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 Computational EconomicsJournal Prestige (SJR): 0.433 Citation Impact (citeScore): 1Number of Followers: 11      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-9974 - ISSN (Online) 0927-7099 Published by Springer-Verlag  [2658 journals]
• An Application of the IFM Method for the Risk Assessment of Financial
Instruments

Abstract: External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the future to assess the risks and determine the potential loss of a security in the stock market, thus reducing reasoning influenced by feelings for bank and financial firms seeking to deploy AI and advanced automation. We used the IFM (inference function for margins) method in different market scenarios, with particular emphasis on the strengths and weaknesses of it. The study is assessed on single product level with the skewed studen-t GARCH(1,1) model and portfolio level with t-copulas for the inter-dependencies. It has been shown that under normal market conditions the risk is predicted properly for both levels. However, when an unexpected market event occurs, the prediction fails. To address this limitation, a combined model with sentiment analysis and regression is proposed for further investigation as a future work.
PubDate: 2021-10-13

• A Fitted L-Multi-Point Flux Approximation Method for Pricing Options

Abstract: In this paper, we introduce a special kind of finite volume method called Multi-Point Flux Approximation method (MPFA) to price European and American options in two dimensional domain. We focus on the L-MPFA method for space discretization of the diffusion term of Black–Scholes operator. The degeneracy of the Black-Scholes operator is tackled using the fitted finite volume method. This combination of fitted finite volume method and L-MPFA method coupled to upwind methods gives us a novel scheme, called the fitted L-MPFA method. Numerical experiments show the accuracy of the novel fitted L-MPFA method comparing to well known schemes for pricing options.
PubDate: 2021-10-13

• DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV
and t Errors

Abstract: Presently there is growing interest in dynamic stochastic general equilibrium (DSGE) models with more parameters, endogenous variables, exogenous shocks, and observable variables than the Smets and Wouters (Am Econ Rev 97(3):586–606, 2007) model, and the incorporation of non-Gaussian distribution and time-varying volatility. A primary goal of this paper is to introduce a user-friendly MATLAB toolkit designed to reliably estimate such high-dimensional models. It simulates the posterior distribution by the tailored random block Metropolis-Hastings (TaRB-MH) algorithm of Chib and Ramamurthy (J Econom 155(1):19–38, 2010), calculates the marginal likelihood by the method of Chib (J Am Stat Assoc 90:1313–1312, 1995) and Chib and Jeliazkov (J Am Stat Assoc 96(453):270–281, 2001), and includes various post-estimation tools that are important for policy analysis, for example, functions for generating point and density forecasts. We also introduce two novel features, i.e., tailoring-at-random-frequency and parallel computing, to boost the overall computational efficiency. Another goal is to provide pointers on the prior, estimation, and comparison of these DSGE models. To demonstrate the performance of our toolkit, we apply it to estimate an extended version of the new Keynesian model of Leeper et al (Am Econom Rev 107(8):2409–2454, 2017) that has 51 parameters, 21 endogenous variables, 8 exogenous shocks, 8 observable variables, and 1494 non-Gaussian and nonlinear latent variables.
PubDate: 2021-10-13

• Streaming Approach to Quadratic Covariation Estimation Using Financial
Ultra-High-Frequency Data

Abstract: We investigate the computational issues related to the memory size in the estimation of quadratic covariation, taking into account the specifics of financial ultra-high-frequency data. In multivariate price processes, we consider both contamination by the market microstructure noise and the non-synchronicity of the observations. We formulate a multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate this estimation as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without this constraint.
PubDate: 2021-10-10

• A Study of the International Stock Market Behavior During COVID-19
Pandemic Using a Driven Iterated Function System

Abstract: We propose a novel approach to visualize and compare financial markets across the globe using chaos game representation (CGR) of iterated function systems (IFS). We modified a fractal method, widely used in life sciences, and applied it to study the effect of COVID-19 on global financial markets. This modified driven IFS approach is used to generate compact fractal portraits of the financial markets in form of percentage CGR (PC) plots and subtraction percentage (SP) plots. The markets over different periods are compared and the difference is quantified through a parameter called the proximity (Pr) index. The reaction of the financial market across the globe and volatility to the current pandemic of COVID-19 is studied and modeled successfully. The imminent bearish and a surprise bullish pattern of the financial markets across the world is revealed by this fractal method and provides a new tool to study financial markets.
PubDate: 2021-10-05

• Maximum Likelihood Estimation for the Asymmetric Exponential Power
Distribution

Abstract: The asymmetric exponential power (AEP) distribution has received much attention in economics and finance. Simulation study shows that iterative methods developed for finding the maximum likelihood (ML) estimates of the AEP distribution sometimes fail to converge. In this paper, the expectation–maximization (EM) algorithm is proposed to find the ML estimates of the AEP distribution which always converges. Performance of the EM algorithm is demonstrated by simulations and a real data illustration. As an application, the proposed EM algorithm is applied to find the ML estimates for the regression coefficients when the error term in a linear regression model follows the AEP distribution. Performance of the AEP distribution in robust simple regression modelling is established through a real data illustration.
PubDate: 2021-10-05

• Enterprise Intelligent Audit Model by Using Deep Learning Approach

Abstract: The purpose is to apply artificial intelligence to enterprise intelligent audit, improve the efficiency of enterprise audit, and finally supervise and manage the enterprise revenue and expenditure timely and accurately. First, the classification, processing and storage of enterprise intelligent audit data are analyzed. An improved DLNN (Dynamic Learning Neural Network) algorithm model is proposed according to the deep learning theory to carry out intelligent audit on enterprise data, and BiLSTM (Bi-directional Long Short-Term Memory) model is adopted to analyze the classification accuracy of audit. Then, the Auto Encoder based on data compression algorithm is adopted to analyze the audit data. Finally, genetic algorithm is applied to the weight optimization of a deep learning network. The economic benefit evaluation data information of X enterprise in 2018 is selected. The current values of relevant indexes are compared with historical data. The results suggest that the performance of the deep learning neural network model optimized by the genetic algorithm is improved. After optimization, the precision of the model is improved from 81 to 87%, the accuracy of the model is improved from 92 to 96%, and the F1 score of the model is reduced from 3.5 to 2.3%. From 2014 to 2017, the current ratio, quick ratio, cash ratio and asset liability ratio of enterprise X decreased in 2015, while they were basically flat from 2015 to 2017. The return on assets and return on net assets corresponding to a competitive value and current value are basically the same, while the operating profit rate and cost profit rate corresponding to competitive value are slightly higher than the current value. Thereby, the data layer conversion model of enterprise intelligent audit based on deep learning has fast data conversion speed, and the audit results obtained are in line with the benchmark evaluation index with high reliability.
PubDate: 2021-10-04

• Hedging the Risks of MENA Stock Markets with Gold: Evidence from the
Spectral Approach

Abstract: In this paper, we contribute to the old debate on the dynamic correlation between gold and stock markets by considering a spectral approach within the framework of portfolio hedging. Specifically, we consider eight MENA stock markets (Tunisia, Egypt, Morocco, Jordan, UAE, Saudi Arabia, Qatar, and Oman) and examine the optimal composition between gold and the stock market index, with a minimum portfolio risk and a high expected return. Based on the spectral approach, we propose seven portfolio structures and evaluate them through a comparison with the conventional DCC-GARCH method and the most best 10 portfolios constructed by using wavelet approach. The main results show that the spectral-based approach outperforms the DCC-GARCH and the wavelet methods. In fact, the optimal gold-stock composition depends on the spectral density of each stock market index, where a stock market index with a stable spectral density requires more investments in gold than a stock market index with an unstable spectral density.
PubDate: 2021-10-02

• Computational Aspects of Sustainability

PubDate: 2021-10-01

• Evaluation of Urban Competitiveness of the Huaihe River Eco-Economic Belt
Based on Dynamic Factor Analysis

Abstract: Construction of the Huaihe River ecological-economic belt—an important component of the “One Belt, One Road” initiative—is essential for the development of central China. Urban competitiveness can reflect the level of urban development and comprehensive strength that, in turn, determine the trend of urban development. To evaluate urban competitiveness in the Huaihe River eco-economic belt, a comprehensive model is established and the dynamic factor analysis method is used for urban panel data. The results show that the economic development of a city has the greatest impact on its competitiveness while the impact of quality of life is small. In general, the spatial distribution of static scores of urban competitiveness in the Huaihe River eco-economic belt is unbalanced and the variation trend of dynamic scores mainly manifests as M or W shapes with regularity in time and space. The spatial distribution of the comprehensive scores of urban competitiveness varies dramatically, ranging from strong in eastern coastal areas to weak in central and western regions. In the construction of the Huaihe River eco-economic belt, urban development should rely on the comparative advantages of central cities to drive the common development of surrounding cities, helping in the overall development of the eco-economic belt and promoting the coordinated development of eastern and western regions.
PubDate: 2021-10-01

• A Guide on Solving Non-convex Consumption-Saving Models

Abstract: Consumption-saving models with adjustment costs or discrete choices are typically hard to solve numerically due to the presence of non-convexities. This paper provides a number of tools to speed up the solution of such models. Firstly, I use that many consumption models have a nesting structure implying that the continuation value can be efficiently pre-computed and the consumption choice solved separately before the remaining choices. Secondly, I use that an endogenous grid method extended with an upper envelope step can be used to solve efficiently for the consumption choice. Thirdly, I use that the required pre-computations can be optimized by a novel loop reordering when interpolating the next-period value function. As an illustrative example, I solve a model with non-durable consumption and durable consumption subject to adjustment costs. Combining the provided tools, the model is solved almost 50 times faster than with standard value function iteration for a given level of accuracy. Software is provided in both Python and C++.
PubDate: 2021-10-01

• Performance Management of Supply Chain Sustainability in Small and
Medium-Sized Enterprises Using a Combined Structural Equation Modelling
and Data Envelopment Analysis

Abstract: Although the contribution of small and medium-sized enterprises (SMEs) to economic growth is beyond doubt, they collectively affect the environment and society negatively. As SMEs have to perform in a very competitive environment, they often find it difficult to achieve their environmental and social targets. Therefore, making SMEs sustainable is one of the most daunting tasks for both policy makers and SME owners/managers alike. Prior research argues that through measuring SMEs’ supply chain sustainability performance and deriving means of improvement one can make SMEs’ business more viable, not only from an economic perspective, but also from the environmental and social point of view. Prior studies apply data envelopment analysis (DEA) for measuring the performance of groups of SMEs using multiple criteria (inputs and outputs) by segregating efficient and inefficient SMEs and suggesting improvement measures for each inefficient SME through benchmarking it against the most successful one. However, DEA is limited to recommending means of improvement solely for inefficient SMEs. To bridge this gap, the use of structural equation modelling (SEM) enables developing relationships between the criteria and sub-criteria for sustainability performance measurement that facilitates to identify improvement measures for every SME within a region through a statistical modelling approach. As SEM suggests improvements not from the perspective of individual SMEs but for the totality of SMEs involved, this tool is more suitable for policy makers than for individual company owners/managers. However, a performance measurement heuristic that combines DEA and SEM could make use of the best of each technique, and thereby could be the most appropriate tool for both policy makers and individual SME owners/managers. Additionally, SEM results can be utilized by DEA as inputs and outputs for more effective and robust results since the latter are based on more objective measurements. Although DEA and SEM have been applied separately to study the sustainability of organisations, according to the authors’ knowledge, there is no published research that has combined both the methods for sustainable supply chain performance measurement. The framework proposed in the present study has been applied in two different geographical locations—Normandy in France and Midlands in the UK—to demonstrate the effectiveness of sustainable supply chain performance measurement using the combined DEA and SEM approach. Additionally, the state of the companies’ sustainability in both regions is revealed with a number of comparative analyses.
PubDate: 2021-10-01

• The Valuation of Weather Derivatives Using One Sided Crank–Nicolson
Schemes

Abstract: This paper prices weather derivatives of two typical processes: the Ornstein–Uhlenbeck process and the Ornstein–Uhlenbeck process with jump diffusions. Efficient one sided Crank–Nicolson schemes are developed to solve the convection dominated partial differential and integral-differential equation corresponding to the two processes, respectively. For second order convergence, the one sided Crank–Nicolson schemes may utilize piecewise cubic interpolations to approximate the jump conditions in degree days direction. The unconditional stability is then obtained through the local von Neumann analysis. As extensive numerical experiments shown, the schemes are highly efficient and accurate, and can serve as competitive and practical pricing instruments in weather derivative markets.
PubDate: 2021-10-01

• MOLES: A New Approach to Modeling the Environmental and Economic Impacts
of Urban Policies

Abstract: This paper presents the Multi-Objective Local Environmental Simulator (MOLES), an urban Computable General Equilibrium model with selected microsimulation features that links urban land use, mobility patterns and their environmental impacts. The model is tailored to uncover the trade-offs between environmental and economic performance in urban areas. It is also designed to capture the synergetic effects of urban planning and transportation policies. We demonstrate the model’s structure, functions and algorithms through an application to Auckland, New Zealand. The application explores the environmental, fiscal and welfare impacts of a reform that promotes a massive switch to public transportation. We show that, in order to achieve that objective, such a reform should drastically increase the kilometer cost of car use and provide considerable subsidies to public transportation. We find that, in spite of these subsidies, the reform will have a fiscal surplus and will generate substantial welfare gains.
PubDate: 2021-10-01

• Statistical Validation of Multi-Agent Financial Models Using the
H-Infinity Kalman Filter

Abstract: The article develops a method that is based on the H-infinity Kalman Filter for statistical validation of models of multi-agent financial systems in the form of an oligopoly. The real outputs of the oligopoly are compared against the outputs of an H-infinity Kalman Filter estimator that incorporates the oligopoly’s dynamic model. The difference between the two outputs forms the residuals’ sequence. The residuals undergo statistical processing. Actually, the sum of the products between the residual vectors’ square and the inverse of their covariance matrix defines a stochastic variable which follows the $$\chi ^2$$ distribution and which provides a statistical test about the existence or absence of parametric changes in the oligopolistic market. Next, by exploiting the properties of the $$\chi ^2$$ distribution one can define confidence intervals to validate the model used by the H-infinity Kalman Filter, in comparison to the real dynamics of the oligopoly. By validating the models that describe the dynamics of multi-agent financial systems one can perform reliable forecasting, and more efficient decision making or risk management.
PubDate: 2021-10-01

• Pricing Exotic Option Under Jump-Diffusion Models by the Quadrature Method

Abstract: This paper extends the quadrature method to price exotic options under jump-diffusion models. We compute the transition density of jump-extended models using convolution integrals. Furthermore, a simpler and more efficient lattice grid is introduced to implement the recursion more directly in matrix form. It can be shown that a lot of running time can be saved. At last, we apply the developed approach to the different jump-extended models to demonstrate its universality and provide a detailed comparison for the discrete path-dependent options to demonstrate its advantages in terms of speed and accuracy.
PubDate: 2021-10-01

• Making Predictions of Global Warming Impacts Using a Semantic Web Tool
that Simulates Fuzzy Cognitive Maps

Abstract: One of the most important environmental problems of our era is Global Warming (GW), which derives its roots mainly from anthropogenic activities and is expected to cause far-reaching and long-lasting impacts to the natural environment, ecosystems and human societies. The purpose of this paper is twofold: (a) to develop a model of the causal relationships that exist in the field of GW, using the well-established Artificial Intelligence technique of Fuzzy Cognitive Maps (FCMs) and (b) to develop a Semantic Web simulation software tool, that visually simulates the FCM dynamic behavior and studies the equilibrium that the FCM dynamic system reaches. Using this generic tool, various scenarios can be imposed to the FCM model and predictions can be made on these, in a “what-if” manner. The features of the web simulation tool are exhibited using the FCM that was created and concerns “Global Warming”. By applying Semantic Web technologies, the tool makes the results and the various FCM models, that can be implemented in it, easily accessible to various users or systems, through the Internet. In this way, policy makers can use this technique and tool to make predictions by viewing dynamically the consequences that the system predicts to their imposed scenarios and share them through the world wide web.
PubDate: 2021-10-01

Models

Abstract: The ability to correctly interpret a prediction model’s output is critically important in many problem spheres. Accurate interpretation generates user trust in the model, provides insight into how a model may be improved, and supports understanding of the process being modeled. Absence of this capability has constrained algorithmic trading from making use of more powerful predictive models, such as XGBoost and Random Forests. Recently, the adaptation of coalitional game theory has led to the development of consistent methods of determining feature importance for these models (SHAP).This study designs and tests a novel method of integrating the capabilities of SHAP into predictive models for algorithmic trading.
PubDate: 2021-10-01

• Pollution and Health Effects: A Nonparametric Approach

Abstract: Pollution is associated with serious environmental and health problems. For instance particulate matter (PM2.5) causes severe health problems like respiratory and cardiovascular diseases and outdoor exposure may be carcinogenic to humans. In this study data envelopment analysis is used to estimate the efficiencies of 18 European countries for the years 2000, 2005, 2010, 2014, 2015 and 2016. Directional distance function is utilized to deal with undesirable outputs. Two models are specified one with labour and capital as inputs and GDP/c and mortality from exposure to PM2.5 as desirable and undesirable outputs respectively and the other with environmental related tax revenues as additional input. The results derived are bias corrected to obtain the accurate efficiency scores of every country considered. On the whole the most efficient countries are revealed to be Sweden, Finland, France, the Netherlands and the UK. The inclusion of environmentally related tax revenues seems to have a little influence in efficiency scores.
PubDate: 2021-10-01

• Social Influence of Competing Groups and Leaders in Opinion Dynamics

Abstract: This paper explores the influence of two competing stubborn agent groups on the opinion dynamics of normal agents. Computer simulations are used to investigate the parameter space systematically in order to determine the impact of group size and extremeness on the dynamics and identify optimal strategies for maximizing numbers of followers and social influence. Results show that (a) there are many cases where a group that is neither too large nor too small and neither too extreme nor too central achieves the best outcome, (b) stubborn groups can have a moderating, rather than polarizing, effect on the society in a range of circumstances, and (c) small changes in parameters can lead to transitions from a state where one stubborn group attracts all the normal agents to a state where the other group does so. We also explore how these findings can be interpreted in terms of opinion leaders, truth, and campaigns.
PubDate: 2021-10-01

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