ill-posed inverse problem, allowing a solution without a dedicated prior distribution. The state reduction is suitable to represent general material distributions.

The state reduction scheme and the MCMC techniques can be applied in different imaging problems. The stabilizing nature of the state reduction improves the solution of ill-posed problems. The tuning of the MCMC methods is simplified.

The paper presents a method to improve the solution process of inverse problems within the Bayesian framework. The stabilization of the inverse problem due to the state reduction improves the solution. The approach simplifies the tuning of MCMC methods.

This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.

Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems either as fast solvers for the direct problem or as straightforward inverse problem solvers, with reference to the TEAM 25 benchmark problem for the sake of exemplification.

Using DNNs as straightforward inverse problem solvers has relevant advantages in terms of promptness but requires a careful treatment of the underlying problem ill-posedness.

This work is one of the first attempts to exploit DNNs for inverse problem resolution in low-frequency electromagnetism. Results on the TEAM 25 test problem show the potential effectiveness of the approach but also highlight the need for a careful choice of the training data set.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Risk parity, also known as equal risk contribution, has recently gained increasing attention as a portfolio allocation method. However, solving portfolio weights must resort to numerical methods as the analytic solution is not available. This study improves two existing iterative methods: the cyclical coordinate descent (CCD) and Newton methods. The authors enhance the CCD method by simplifying the formulation using a correlation matrix and imposing an additional rescaling step. The authors also suggest an improved initial guess inspired by the CCD method for the Newton method. Numerical experiments show that the improved CCD method performs the best and is approximately three times faster than the original CCD method, saving more than 40% of the iterations.

La utilización de plataformas colaborativas, que intermedian en la cesión de uso de viviendas con fines turísticos, ha originado: por una parte, que se adopten medidas con el objetivo de prevenir el fraude fiscal (como por ejemplo, la obligación de información, a través del modelo 179) y, por otra parte, que la Administración tributaria preste especial atención al tratamiento fiscal de los rendimientos percibidos por los usuarios de las plataformas colaborativas.

Enfoque jurídico-tributario.

Las conclusiones se han puesto de manifiesto en el trabajo de investigación realizado.

Análisis de algunos aspectos fiscales sobre la cesión de uso de viviendas con fines turísticos.

This paper aims to analyze the antecedents of two variables concerning the presence of quality certifications in hotel chains: the (ex ante) decision to become a member of the quality system and the (ex post) trend to increase or decrease the number of certified properties. Six hypotheses are posed and tested.

The empirical investigation is carried out on the Spanish Q for Quality in Tourism using a database including 295 hotel chains and 2,727 hotels.

The results evidence the presence of differences in the behavior of hotel chains relative to certification depending on their size, market segment, customer origin and the geographical concentration of their establishments.

This research deepens in how the hotel chain characteristics affect the effectiveness of a quality certification. The consideration of two stages in investment decisions allows the authors to identify differences in the ex ante and ex post decision processes. As a result, one factor (geographical concentration) has been detected as being underrated by managers in the first stage.

Este artículo analiza los antecedentes de dos variables relacionadas con las certificaciones de calidad en cadenas hoteleras: La (ex-ante) decisión de formar parte de un sistema de calidad, y la (ex-post) tendencia a incrementar o reducir el número de establecimientos certificados. Seis hipótesis han sido propuestas y contrastadas.

La investigación empírica ha sido desarrollada en el marco de la marca Q de calidad para el turismo en España usando una base de datos que incluye 295 cadenas hoteleras y 2,727 hoteles.

Los resultados ponen de manifiesto la presencia de diferencias en el comportamiento de las cadenas hoteleras en materia de certificación dependiendo de su tamaño, segmento de mercado atendido, origen de la clientela y del grado de concentración geográfica de sus establecimientos.

El artículo profundiza en cómo las carfacterísticas de la cadena hotelera afectan a la eficacia de la certificación de calidad. Tener en consideración la existencia de dos etapas en las decisones de inversión nos permite identificar diferencias entre los procesos de decisión ex-ante y ex-post. Como resultado, hemos observado que un factor (la concentración geográfica) está siendo infravalorado por parte de os directivos en sus decisiones en la primera etapa.

Se estudian los determinantes y la evolución de la actividad bursátil mensual en el mercado accionario colombiano de 2007 a 2016.

Para ello se emplean modelos de series de tiempo tipo ARIMAX y GARCH, incluyendo variables exógenas, recomendadas por la literatura previa.

Encontramos que la actividad bursátil puede ser pronosticada en buena parte por el valor rezagado a un mes y las innovaciones de cinco y 12 meses. También contribuyen a predecirla, como variables exógenas, una dummy de rendimientos positivos en los últimos tres meses, la presencia de emisiones primarias y el índice VIX de volatilidad del SP500. Estos resultados se mantienen en un alto grado al emplear medidas alternativas de actividad bursátil, el número total de operaciones y la rotación.

Se propone un modelo de predicción de la actividad bursátil que puede servir de modelo para otros mercados accionarios de Latinoamérica. El modelo obtenido es altamente predictivo del valor transado total del mercado al siguiente mes. La estimación de la actividad bursátil es de utilidad para instituciones como la Bolsa de Valores de Colombia, reguladores de los mercados financieros, así como para grandes inversionistas institucionales.

El propósito central de los mercados financieros secundarios consiste en facilitar la transacción de activos financieros, lo que debe reflejarse en alta actividad bursátil, tanto en número de operaciones como en valor transado total. La posibilidad de transar altos montos es una medida importante del desarrollo de un mercado financiero. De esta manera, el modelo aquí propuesto puede usarse para monitorizar y explicar el desarrollo del mercado. En particular, se evidencia el nocivo efecto de la debacle de Interbolsa a finales de 2012 y el positivo efecto de las emisiones primarias.

Este es el primer paper en estudiar la actividad bursátil del mercado accionario colombiano en años recientes. Sirve como modelo para el estudio y seguimiento de esta variable en otros mercados accionarios latinoamericanos.

To study the determinants and evolution of the trading activity in the Colombian Stock Market from 2007 to 2016.

ARMA time series models were used, including several explanatory variables recommended by previous literature.

We find that stock market activity can be predicted to a large extent by its lags, and that positive returns in the last three months, emissions and the VIX index are also explicative variables, as suggested by empirical studies in other countries and theoretical models of market microstructure. These results are robust by using alternative measures of trading activity, total number of trades and turnover.

The main contribution of this study is the analysis of the trading activity of the Colombian Stock Market, a critical variable for monitoring the development of any financial market.