Computational Economics最新文献

In today’s economic landscape, with its increasingly brief economic cycles and ever-changing market conditions, forecasting has become more critical than ever. In the specific case of small and medium-sized enterprises (SMEs), a crucial aspect is to anticipate the state of bankruptcy due to the low life expectancy of this type of company. A requirement that has been recommended by several international organizations such as the European Union, especially because SMEs contribute significantly to job creation and added value and to overcoming the effects of economic crises. Despite the progress in this field, there are economies that have been little or poorly addressed by the literature. This is the case for Spain, an economy where SMEs account for a significant share of its business landscape. To close this gap, this paper addressed the problem of predicting the insolvency of Spanish SMEs from a Machine Learning perspective. Leveraging a dataset encompassing financial and non-financial data from 58,267 Spanish SMEs spanning the period 2009–2020, we adjusted several decision tree models to address two scenarios of practical value in the Spanish context. Additionally, we conducted a thorough analysis of the most influential predictors of insolvency from a financial perspective. To empower Spanish SMEs, we provided them with a free software tool implementing the best models for the considered scenarios. The tool is intended to serve as an additional means to proactively and early assess solvency status.

Abstract

The aim of nearly all empirical studies in economics is to provide scientific evidence that can be used to assess policy relevant cause-and-effect. In the context of the general potential outcomes framework, we review how a causal effect parameter can be rigorously but tractably specified, identified and estimated along with its asymptotic standard error. For cases in which the analytic and computational requirements for calculation of the ASE are challenging, we suggest the use of numerical derivatives (ND). We detail the specific type of ND software required for this purpose, and note that it is offered as a feature in most statistical packages. As an illustration, we analyze the causal effect of wife's high school graduation on family size using the Stata/Mata deriv command. Code for this example is supplied in an appendix.

Requirements to understand and forecast the behavior of complex macroeconomic interactions mandate the use of high-dimensional macroeconometric models. The Global Vector Autoregressive (GVAR) modeling technique is very popular among them and it allows researchers and policymakers to take into account both the complex interdependencies that exist between various economic entities and the global economy through the world’s trade and financial channels. However, determining the cross-section unit size while using this approach is not a trivial task. In order to address this issue, we suggest an objective procedure for the detection of the size of the cross-country aggregation in GVAR models. While doing so, we depart from the Akaike Information Criterion (AIC) and propose an analytical modification to it, mainly employing an ad hoc approach without violating Akaike’s main principles. To supplement the theoretical results, small sample performances of those procedures are studied in Monte Carlo experiments as well as implementing our approach on real data. The numerical results suggest that our ad hoc modification of AIC can be used to determine the structure of the cross-section unit dimension in GVAR models, allowing the researchers and policymakers to build parsimonious models.

The first observation of the paper is that methods for determining proportional representation in electoral systems may be suitable as alternatives to the pro-rata order matching algorithm used in stock exchanges. The main part of our work is to comprehensively consider various well known proportional representation methods and analyse in details their suitability for replacing the pro-rata algorithm. Our analysis consists of a theoretical study as well as simulation studies based on data sampled from a distribution which has been suggested in the literature as models of limit orders. Based on our analysis, we put forward the suggestion that the well known Hamilton’s method is a superior alternative to the pro-rata algorithm for order matching applications.

Integration of asset preselection with appropriate portfolio optimization techniques can improve the performance of the portfolio optimization models. This paper morphed the potential asset selection and the optimal portfolio construction rather than focusing on one. A large volume of sample data from 25 stocks is used for the experiment from the National Stock Exchange, India, between January 2005 and December 2021. Initially, a 3-step screening approach, an asset selection method is applied to select potential assets. The 3-steps comprise data choice, fundamental screening, and the Long Short Term Memory model anticipating real-time stock prices to shortlist stocks with higher expected returns. The suggested approach is effective in determining the quality of assets. Further, the optimal asset allocation is done by introducing a novel exponentially weighted-mean-variance model. This exponential weighting scheme outperforms the classical Mean-Variance model when applied to the maximum Sharpe ratio model. The proposed model outperforms the five baseline techniques in terms of the Sharpe ratio and average potential returns and risks. Additionally, the proposed model’s resilience across diversified time frames is tested through the incorporation of multiple time windows, demonstrating robustness of the performance.

This paper provides a general representation of endogenous and threshold-based regime-switching models while also developing an efficient numerical solution method. Regime-switching occurs endogenously when certain variables cross threshold conditions that can themselves be regime-dependent. We illustrate our approach using a RBC model with state-dependent government spending policies. It is shown that regime-switching models involve strong non linearities and discontinuities in the dynamics of the model. However, our numerical solution which relies on simulation and projection methods with regime-dependent policy rules, proves to be accurate and sufficiently fast address these challenging aspects. Several also explore several alternative specifications for the model and the method.

Scoring rules are commonly applied to assess the accuracy of density forecasts in both univariate and multivariate settings. In a financial risk management context, we are mostly interested in a particular region of the density: the (left) tail of a portfolio’s return distribution. The dependence structure between returns on different assets (associated with a given portfolio) is usually time-varying and asymmetric. In this paper, we conduct a simulation study to compare the discrimination ability between the well-established scores and their threshold-weighted versions with selected regions. This facilitates a comprehensive comparison of the performance of scoring rules in different settings. Our empirical applications also confirm the importance of weighted-threshold scores for accurate estimates of Value-at-risk and related measures of downside risk.

The main aim of the present research is to consider a monetary union’s economy consisting of N countries, N fiscal authorities (one for each country) and a single monetary authority. The fiscal authorities want to stabilise output and public debt through the primary government balance, and they can exhibit heterogeneous preferences about the trade-off between output and debt stability. Unlike these, the monetary authority has the aim of price and output stability. They play a non-cooperative policy game, in which they independently and simultaneously choose monetary and fiscal instruments to pursue their goals. In a dynamic setting, each authority must choose its policy instrument prevailing in the next period without knowing—at the end of each period—the choice of other authorities. By assuming static expectations, the present work shows the possibility of several dynamic outcomes. First, there exists one Nash equilibrium representing the optimal level for the macro economy; this equilibrium is stable if the average weight that fiscal authorities assign to output stability is not excessively high; therefore, this result holds even if some authorities are less willing to promote debt stabilisation. Second, in addition to this equilibrium, there exist other Nash equilibria representing steady-state values for macroeconomic variables that differ from the targets adopted by the authorities; these equilibria emerge and are stable if the authorities’ preference for output stability is even greater and with a higher degree of heterogeneity compared to the previous case. Third, the parameters of the model matter to determine the stability properties of the equilibria, and the analysis shows the possibility of nonlinear dynamics.

Abstract

Composite distributions where volatility itself is assumed to be a random variable have been used to model stock returns. In this paper, we give details of estimation of these composite distributions when the volatility is assumed to follow an arbitrary distribution and the conditional distribution of stock returns given the volatility follows one of normal, Laplace, uniform, Student’s t, Cauchy, logistic of type I, logistic of type II, logistic of type III, logistic of type IV, generalized normal or skew normal distributions. The details given include estimating equations and observed information matrices. An application to Bitcoin exchange rate data is illustrated. Models taking volatility to follow gamma and Weibull distributions are shown to provide excellent fits.

Valuation of large portfolios of variable annuities (VAs) is a well-researched area in the actuarial science field. However, the study of producing reliable prediction intervals for prices has received comparatively less attention. Compared to point prediction, the prediction interval can calculate a reasonable price range of VAs and help investors and insurance companies better manage risk to maintain profitability and sustainability. In this study, we address this gap by utilizing five different models in conjunction with bootstrapping techniques to generate robust prediction intervals for variable annuity prices. Our findings show that the Gradient Boosting regression (GBR) model provides the narrowest intervals compared to the other four models. While the Random sample consensus (RANSAC) model has the highest coverage rate, but it has the widest interval. In practical applications, considering the trade-off between coverage rate and interval width, the GBR model would be a preferred choice. Therefore, we recommend using the gradient boosting model with the bootstrap method to calculate the prediction interval of valuation for a large portfolio of variable annuity policies.